Dissertations / Theses: 'Fuzzy processes'

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Bell, Michael Ray. "Fuzzy logic control of uncertain industrial processes." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18998.

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Kandiah, Sivasothy. "Fuzzy model based predictive control of chemical processes." Thesis, University of Sheffield, 1996. http://etheses.whiterose.ac.uk/3029/.

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The past few years have witnessed a rapid growth in the use of fuzzy logic controllers for the control of processes which are complex and ill-defined. These control systems, inspired by the approximate reasoning capabilities of humans under conditions of uncertainty and imprecision, consist of linguistic 'if-then' rules which depend on fuzzy set theory for representation and evaluation using computers. Even though the fuzzy rules can be built from purely heuristic knowledge such as a human operator's control strategy, a number of difficulties face the designer of such systems. For any reasonably complex chemical process, the number of rules required to ensure adequate control in all operating regions may be extremely large. Eliciting all of these rules and ensuring their consistency and completeness can be a daunting task. An alternative to modelling the operator's response is to model the process and then to incorporate the process model into some sort of model-based control scheme. The concept of Model Based Predictive Control (MB PC) has been heralded as one of the most significant control developments in recent years. It is now widely used in the chemical and petrochemical industry and it continues to attract a considerable amount of research. Its popularity can be attributed to its many remarkable features and its open methodology. The wide range of choice of model structures, prediction horizon and optimisation criteria allows the control designer to easily tailor MBPC to his application. Features sought from such controllers include better performance, ease of tuning, greater robustness, ability to handle process constraints, dead time compensation and the ability to control nonminimum phase and open loop unstable processes. The concept of MBPC is not restricted to single-input single-output (SISO) processes. Feedforward action can be introduced easily for compensation of measurable disturbances and the use of state-space model formulation allows the approach to be generalised easily to multi-input multi-output (MIMO) systems. Although many different MBPC schemes have emerged, linear process models derived from input-output data are often used either explicitly to predict future process behaviour and/or implicitly to calculate the control action even though many chemical processes exhibit nonlinear process behaviour. It is well-recognised that the inherent nonlinearity of many chemical processes presents a challenging control problem, especially where quality and/or economic performance are important demands. In this thesis, MBPC is incorporated into a nonlinear fuzzy modelling framework. Even though a control algorithm based on a 1-step ahead predictive control strategy has initially been examined, subsequent studies focus on determining the optimal controller output using a long-range predictive control strategy. The fuzzy modelling method proposed by Takagi and Sugeno has been used throughout the thesis. This modelling method uses fuzzy inference to combine the outputs of a number of auto-regressive linear sub-models to construct an overall nonlinear process model. The method provides a more compact model (hence requiring less computations) than fuzzy modelling methods using relational arrays. It also provides an improvement in modelling accuracy and effectively overcomes the problems arising from incomplete models that characterise relational fuzzy models. Difficulties in using traditional cost function and optimisation techniques with fuzzy models have led other researchers to use numerical search techniques for determining the controller output. The emphasis in this thesis has been on computationally efficient analytically derived control algorithms. The performance of the proposed control system is examined using simulations of the liquid level in a tank, a continuous stirred tank reactor (CSTR) system, a binary distillation column and a forced circulation evaporator system. The results demonstrate the ability of the proposed system to outperform more traditional control systems. The results also show that inspite of the greatly reduced computational requirement of our proposed controller, it is possible to equal or better the performance of some of the other fuzzy model based control systems that have been proposed in the literature. It is also shown in this thesis that the proposed control algorithm can be easily extended to address the requirements of time-varying processes and processes requiring compensation for disturbance inputs and dead times. The application of the control system to multivariable processes and the ability to incorporate explicit constraints in the optimisation process are also demonstrated.

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Guner, Evren. "Adaptive Neuro Fuzzy Inference System Applications In Chemical Processes." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1252246/index.pdf.

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Neuro-Fuzzy systems are the systems that neural networks (NN) are incorporated in fuzzy systems, which can use knowledge automatically by learning algorithms of NNs. They can be viewed as a mixture of local experts. Adaptive Neuro-Fuzzy inference system (ANFIS) is one of the examples of Neuro Fuzzy systems in which a fuzzy system is implemented in the framework of adaptive networks. ANFIS constructs an input-output mapping based both on human knowledge (in the form of fuzzy rules) and on generated input-output data pairs. Effective control for distillation systems, which are one of the important unit operations for chemical industries, can be easily designed with the known composition values. Online measurements of the compositions can be done using direct composition analyzers. However, online composition measurement is not feasible, since, these analyzers, like gas chromatographs, involve large measurement delays. As an alternative, compositions can be estimated from temperature measurements. Thus, an online estimator that utilizes temperature measurements can be used to infer the produced compositions. In this study, ANFIS estimators are designed to infer the top and bottom product compositions in a continuous distillation column and to infer the reflux drum compositions in a batch distillation column from the measurable tray temperatures. Designed estimator performances are further compared with the other types of estimators such as NN and Extended Kalman Filter (EKF). In this study, ANFIS performance is also investigated in the adaptive Neuro-Fuzzy control of a pH system. ANFIS is used in specialized learning algorithm as a controller. Simple ANFIS structure is designed and implemented in adaptive closed loop control scheme. The performance of ANFIS controller is also compared with that of NN for the case under study.

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Teague, Karen J. "Fuzzy comprehensive evaluation (FCE) in military decision support processes." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/39023.

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Approved for public release; distribution is unlimited.
The United States has a tradition of military analysis using a federated or combined suite of models. However, these are not the only methods of modeling military problems. We consider the application and implications of foreign modeling approaches. The particular alternate technique we focus on is fuzzy comprehensive evaluation (FCE). FCE makes use of fuzzy mathematics, alone and in partnership with Analytic Hierarchy Process (AHP) models, to inform strategic and operational decisions. It is designed to aid leaders in capturing the complicated and sometimes fuzzy nature of multi-criteria decision problems through human knowledge and evaluations. These subjective inputs present criticisms regarding FCE solutions. FCE results are only as valid as the consistency of the subject matter experts opinions. Therefore, this thesis analyzes the FCE approach through a case study and evaluates the implications of FCE results when there is high variance in expert opinions.

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Jin, Gang-Gyoo. "Intelligent fuzzy logic control of processes with time delays." Thesis, Cardiff University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388058.

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Van, Den Bosch Magali Marie. "Simulation of ion exchange processes using neuro-fuzzy reasoning." Thesis, Cape Peninsula University of Technology, 2009. http://hdl.handle.net/20.500.11838/2161.

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Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2009.
Neuro-fuzzy computing techniques have been approached and evaluated in areas of process control; researchers have recently begun to evaluate its potential in pattern recognition. Multi-component ion exchange is a non-linear process, which is difficult to model and simulate as there are many factors influencing the chemical process which are not well understood. In the past, empirical isotherm equations were used but there were definite shortcomings resulting in unreliable simulations. In this work, the use of artificial intelligence has therefore been researched to test the effectiveness in simulating ion exchange processes. The branch of artificial intelligence used was the adaptive neuro fuzzy inference system. The objective of this research was to develop a neuro-fuzzy software package to simulate ion exchange processes. The first step towards building this system was to collect data from laboratory scale ion exchange experiments. Different combinations of inputs (e.g. solution concentration, resin loading, impeller speed), were tested to determine whether it was necessary to monitor all available parameters. The software was developed in MSEXCEL where tools like SOLVER could be utilised whilst the code was written in Visual Basic. In order to compare the neuro-fuzzy simulations to previously used empirical methods, the Fritz and Schluender isotherm was used to model and simulate the same data. The results have shown that both methods were adequate but the neuro-fuzzyapproach was the more appropriate method. After completion of this study, it could be concluded that a neuro-fuzzy system does not always have the ability to describe ion exchange processes adequately.

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Beyan, Timur. "A New Fuzzy-chaotic Modelling Proposal For Medical Diagnostic Processes." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12605924/index.pdf.

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Main reason of this study is to set forth the internal paradox of the basic approach of the artificial intelligence in the medical field to by discussing on the theoretical and application levels and to suggest solutions in theory and practice against that. In order to rule out the internal paradox in the medical decision support systematic, a new medical model is suggested and based on this, concepts such as disease, health, etiology, diagnosis and treatment are questioned. Meanwhile, with the current scientific data, a simple application sample based on how a decision making system which was set up by fuzzy logic and which is based on the perception of human as a complex adaptive system has been explained. Finally, results of the research about accuracy and validity of this application, current improvements based on the current model and the location on the artificial intelligence theory is discussed.

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Tecle, Aregai, and Shafiu Jibrin. "Incorporating Fuzzy Logic and Stochastic Processes into Multiobjective Forest Management." Arizona-Nevada Academy of Science, 2011. http://hdl.handle.net/10150/296992.

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Sozio, John Charles. "Intelligent Parameter Adaptation for Chemical Processes." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/34089.

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Reducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances. Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of "survival of the fittest". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process.
Master of Science

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Petley, Gary John. "A method for estimating the capital cost of chemical process plants : fuzzy matching." Thesis, Loughborough University, 1997. https://dspace.lboro.ac.uk/2134/11165.

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The purpose of this thesis is to improve the 'art' of early capital cost estimation of chemical process plants. Capital cost estimates are required in the early business planning and feasibility assessment stages of a project, in order to evaluate viability and to compare the economics of the alternative processes and operating conditions that are under consideration for the plant. There is limited knowledge about a new plant in the early stages of process development. Nevertheless, accurate cost estimates are needed to prevent incorrect decisions being made, such as terminating the development of a would-be profitable plant. The published early capital cost estimation methods are described. The methods are grouped into three types of estimate: exponent, factorial and functional unit. The performance of these methods when used to estimate the capital costs of chemical plants is assessed. A new estimating method is presented. This method was developed using the same standard regression techniques as used in the published methods, but derived from a new set of chemical plant data. The effect that computers have had on capital cost estimating and the future possibilities for the use of the latest computer techniques are assessed. This leads to the fuzzy matching technique being chosen to develop a new method for capital cost estimation. The results achieved when using fuzzy matching to estimate the capital cost of chemical plants are presented. These results show that the new method is better than those that already exist. Finally, there is a brief discussion of how fuzzy matching could be applied in the future to other fields of chemical engineering.

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Geng, Guang. "Modelling and control of some nonlinear processes in air-handling systems." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386699.

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GarcÃa, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.

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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.

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Kim, Sungshin. "A neuro-fuzzy approach to optimization and control of complex nonlinear processes." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14820.

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Baumgartner, Ronaldo. "Projeto habilis : a logica fuzzy contribuindo com o autoconhecimento e a escolha da profissão." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307149.

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Orientador: Geraldo Lucio Diniz
Dissertação (mestrado profissional) Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
Made available in DSpace on 2018-08-14T22:58:03Z (GMT). No. of bitstreams: 1 Baumgartner_Ronaldo_M.pdf: 1920777 bytes, checksum: e7ec868482e4dfcf3fc862840df5519f (MD5) Previous issue date: 2009
Resumo: O presente trabalho propõe através do modelo fuzzy, a construção de um sistema de inferência para uma ferramenta de autoconhecimento (Método Habilis), que forneça orientação às pessoas com relação às tomadas de decisão quanto à escolha da profissão, curso universitário, papéis profissionais, etc. através da análise de seus sonhos, medos e habilidades sensoriais, cognitivas e emocionais. Esta análise é feita através do método de inferência fuzzy do tipo Mamdani, utilizando-se da lógica fuzzy e da teoria dos conjuntos fuzzy. Este método tem como entrada, os sonhos, medos e habilidades da pessoa analisada e fornece como saída, índices que a classificam quanto ao possível sucesso nas profissões, cursos, papéis profissionais, etc. Neste trabalho, é apresentado como exemplo, o método de inferência fuzzy (Mamdani) para os potenciais funcionais.
Abstract: A fuzzy model is proposed in this thesis, in order to develop an inference system as a tool of self-knowledge (named by Habilis method), that have the purpose to provide a frame of reference for the people to make a decision of professional choice, university studies or professional functions, and others. The analysis is made based on data bank of experts and self-evaluation of the people that will be self-analyzed, with respect to yours setting of yearning-dream, limitation-phobias, sensory abilities, cognitive abilities and emotional abilities, using fuzzy sets and fuzzy logic by the inference method of Mandani. This analysis has as input the dreams, the fears and the abilities of the person that will be analyzed, and the system has as output a rank of professional success or performance of study or professional functions, through the rule-based computation. As example, is presented the inference method of Mandani for the professional functions.
Mestrado
Logica Fuzzy
Mestre em Matemática

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Kumar, Abhishek. "A tolerance allocation framework using fuzzy comprehensive evaluation and decision support processes." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37212.

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Tolerances play an important role in product fabrication. Tolerances impact the needs of the designer and the manufacturer. Engineering designers are concerned with the impact of tolerances on the variation of the output, while manufacturers are more concerned with the cost of fitting the parts. Traditional tolerance control methods do not take into account both these needs. In this thesis, the author proposes a framework that overcomes the drawbacks of the traditional tolerance control methods, and reduces subjectivity via fuzzy set theory and decision support systems (DSS). Those factors that affect the manufacturing cost (geometry, material etc) of a part are fuzzy (i.e. subjective) in nature with no numerical measure. Fuzzy comprehensive evaluation (FCE) is utilized in this thesis as a method of quantifying the fuzzy (i.e. subjective) factors. In the FCE process, the weighted importance of each factor affects the manufacturing cost of the part. There is no systematic method of calculating the importance weights. This brings about a need for decision support in the evaluation of the weighted importance of each factor. The combination of FCE and DSS, in the form of Conjoint Analysis (CA), is used to reduce subjectivity in calculation of machining cost. Taguchi's quality loss function is considered in this framework to reduce the variation in the output. The application of the framework is demonstrated with three practical engineering applications. Tolerances are allocated for three assemblies; a friction clutch, an accumulator O-ring seal and a Power Generating Shock Absorber (PGSA) using the proposed framework. The output performances of the PGSA and the clutch are affected by the allocated tolerances. On using the proposed framework, there is seen to be a reduction in variation of output performance for the clutch and the PGSA. The use of CA is also validated by checking efficiency of final tolerance calculation with and without use of CA.

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Rahmati, Seyed Saeed. "Implementation of a power adaptive fuzzy control system for end milling processes." Thesis, University of Ottawa (Canada), 1998. http://hdl.handle.net/10393/4388.

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This thesis reports a fuzzy control system designed for power control of end milling processes. As compared to most of the existing end milling control systems, the proposed fuzzy control system has the following advantages: (a) multi-parameter adjustment; (b) insensitive to changes in work piece geometry, cutter immersion rate, and workpiece material; (c) cost-efficient and easy to implement; and (d) mathematically modeling-free. The proposed fuzzy controller is a two-input two-output system with simple triangular membership functions for both feedrate and spindle speed. The system also features a scaling factor adjustment mechanism used to tune the gain coefficients. The system is first examined by simulation using Simulink and Matlab fuzzy logic toolbox and then verified by various experiments on a CNC milling machine. The experiments carried out include: (i) steel cutting with different change patterns (gradual linear change, gradual curved change, and abrupt step change) in depth of cut; (ii) steel cutting with full and partial immersion rates; (iii) steel cutting with variable immersion rate; and (iv) aluminum cutting with step change in depth of cut. The experimental results show that as compared to single parameter (feedrate) adjustment the material removal rate can be improved by up to 25% when both feedrate and spindle speed are adjusted. It is also shown that the proposed system is stable and displays very good transient performance under all of our experimental conditions, indicating sound robustness. The use of power sensor has significantly reduced investment cost and avoided tedious setups. The simplicity in design and implementation of the system has been demonstrated in our development process.

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Ghwanmeh, Sameh Hussein. "The investigation of on-line self-learning fuzzy logic control for non-linear processes." Thesis, Liverpool John Moores University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337808.

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Schuster, Alfons. "Supporting data analysis and the management of uncertainty in knowledge-based systems through information aggregation processes." Thesis, University of Ulster, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264825.

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Popescu, Mihail. "New sequence processing algorithms using hidden Markov models /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3115580.

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GuimarÃes, Felipe de Azevedo. "Self-tuning PID and Fuzzy controllers in industrial plants." Universidade Federal do CearÃ, 2007. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=4767.

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FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico
With the globalization and the competitiveness in all the levels of the industrial sector, the final product quality search became higher. On the other hand, energy saving became another important factor in modern industry. The consumption of electric energy in the industrial sector represents 45.5% of the total consumption of the country. The motor force represents most of this consumption, arriving to be superior of 80% in the textile, paper and cellulose sectors. This work presents two strategies of control, auto-adjustable PID and fuzzy controller, the objectives are a better final product quality and the energy saving. Ventilation and compression industrials process are used in this work. An analysis of limit cyclesâ presence through the descriptive function of the fuzzy controller is carried through, providing a previously validation of fuzzy controllerâs parameters by simulations, saving time in the adjust phase. Set-point changes are easily made and on-line, still the process is running, in the two considered controllers. Comparisons of energy consumption are made between the conventional strategies and the two strategies considered in this work.
Com a globalizaÃÃo e a competitividade em todos os nÃveis do setor industrial, a qualidade do produto final se tornou de importÃncia crescente. Por outro lado, a conservaÃÃo de energia no setor industrial se tornou outro fator importante na indÃstria moderna. O consumo de energia elÃtrica do setor industrial representa 45,5% do consumo total do paÃs. A forÃa motriz representa a maior parte deste consumo, chegando a ser superior a 80% nos setores tÃxtil, de papel e celulose. Este trabalho apresenta duas estratÃgias de controle, PID auto-ajustÃvel e controlador nebuloso, e tÃm como objetivos tanto a qualidade do produto final quanto a eficiÃncia energÃtica. SÃo utilizados processos de ventilaÃÃo e de compressÃo em escala industriais. Uma anÃlise da presenÃa dos ciclos limites atravÃs da funÃÃo descritiva do controlador nebuloso Ã realizada, de forma que os parÃmetros do controlador nebuloso podem ser previamente validados atravÃs de simulaÃÃes, poupando tempo na fase de ajuste dos parÃmentros do controlador. MudanÃas de set-point sÃo facilmente realizadas de forma online e sem que o funcionamento do processo seja descontinuado, nos dois controladores propostos. ComparaÃÃes quanto ao consumo de energia sÃo realizadas usando as estratÃgias de controle convencionais e as proposta neste trabalho.

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Weerasinghe, Manori. "Fault detection and diagnosis for complex multivariable processes using neural networks." Thesis, Liverpool John Moores University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298141.

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Mendes, Amanda dos Santos. "Gráficos de controle fuzzy para o monitoramento da média e amplitude de processos univariados /." Guaratinguetá, 2019. http://hdl.handle.net/11449/180937.

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Orientador: Marcela Aparecida Guerreiro Machado Freitas
Resumo: O controle de qualidade, principalmente por meio do uso de gráficos de controle, torna-se essencial na indústria para garantir um processo livre de causas especiais de variabilidade. Como os dados amostrais podem incluir incertezas advindas da subjetividade humana e dos sistemas de medição, a teoria dos conjuntos fuzzy pode ser aplicada aos gráficos de controle quando dados precisos não estiverem disponíveis. Para tal feito, os valores da característica de qualidade são fuzzificados a partir da inserção de incertezas e transformados em valores representativos para uma melhor comparação com o gráfico de controle tradicional. Este trabalho propõe o uso da lógica fuzzy aos gráficos de controle para monitorar a média e a amplitude de processos univariados, assim como dois gráficos de controle fuzzy baseados nas regras especiais de decisão: Synthetic e Side Sensitive Synthetic. O desempenho do gráfico de controle é medido pelo número médio de amostras até sinal (NMA). Verificou-se neste trabalho que os gráficos de controle fuzzy possuem maior eficiência que os gráficos de controle tradicionais para menores valores de α-cut, ou seja, maior incerteza inserida no processo e para cenários onde se tem uma maior diferença entre os limitantes de incerteza dos números fuzzy.
Abstract: Quality control, mainly through the use of control charts, becomes essential in the industry to ensure a process free from special causes of variability. As sample data may include uncertainties arising from human subjectivity and measurement systems, fuzzy set theory can be applied to control charts when accurate data is not available. For this purpose, the quality characteristic values are fuzzified from the insertion of uncertainties and transformed into representative values for a better comparison with the traditional control chart. This work proposes the use of fuzzy logic to control charts to monitor the mean and range of univariate processes, as well as two fuzzy control charts based on the special run rules: Synthetic and Side Sensitive Syntehtic. The performance of the control chart is measured by the average run length (ARL). It was verified in this work that the fuzzy control charts have higher efficiency than the traditional control charts for lower values of α-cut, that is, greater uncertainty inserted in the process and for scenarios where there is a greater difference between the limiting uncertainties of fuzzy numbers.
Mestre

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Sahab, Nazanin. "Adaptive type-2 non-singleton type-2 fuzzy logic system for handling numerical and linguistic uncertainties in complex processes." Thesis, University of Essex, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589540.

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Real world environments are characterized by high levels of linguistic and numerical uncertainties [Sahab 2011 c]. A Fuzzy Logic System' (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications. The first generation of FLSs was type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this thesis, we will present a new approach which is based on an adaptive non- singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents' linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor.

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Sales, Raquel JucÃ de Moraes. "Application of Fuzzy Logic in the Streeter-Phelps model to analyze the risk of contamination of rivers, considering multiple processes and multiple launch." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=11354.

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CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior
Na tentativa de facilitar o diagnÃstico dos diversos fatores que afetam a qualidade da Ãgua e antever possÃveis impactos futuros sobre o meio ambiente , sÃo adotadas aÃÃes que racionalize m o uso da Ãgua a partir da otimizaÃÃo de processos naturais ou tecnolÃgicos. A modelagem matemÃtica Ã um exemplo disso e, em conjunto com a Teoria Fuzzy , que permite fazer a anÃlise dos resultados sem necessidade de significativos bancos de dados, pode - se estabelecer o risco como indicador de contaminaÃÃo das Ãguas de rios, sendo de valor prÃtico na tomada de decisÃo e concessÃo de outorga de lanÃamentos. Neste estudo, foi desenvolvido um modelo matemÃtico aplicado Ãs equaÃÃes completas de Streeter - Phelps utilizando a Teoria dos nÃmeros Fuzzy , a fim de analisar o risco de contaminaÃÃo de um curso d'Ãgua que recebe agentes poluentes de mÃltiplas fontes de lanÃamento. Pelas simulaÃÃes do modelo, foram analisados diferentes cenÃrios, verificando a influÃncia d os seus parÃmetros, bem como o lanÃamento de fontes poluidoras pontuais e difusas, nos percentuais de risco. De acordo com os resultados, observou - se que a quantidade de carga lanÃada tem influÃncia no tempo de diluiÃÃo desta massa no sistema, de forma que , para maiores valores de lanÃamento, o tempo de diluiÃÃo Ã menor, favorecendo os processos de decaimento e formaÃÃo da camada bentÃnica; em relaÃÃo Ãs reaÃÃes fÃsicas, quÃmicas e biolÃgicas, verifica - se que os processos de sedimentaÃÃo, fotossÃntese e res piraÃÃo, para os dados mÃdios encontrados em literatura, tem pequena influÃncia no comportamento das curvas de concentraÃÃo de OD e curvas de risco, enquanto que o processo de nitrificaÃÃo tem forte influÃncia; jÃ a temperatura desempenha um significativo papel no comportamento do OD, onde, para valores maiores, maior serÃ o dÃficit OD e, em consequÃncia, aumento dos percentuais de risco. Por fim, o modelo desenvolvido como proposta de facilitar a tomada de decisÃo no controle de lanÃamento de efluentes em rios mostrou - se uma alternativa viÃvel e de valor prÃtico de anÃlise, jÃ que os objetivos foram alcanÃados
In an attempt to facilitate the diagnosis of the various factors that affect water quality and predict possible future impacts on the environment, actions to rationalize the use of water from the optimization of natural and technological processes are adopted. Mathematical modeling is one example and, together with Fuzzy Theory, which allows the analysis of the results without the need for significant databases, one can establish the risk as an indicator of contamination of rivers, and of practical value in decision making and allocation of grant releases. In this study, the full Streeter-Phelps equations, using the Fuzzy set Theory, was applied, in order to analyze the risk of contamination of a watercourse that receives multiple sources release pollutants. Through the model simulations, different scenarios were analyzed, and the influence of its parameters as well as the launch point and nonpoint pollution sources, in the calculation of the risk. According to the results, it was observed that the amount of discharge released influences the time of the mass dilution in the system, so that for higher values of launch, the dilution time is less favoring the formation and decay processes of benthic layer; regarding the physical, chemical and biological reactions, it appears that sedimentation processes, photosynthesis and respiration, concerning with the average data found in literature, have little influence on the behavior of the curves of DO concentration curves and risk, while the nitrification process has a strong influence; with respect to the temperature, the results showed that it plays a significant role in the behavior of DO, where, for larger values of it, the higher the DO deficit and, consequently, increase in the risk. Finally, the model developed as a proposal to facilitate the decision making in the control of discharge of effluents into rivers proved to be a viable and practical analytical alternative way, since the goals were achieved.

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25

Pan, YaDung. "Fuzzy adaptive recurrent counterpropagation neural networks: A neural network architecture for qualitative modeling and real-time simulation of dynamic processes." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187101.

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In this dissertation, a new artificial neural network (ANN) architecture called fuzzy adaptive recurrent counterpropagation neural network (FARCNN) is presented. FARCNNs can be directly synthesized from a set of training data, making system behavioral learning extremely fast. FARCNNs can be applied directly and effectively to model both static and dynamic system behavior based on observed input/output behavioral patterns alone without need of knowing anything about the internal structure of the system under study. The FARCNN architecture is derived from the methodology of fuzzy inductive reasoning and a basic form of counterpropagation neural networks (CNNs) for efficient implementation of finite state machines. Analog signals are converted to fuzzy signals by use of a new type of fuzzy A/D converter, thereby keeping the size of the Kohonen layer of the CNN manageably small. Fuzzy inferencing is accomplished by an application-independent feedforward network trained by means of backpropagation. Global feedback is used to represent full system dynamics. The FARCNN architecture combines the advantages of the quantitative approach (neural network) with that of the qualitative approach (fuzzy logic) as an efficient autonomous system modeling methodology. It also makes the simulation of mixed quantitative and qualitative models more feasible. In simulation experiments, we shall show that FARCNNs can be applied directly and easily to different types of systems, including static continuous nonlinear systems, discrete sequential systems, and as part of large dynamic continuous nonlinear control systems, embedding the FARCNN into much larger industry-sized quantitative models, even permitting a feedback structure to be placed around the FARCNN.

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26

Freitag, Steffen, Wolfgang Graf, and Michael Kaliske. "Prognose des Langzeitverhaltens von Textilbeton-Tragwerken mit rekurrenten neuronalen Netzen." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1244048026002-79164.

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Zur Prognose des Langzeitverhaltens textilbetonverstärkter Tragwerke wird ein modellfreies Vorgehen auf Basis rekurrenter neuronaler Netze vorgestellt. Das Vorgehen ermöglicht die Prognose zeitveränderlicher Strukturantworten unter Berücksichtigung der gesamten Belastungsgeschichte. Mit unscharfen Größen aus Messungen an Versuchstragwerken werden rekurrente neuronale Netze trainiert. Anschließend ist die unscharfe Prognose des Tragverhaltens möglich.

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27

Smolík, Luboš. "Posouzení informačního systému firmy a návrh změn." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2016. http://www.nusl.cz/ntk/nusl-254260.

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This thesis focuses on the draft amendments for further business development in the company. Draft amendments based on findings from the analysis of the current state of IS and related processes in the company. The proposed amendments will take place requirements on the company information system and the strategic goals of the company.

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28

Ling, Hong. "Implementation of Stochastic Neural Networks for Approximating Random Processes." Master's thesis, Lincoln University. Environment, Society and Design Division, 2007. http://theses.lincoln.ac.nz/public/adt-NZLIU20080108.124352/.

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Artificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biological systems on the basis of mimicking the information processing methods in the human brain. The capability of current ANNs only focuses on approximating arbitrary deterministic input-output mappings. However, these ANNs do not adequately represent the variability which is observed in the systems natural settings as well as capture the complexity of the whole system behaviour. This thesis addresses the development of a new class of neural networks called Stochastic Neural Networks (SNNs) in order to simulate internal stochastic properties of systems. Developing a suitable mathematical model for SNNs is based on canonical representation of stochastic processes or systems by means of Karhunen-Loève Theorem. Some successful real examples, such as analysis of full displacement field of wood in compression, confirm the validity of the proposed neural networks. Furthermore, analysis of internal workings of SNNs provides an in-depth view on the operation of SNNs that help to gain a better understanding of the simulation of stochastic processes by SNNs.

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29

Oliveira, Alessandro Bertolani. "Modelo de predição para análise comparativa de técnicas neuro-fuzzy e de regressão." Universidade Federal do Espírito Santo, 2010. http://repositorio.ufes.br/handle/10/6386.

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We investigate strategies to define prediction models for a quality parameter of an industrial process. We estimate this variable using computational intelligence and in special regression methods. The main contribution of this paper is the comparative analysis of heuristic training models to create the prediction system. We propose two main paradigms to obtain the system, machine learning and hybrid artificial neural networks. The resulting system is a prototype for the intelligent supervision of a real-time production process. Statistical tools are used to compare the performance of the regression based predictor and the neuro-fuzzy based predictor, considering the degree of adaptation of the system to the problem and its generalization ability
Neste trabalho são investigadas estratégias para a elaboração de Modelos de Predição que possam ser utilizados no monitoramento de uma variável de qualidade pertencente a um determinado Processo Produtivo Industrial. Neste cenário, a variável de qualidade é estimada por meio de técnicas da Inteligência Computacional e empiricamente avaliada na resolução de problemas de regressão. A principal contribuição desta monografia é a análise comparativa de Técnicas da Inteligência Computacional associadas às estratégias heurísticas de treinamento para a construção dos Modelos de Predição. São propostas duas linhas de pesquisa investigadas a partir de uma pesquisa empírica dos dados, e analisados a partir de dois grandes ramos da Inteligência Computacional Aprendizagem de Máquina e Redes Neurais Híbridas. Os Modelos de Predição desenvolvidos são protótipos conceituais para potencial implementação de Sistemas Inteligentes em tempo real de uma planta industrial. O método de construção dos Modelos de Predição por técnicas de Regressão é comparado com o método de construção do Modelo de Predição por redes Neuro-Fuzzy e analisados por critérios estabelecidos a partir de ferramentas estatísticas que levam em consideração os níveis de adequação e generalização dos mesmos. Ao final, são apresentados resultados dos métodos implementados sobre a mesma base de dados bem como os pertinentes trabalhos futuros

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30

Leite, Jefferson Cruz dos Santos 1981. "Sistemas dinâmicos fuzzy aplicados a processos difusivos." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306458.

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Orientador: Rodney Carlos Bassanezi
Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica
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Resumo: Neste trabalho definiremos solução fuzzy para problemas que envolvam difusão e exploraremos algumas propriedades importantes como unicidade e estabilidade dessas soluções. Basicamente estamos interessados em considerar algumas características importantes desses problemas difusivos como incertos, para isso, usaremos o conceito de numero fuzzy. Termos como coeficiente de difusão e condição inicial serão considerados como incertos e através da extensão de Zadeh aplicado a solução da equação determinística associada ao problema teremos a solução fuzzy. Serão obtidas também soluções via base de regras, utilizando sistemas dinâmicos pfuzzy, garantindo assim, uma maneira eficiente e prática de obtermos, boas respostas para os problemas, sem necessariamente termos as soluções explícitas. Aplicações desses resultados também serão apresentados
Abstract: This work will define fuzzy solution for problems involving di_usion and explore some important properties such as uniqueness and stability of these solutions. Basically we are interested in considering some important features of these diffusion problems as uncertain and, we use the concept of fuzzy numbers for this. Terms such as diffusion coefficient and initial condition are considered as uncertain and by the extension of Zadeh's solution applied to deterministic equation associated with the problem we have the fuzzy solution. Solutions for rule-base situations are also obtained, using p-fuzzy dynamic systems, thus guaranteeing an, efficient and practical way of obtaining adequate answers to the problems, not necessarily under the explicit solutions. Applications of these results will also be discussed
Doutorado
Matematica Aplicada
Doutor em Matemática Aplicada

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31

Bartasson, Maria Cristiane. "Desenvolvimento de software de modelagem de processos de sintese de polietileno e correlações entre propriedades das resinas." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266788.

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Orientador: Rubens Maciel Filho
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica
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Resumo: Esta tese propõe a aplicação de Lógica Fuzzy para a modelagem de um processo de polimerização de eteno em baixa pressão para predição de propriedades de qualidade. O software de modelagem é inovador com relação à questão de relacionar como variável de saída, características da qualidade e de desempenho das resinas a partir de condições de síntese. Adicionalmente, foram propostos modelos de correlação semi-empírica de propriedades das resinas em estudo. As propostas apresentadas possibilitam o uso de simulações para obtenção de respostas rápidas, tendo aplicação em ambiente industrial
Abstract: This thesis proposes an application of the Fuzzy Logic the modeling of an ethane low pressure polymerization process to predict properties related to quality. The modeling software is innovative with regard to the question of correlate out variables concerning to quality characteristics to synthesis conditions. Additionally, correlations semi-empirical models have been considered to predict resin¿s properties. The presented proposals enable the use of simulation for the attainment of fast answers for the use in industry
Doutorado
Desenvolvimento de Processos Químicos
Mestre em Engenharia Química

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32

Gonçalves, Caio Márcio 1963. "Abordagem de resolução de problemas complexos orientada aos princípios de processo." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/257940.

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Orientador: André Munhoz de Argollo Ferrão
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo
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Resumo: Os métodos, técnicas e abordagens clássicas de identificação e caracterização de problema parecem não satisfazer e responder plena e prontamente aos problemas complexos da sociedade contemporânea. A complexidade dos problemas atuais requer a adoção de ferramentas inovadoras, centradas no problema e não em efeitos ou soluções pré-concebidas. O desenvolvimento da humanidade é um processo empreendedor das sociedades que a compõem e deve estar orientado ao ser humano e seu contexto. Esse escopo enfatiza a noção de processo, da possibilidade, da lógica difusa, do complexo, do transdisciplinar, bem como o emprego de estratégias investigativas, inclusive do tipo pesquisa-ação. O propósito da pesquisa converge para um tipo de engenharia social que visa a definição de elementos estratégicos para a definição de uma abordagem voltada para a real identificação do problema. A "Abordagem de Resolução de Problema Complexo Orientada aos Princípios de Processo" [ARPCOOP] é o resultado da pesquisa e está fundamentada no arcabouço teórico existente sobre resolução de problema e nos princípios da visão de mundo em processo, lançando luzes sobre o problema e não sobre a solução
Abstract: The methods, techniques, and classical approaches for the identification and characterization of a problem does not seem to neither please, nor fully answer the complex problems of contemporary society in a speedy manner. The complexity of today's problems requires the adoption of innovative tools, problem-centered rather than in effects or preconceived solutions. The human development is an entrepreneurial process by the comprising societies, and should be directed to the human being and its context. This scope emphasizes the notion of process, the complex, the trans disciplinary, as well as the use of strategic action research investigations. The purpose of the research converges to a type of social engineering aimed at defining strategic elements to form an approach directed at identifying the real problem. Known as "The Approach to Complex Problem Solving Oriented toward the Principles of Process" [ARPCOOP], the proposal is based on existing theoretical framework of a problem and on the principles of the world view in the process, casting light on the problem and not the solution
Doutorado
Recursos Hidricos, Energeticos e Ambientais
Doutor em Engenharia Civil

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33

Nascimento, Renato Rosa do. "Controle não linear aplicado a processos de lingotamento contínuo de tiras." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-05062017-090446/.

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O presente trabalho tem como objetivo explorar o uso de técnicas de controle avançados na indústria siderúrgica. Propõe-se uma estratégia de controle do nível do aço da piscina formada entre os rolos de um sistema lingotamento contínuo de tiras (LCT) utilizando a tecnologia twin-roll (rolos duplos). O processo LCT rolos duplos tem por finalidade a produção de tiras solidificadas de espessura constante sob uma força de separação entre os rolos também constante. O nível de aço bem como a força de separação são as variáveis mais críticas para a produção de tiras de aço de alta qualidade. O nível pode ser controlado usando a entrada de aço ou a velocidade de laminação. Entretanto, a velocidade de laminação é usualmente utilizada para regular a força de separação entre os rolos. A estratégia de controle proposta inclui a incorporação de um tundish intermediário submerso na piscina. O controle do nível é então feito a partir da saída de aço do tundish intermediário. Consideramos as técnicas de controle linearizante por realimentação de estado e de controle fuzzy usando ambos os modelos Takagi-Sugeno (T-S) e Mamdani. Resultados de simulação são apresentados para uma planta instalada no Instituto de Pesquisa Tecnológica (IPT) de São Paulo, divisão de metalurgia (DIMET).
The aim of this work is to explore the use of advanced control techniques in the metallurgical industry. A control strategy to regulate the molten steellevel of a strip-casting process is proposed. The process produces a solidified strip of constant thickness given by the roll gap under a constant roll separation force. Along with the molten steel level the rool separation force are the most criticaI process variables. The molten steel level may be controlled using the tundish output flow or the casting speed. However, the casting speed is usually used to control the roll force separation. In the control strategy proposed it is incorporated an intermediary tundish submerse into the pool between the rotating rolls to improve the strip thickness uniformity. The molten steel level is thus controlled by the intermediary tundish output flow. Conventional PI, feedback linearizing plus a fuzzy control term and a fuzzy controller in a cascade configuration are considered. Simulation results are presented considering the real system parameters of a plant installed at the Instituto de Pesquisa Tecnológica (IPT) de São Paulo, Divisão de Metalurgia (DIMET).

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34

Taylor, Graham. "Reinforcement Learning for Parameter Control of Image-Based Applications." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/832.

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The significant amount of data contained in digital images present barriers to methods of learning from the information they hold. Noise and the subjectivity of image evaluation further complicate such automated processes. In this thesis, we examine a particular area in which these difficulties are experienced. We attempt to control the parameters of a multi-step algorithm that processes visual information. A framework for approaching the parameter selection problem using reinforcement learning agents is presented as the main contribution of this research. We focus on the generation of state and action space, as well as task-dependent reward. We first discuss the automatic determination of fuzzy membership functions as a specific case of the above problem. Entropy of a fuzzy event is used as a reinforcement signal. Membership functions representing brightness have been automatically generated for several images. The results show that the reinforcement learning approach is superior to an existing simulated annealing-based approach. The framework has also been evaluated by optimizing ten parameters of the text detection for semantic indexing algorithm proposed by Wolf et al. Image features are defined and extracted to construct the state space. Generalization to reduce the state space is performed with the fuzzy ARTMAP neural network, offering much faster learning than in the previous tabular implementation, despite a much larger state and action space. Difficulties in using a continuous action space are overcome by employing the DIRECT method for global optimization without derivatives. The chosen parameters are evaluated using metrics of recall and precision, and are shown to be superior to the parameters previously recommended. We further discuss the interplay between intermediate and terminal reinforcement.

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Fonseca, Rodolpho Rodrigues 1987. "Desenvolvimento de um controlador Fuzzy - Split-range aplicado em um reator batelada para a produção de biodiese." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266608.

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Orientador: Flávio Vasconcelos da Silva
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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Resumo: Devido ao aumento da demanda nacional e mundial por combustíveis renováveis e novas tecnologias para melhoria de seus processos, é inegável a importância do desenvolvimento de novos controladores que possam garantir o funcionamento adequado destes sistemas. Neste contexto, este trabalho focou no desenvolvimento de um tipo de controlador não convencional baseado em inteligência artificial (Lógica Fuzzy) associado a uma estratégia Split-range para a manutenção da temperatura de reação de transesterificação do óleo de soja. Os ensaios foram conduzidos em um reator batelada totalmente instrumentado, monitorado e controlado via SCADA (Supervisory Control And Data Acquisition). Verificou-se que a melhor estratégia proposta para os sistemas de controle Fuzzy - Split-range na regulação da temperatura do reator foi a que empregou 147 regras sem a mistura de utilidades na jaqueta do reator, obtendo rápida estabilização da temperatura do reator, aproximadamente 15 minutos, e menor esforço de controle quando comparado às demais estratégias testadas. Como ferramentas de análise comparativa do sistema de controle foram utilizados os critérios de desempenho IAE, ISE e ITAE, além dos esforços de controle requeridos pelas válvulas durante os ensaios. Os resultados mostraram que a combinação Fuzzy - Split-range é viável no controle de temperatura, podendo ser estendida a demais processos industriais.
Abstract: In fact of national and international demand increasing for renewable fuels as biodiesel and also new technologies for process enhancement, it is worthy of attention the development of new controllers that guarantee adequate biodiesel production process control. In this context, this work applied the design of a non-conventional controller based on artificial intelligence (Fuzzy Logic) associated with Split-range strategy to regulate the temperature of soybean oil transesterification. The tests were conducted in a instrumented batch reactor, monitored and controlled by a SCADA (Supervisory Control And Data Acquisition) system. For the studied process control, the best combination set among the Fuzzy - Split-range strategies for the reactor's temperature control applied 147 set rules and no mixture of utilities in reactor's jacket. With fast temperature estabilization in almost 15 min, less control effort was required by the system among the strategies testeds. Performance criterions as IAE, ISE and ITAE were used to support comparative analysis, either control efforts by valves were used. The results show that Fuzzy - Split-range strategy is viable in biodiesel batch reactor temperature control, promising to application in others chemical processes.
Mestrado
Sistemas de Processos Quimicos e Informatica
Mestre em Engenharia Química

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36

Costa, Bruno Sielly Jales. "Ambiente para desenvolvimento de aplica??es fuzzy industriais." Universidade Federal do Rio Grande do Norte, 2009. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15307.

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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
This paper describes the design, implementation and enforcement of a system for industrial process control based on fuzzy logic and developed using Java, with support for industrial communication protocol through the OPC (Ole for Process Control). Besides the java framework, the software is completely independent from other platforms. It provides friendly and functional tools for modeling, construction and editing of complex fuzzy inference systems, and uses these logical systems in control of a wide variety of industrial processes. The main requirements of the developed system should be flexibility, robustness, reliability and ease of expansion
Este trabalho descreve o projeto, implementa??o e aplica??o de um sistema para controle de processos industriais, baseado na l?gica fuzzy e desenvolvido na linguagem java, com suporte a comunica??o industrial atrav?s do protocolo OPC (Ole for Process Control). Al?m do framework java, o software ? completamente independente de outras plataformas. Ele prov? ferramentas amig?veis e funcionais para modelagem, constru??o e edi??o de sistemas de infer?ncia fuzzy complexos, e utiliza tais sistemas l?gicos no controle de uma grande variedade de processos industriais. Os principais requisitos do sistema desenvolvido devem ser flexibilidade, robustez, confiabilidade e facilidade de expans?o

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37

Alsadaie, Salih M. M. "Design and Operation of Multistage Flash (MSF) Desalination: Advanced Control Strategies and Impact of Fouling. Design operation and control of multistage flash desalination processes: dynamic modelling of fouling, effect of non-condensable gases on venting system design and implementation of GMC and fuzzy control." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/15924.

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The rapid increase in the demand on fresh water due the increase in the world population and scarcity of natural water puts more stress on the desalination industrial sector to install more desalination plants around the world. Among these desalination plants, multistage flash desalination process (MSF) is considered to be the most reliable technique of producing potable water from saline water. In recent years, however, the MSF process is confronting many problems to cut off the cost and increase its performance. Among these problems are the non-condensable gases (NCGs) and the accumulation of fouling which they work as heat insulation materials. As a result, the MSF pumps and the heat transfer equipment are overdesigned and consequently increase the capital cost and decrease the performance of the plants. Moreover, improved process control is a cost effective approach to energy conservation and increased process profitability. Thus, this study is motivated by the real absence of detailed kinetic fouling model and implementation of advance process control (APC). To accomplish the above tasks, commercial modelling tools can be utilized to model and simulate MSF process taking into account the NCGs and fouling effect, and optimum control strategy. In this research, gPROMS (general PROcess Modeling System) model builder has been used to develop the MSF process model. First, a dynamic mathematical model of MSF is developed based on the basic laws of mass balance, energy balance and heat transfer. Physical and thermodynamic properties of brine, distillate and water vapour are included to support the model. The model simulation results are validated against actual plant data published in the literature and good agreement with these data is obtained. Second, the design of venting system in MSF plant and the effect of NCGs on the overall heat transfer coefficient (OHTC) are studied. The release rate of NCGs is studied using Henry’s law and the locations of venting points are optimised. The results reveal that high concentration of NCGs heavily affects the OHTC. Furthermore, advance control strategy namely: generic model control (GMC) is designed and introduced to the MSF process to control and track the set points of the two most important variables in the MSF plant; namely the Top Brine Temperature (TBT) which is the output temperature of the brine heater and the Brine Level (BL) in the last stage. The results are compared to conventional Proportional Integral Derivative Controller (PID) and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system. In addition, a new control strategy called hybrid Fuzzy-GMC is developed and implemented to control the same aforementioned loops. Its results reveal that the new control outperforms the pure GMC in some areas. Finally, a dynamic fouling model is developed and incorporated into the MSF dynamic process model to predict fouling at high temperature and high velocity. The proposed dynamic model considers the attachment and removal mechanisms of calcium carbonate and magnesium hydroxide with more relaxation of the assumptions. Since the MSF plant stages work as a series of heat exchangers, there is a continuous change of temperature, heat flux and salinity of the seawater. The proposed model predicts the behaviour of fouling based on the physical and thermal conditions of every single stage of the plant.

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Costa, Herbert Rodrigues do Nascimento. "Aplicação de técnicas de inteligência artificial em processos de fabricação de vidro." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-09032007-171929/.

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A Inteligência Artificial atualmente é um vasto campo de pesquisa. Existem diversas técnicas sendo pesquisadas, sendo que nesta tese foram utilizadas a Teoria Fuzzy, Árvores de Decisão e Redes Neurais. As três técnicas têm sido empregadas com sucesso nas mais diversas aplicações nas áreas de automação e controle, reconhecimento de padrões, reconhecimento de voz, detecção de falhas e classificação, entre outras. A Teoria Fuzzy permite trabalhar com as incertezas e provê um entendimento simbólico para compreensão do conhecimento. As Árvores de Decisão têm capacidade de construir decisões simbólicas para a classificação de problemas e, através do conhecimento obtido, pode-se construir regras simbólicas para uma tomada de decisão. A Teoria Fuzzy também pode ser incorporada às árvores de decisão, aumentando seu poder de representação e aplicabilidade. As Redes Neurais (algoritmo back-propagation) têm apresentado ótimos resultados na aprendizagem de funções e em problemas de classificação. A contribuição desta tese é mostrar a aplicação das três técnicas de Inteligência Artificial (IA) em processos de fabricação de Vidro. Os processos de fabricação do vidro foram analisados e a proposta da tese é a aplicação das técnicas de IA nas fábricas de produção de vidros para embalagens e vidros planos. Na primeira fábrica aplicam-se as técnicas de IA para classificar os defeitos que ocorrem no Vidro para Embalagens, em função das condições operacionais dos fornos de fusão. Na segunda fábrica aplicam-se as técnicas para classificar os defeitos em função das matérias primas utilizadas na produção do vidro. Na terceira fábrica as técnicas são aplicadas na classificação dos padrões de fabricação do vidro plano. Os resultados obtidos com a classificação de defeitos e padrões foram de maneira geral satisfatórios. As três técnicas de IA apresentadas foram utilizadas para a análise das bases de dados nas três fábricas de vidro estudadas nesta tese. As técnicas de IA obtiveram classificações satisfatórias para os defeitos do vidro para embalagens e para classificar os padrões dos vidros planos. Os resultados obtidos a partir das técnicas são comparados e apresentam resultados promissores.
The Artificial Intelligence now is a vast research field. There are several techniques exist being researched. In this thesis Fuzzy Theory, Decision Trees and Neural Networks were used. The three techniques have been successfully applied in several applications in the areas of automation and control, pattern recognition, voice recognition, detection of flaws and classification, among others. The Fuzzy Theory allows to work with the uncertainties and they provide a symbolic understanding for understanding of the knowledge. The Decision Trees have capacity to build symbolic decisions for the classification of problems and through the knowledge obtained by the tree could be built symbolic rules for a socket of decision. The Fuzzy Theory can also be incorporate them tree of decision increasing the representation power and applicability of the Decision trees. Neural Networks (algorithm back-propagation) it has been presenting great results in the learning of functions and in classification problems. The contribution of this thesis is to show the application of the three techniques of Artificial Intelligence (AI) in processes of production of Glass. The processes of production of the glass were analyzed and the proposal of the thesis is the application of the techniques of AI in the factories of production of glasses to packings and plane glasses. In the first factory it is applied the techniques of AI to classify the defects that happen in the Glass for Packings in function of the operational conditions of the coalition ovens. In the second factory it is applied the techniques to classify the defects in the matters cousins\' function used in the production of the glass. In the third factory the techniques are applied in the classification of the patterns of production of the plane glass. The results obtained with the classification of defects and patterns were in a satisfactory general way. The three techniques of AI presented were used for the analysis of the bases of data in the three glass factories studied in thesis. The techniques of AI obtained a satisfactory classification for the defects of the glass for packings and for the patterns of the plane glasses. The results obtained starting from the techniques are compared and they present promising results.

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Lima, Nádson Murilo Nascimento. "Modelagem e controle hibrido preditivo por logica fuzzy de processos de polimerização." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266795.

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Orientador: Rubens Maciel Filho
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica
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Resumo: A síntese de controladores representa uma importante vertente dos desenvolvimentos atuais no campo da pesquisa acadêmica e industrial. Um controlador bem projetado pode significar sucesso no que se refere aos objetivos de produção, sendo gerados materiais com as especificações desejadas e proporcionando que o sistema opere sob certas restrições, levando em consideração aspectos relativos à operabilidade, segurança e minimização de resíduos. Para tanto, sabe-se que as etapas de modelagem são fundamentais para a delineação de estratégias de controle. No entanto, a obtenção de representações matemáticas precisas e, ao mesmo tempo, aplicáveis para controle da maioria dos processos de interesse da engenharia química é uma tarefa árdua, devido à presença de comportamentos dinâmicos não lineares e variantes ao longo do espaço e do tempo. Deste modo, busca-se a obtenção de modelos mais simples, porém dotados da imprescindível representatividade inerente aos sistemas de produção, a fim de serem projetadas estruturas de controle adequadas para cada necessidade específica. Este trabalho enfoca o desenvolvimento de um controlador híbrido preditivo baseado em modelos nebulosos (fuzzy) tipo Takagi-Sugeno para processos de polimerização, os quais apresentam dinâmicas altamente complexas e de difícil modelagem matemática, dificultando assim a aplicação de metodologias convencionais de controle. Foram considerados dois casos de estudo para análise de desempenho do controlador proposto: o processo de copolimerização em solução do metacrilato de metila e acetato de vinila, e a copolimerização industrial do eteno/1-buteno com catalisador Ziegler-Natta solúvel. Os modelos fenomenológicos de ambos os processos já se encontram descritos na literatura, sendo considerados como plantas virtuais para geração de dados dinâmicos e implementação do controlador. A partir de simulações computacionais, os modelos dinâmicos nebulosos funcionais foram construídos ¿ os quais demonstraram excelentes capacidades para predição das saídas dos processos como uma função dos dados dinâmicos de entrada ¿ sendo, posteriormente, inseridos na estrutura interna do controle preditivo DMC (Dynamic Matrix Control). A escolha do controlador DMC como base para o desenvolvimento da estrutura proposta deve-se ao fato de sua notória aplicabilidade industrial, aliada à simplicidade de projeto e execução, além de possibilitar a incorporação de restrições nas variáveis controladas e manipuladas. Por fim, foram comparados os desempenhos entre os controladores híbrido e DMC convencional para os problemas regulatório e servo, fornecendo resultados satisfatórios em ambas as situações. Isto demonstra o alto potencial do algoritmo proposto para o controle de sistemas não lineares
Abstract: Controller synthesis represents an important slope of recent development in the field of academic and industry research. A well-projected controller may express the success in reference to the aim of production, and it also creates materials with desirable specification and it allows that the system operates under certain restrictions, considering aspects related to operability, safety and minimization of residue. Then, it¿s known that modeling stage is fundamental to delineation of controller strategies. However, the obtaining of precise and applicable mathematical representation to control most of relevant process in chemist engineering is an arduous task, because of the presence of nonlinear dynamic behavior and space-time variation. Therefore, it searches obtaining of simplier models, but gifted of essential representativity inherent to production system, because of this suitable control structure to each specific necessity projected. This work focus on the development of predictive hybrid controller based on fuzzy models, type Takagi-Sugeno, to process of copolymerization, that present high complex dynamic and hard mathematical modeling, what is also difficult to apply conventional methodologies of control. Two study cases that present analyze of purpose controller were considered: the process of copolymerization in solution of methyl methacrylate and vinyl acetate, and industry copolymerization of ethene/1-butene with Ziegler-Natta catalyzation. The phenomenologic models of the two processes have already described in relevant literature, and they are considered as virtual plants to create dynamic data and controller implementation. Based on computer simulation, functional dynamic fuzzy models were made ¿ they demonstrate excellent capacity to output prediction in process as a function of input dynamic data ¿ and they are, subsequently, inserted in an internal structure of DMC (Dynamic Matrix Control) predictive control. The choice of DMC controller as the base to development of proposal structure is responsible to the fact of its well-known industry applicability, allied to simplicity of the project and execution, beyond it makes possible the incorporation of restrictions in controlled and manipulated variables. Finally, the performance between hybrid controller and conventional DMC to regulatory and servo problems were compared, and it supplies satisfactory results in both situation. It demonstrates high potential of propose algorithm to control nonlinear system
Mestrado
Desenvolvimento de Processos Químicos
Mestre em Engenharia Química

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Silveira, Graciele Paraguaia 1982. "Métodos numéricos integrados à lógica Fuzzy e método estocástico para solução de EDP's = uma aplicação à dengue." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307567.

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Orientadores: Laécio Carvalho de Barros, Laércio Luis Vendite
Tese (doutorado) - Universidade Estadual de Campinas,Instituto de Matemática, Estatística e Computação Científica
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Resumo: Neste trabalho um modelo matemático (do tipo SIR - Suscetível, Infectado, Recuperado) integrado foi proposto para o estudo do espalhamento espaço - temporal da dengue. O modelo é descrito por Equações Diferenciais Parciais cujas soluções numéricas foram obtidas a partir de um esquema híbrido, que também incorpora lógica fuzzy e método estocástico. Utilizou-se os métodos WENO-5 (esquemas essencialmente não-oscilatórios, de ordem 5) para regiões não suaves do domínio e esquemas de diferenças finitas de alta ordem para as regiões suaves na discretização espacial. Além disso, um esquema lifting foi construído para definir suavidade ou não, nas regiões. Para a evolução temporal, escolheu-se um método de Runge-Kutta TVD (Valor Total Decrescente) de ordem 3. Os parâmetros incertos, relacionados ao comportamento do Aedes aegypti foram estimados fazendo-se uso de Sistemas Baseados em Regras Fuzzy (SBRF). Tais parâmetros dependem de hábitos da população, que fornece criadouros e sangue para a maturação dos ovos da fêmea e dependem ainda da ocorrência de chuvas. Esta variável, quantidade de chuva, apresenta dependência estocástica nos valores amostrados e, por essa razão, optou-se pelo Método Cadeia de Markov (de ordem 2). Dados reais sobre o comportamento da doença e proliferação do vetor, na região sul de Campinas, foram obtidos da Secretaria Municipal de Saúde, IAC (Instituto Agronômico de Campinas) e de especialistas do epiGeo (Laboratório de Análise Espacial de Dados Epidemiológicos - UNICAMP). Simulações e análise de variados cenários foram realizadas, visando obter cenários (mapas) a respeito do espalhamento da doença, levando em conta características típicas do domínio estudado. Por fim, um modelo do tipo Takagi-Sugeno - regras fuzzy, cujas saídas são EDP's - foi elaborado para a análise do risco de dengue na região do domínio, a partir de um mapa de risco relativo desenvolvido pelos pesquisadores do epiGeo
Abstract: In this work we proposed an integrated mathematical model of the type SIR - Susceptible, Infected and Recovered - to study the spatial and time evolutions of dengue disease. The model consists of a partial differential equations system whose numerical solutions were obtained by an explicit high order hybrid scheme that incorporates Fuzzy logic and stochastic process. For the spatial discretization, we used a WENO-5 scheme (Weighted Essentially Non Oscillatory Schemes, fifth order) for regions not smooth of the map and centered finite difference schemes of high order for the regions smooth. Also, a lifting scheme was made to define smoothness or not in the regions. For the time evolution, we have chosen a third order TVD Runge-Kutta (Total Value Diminishing). The uncertain parameters related to the behavior of Aedes aegypti were estimated by the Fuzzy Rule- Based Systems. Such parameters depend of the population habits, mosquito's breeding, blood for the maturation of the eggs and rain events. The rainfall variable has stochastic dependence on the sampled values and for this reason, we chose a Markov chain method (order 2) to estimate the rain. Informations on the behavior of the disease and the conditions for the proliferation of vectors in the region south of city of Campinas were researched for the Health Department, Agronomic Institute and epiGeo (Laboratory for Spatial Analysis of Epidemiological Data) of the Medical Sciences Faculty of UNICAMP. Simulations of various situations were performed to obtain scenarios regarding the spread of the disease, taking into account characteristics of the region studied. Finally, a model of the Takagi-Sugeno type - fuzzy rules, whose outputs are EDP's - was designed to analyze the dengue risk in the region of the domain, from a map of relative risk developed by researchers at the epiGeo
Doutorado
Matematica Aplicada
Doutor em Matemática Aplicada

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41

Mattedi, Alessandro. "Sintese de um controlador hibrido fuzzy-preditivo : aplicação para processos de polimerização." [s.n.], 2003. http://repositorio.unicamp.br/jspui/handle/REPOSIP/267486.

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Orientadores: Rubens Maciel Filho, Wagner Caradori do Amaral
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica
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Resumo: Este trabalho apresenta o desenvolvimento de um controlador preditivo baseado em modelos fuzzy funcionais para o controle de processos de polimerização. Estes processos apresentam um comportamento dinâmico altamente não linear, dificultando assim o desenvolvimento de controladores baseados em modelo convencionais. Para tanto, consideram-se dois casos de estudo da literatura: processo de copolimerização (Congalidis et aI. 1989) e policondensação (Giudici et aI. 1999). O processo de copolimerização apresenta um sistema de reciclo que ocasiona algumas perturbações na entrada do reator do processo. Soluciona-se este problema através de um controlador feedforward apresentado por Congalidis et aI. 1989. Dessa forma, pode-se tratar o reator do processo como um sistema isolado no desenvolvimento do controlador. Para o caso do processo de policondensação, tomando-se como base o modelo estacionário do reator tubular desenvolvido por Giudici et aI. 1999, foi desenvolvido um modelo dinâmico através do seqüenciamento de dez reatores de tanque agitado contínuo (CSTR). Este modelo representa satisfatoriamente o comportamento dinâmico das principais variáveis no desenvolvimento do sistema de controle. Dessa forma, cada um desses dois casos de estudo são considerados como as plantas para aplicação dos sistemas de controle. A escolha de um controlador preditivo como base de desenvolvimento do controlador proposto deve-se ao fato do sucesso na implementação dos controladores preditivos em diversos processos químicos, pois tais controladores, além apresentarem bom desempenho nos controles regulatório e servo, apresentam a possibilidade de inclusão de restrições nas variáveis manipuladas e controladas. Assim sendo, procura-se neste trabalho apresentar o desenvolvimento de uma metodologia de projeto sistemático de controlador preditivo baseando-se em modelos dinâmicos fuzzy funcionais (Takagi e Sugeno 1985) . Estes modelos apresentam uma excelente capacidade de representação de dados dinâmicos. Além disso, apresentam a possibilidade de inclusão de informações qualitativas (ou operacionais) do processo. Tomando-se como base o modelo fuzzy de Takagi e Sugeno, a determinação do modelo (número de regras e parâmetros) é obtida a partir de um conjunto de dados provenientes do processo. O tratamento desses dados para a determinação do modelo fuzzy é realizado por meio de algoritmos matemáticos de agrupamento (clustering) e mínimos quadrados. Os modelos são validados utilizando-se uma parte dos dados de identificação, denominados de dados de teste. Os resultados dessa modelagem para os dois casos de estudo considerados são apresentados com excelentes resultados. Por fim, esses modelos fuzzy são inseridos na metodologia de controle preditivo; emprega-se o controlador preditivo baseado em coeficientes da resposta à entrada degrau (DMC) como base comparativa nos resultados de controle. Nas simulações realizadas os resultados de controle obtidos com o controlador proposto foram superiores aos oriundos do DMC convencional, demonstrando o potencial de utilização do novo algoritmo proposto para o controle de sistemas não lineares
Abstract: This work presents the development of a new predictive controller based in functional fuzzy models for polymerizatíon processes. These processes present a highly non-linear dynamic behavior, thus making difficult the development of controllers based on model as conventional predictive controllers. Two case studies were considered to analyse the performance of the proposed control/er, to know: process of copolimerization (Congalidis et al. 1989) and policondensation (Giudici et ai. 1999). The copolimerization process presents a recycle system that leads to some disturbances in the reactor input of the process. This problem is solved through a feedforward controller, developed by Congalidis et al. 1989. In this way, the reactor can be interpreted as an isolated process system for the control system design. For the polycondensation process, a dynamic model was developed through the sequence of ten reactors CSTR. This model satisfactorily represents the dynamic behavior of the main process variables for the development of the control system. Taking this into consideration the success of some applications of predictive controllers in chemical processes, and also its ability to consider the restrictions on the manipulated and control/ed variables. This type of controller was used as a basis for the development of new control algorithm coupling the fuzzy concepts together with the model predictive controllers. Thus, it explicated in this work the development of a methodology for the design the predictive control/er being based on functional dynamic models fuzzy (Takagi e Sugeno, 1985). These models present an excellent capacity to represent dynamic data. Moreover, they allow the inclusion of qualitative or operational information of the process. The fuzzy model determination (rules number and model parameters) is obtained from the process database. The treatment of these data for the fuzzy model determination is carried out by means of mathematical algorithms of clustering and least squares. The modeling by the fuzzy approach showed to have a good potential for representation. The fuzzy internal models were developed based on the functional dynamic fuzzy representation (Takagi e Sugeno, 1985). The proposed fuzzy based controller were compared to the dynamic matrix controller (DMC) and the obtained results showed that the proposed controller is robust and does not require step test as conventional controllers
Doutorado
Doutor em Engenharia Química

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Moura, Luciano de. "Um algoritmo genetico para otimização multiobjetivo fuzzy." [s.n.], 2002. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260119.

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Orientador : Akebo Yamakami
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Mestrado

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Mahjoub, Mohamed-Said. "Commande floue et application à un processus biologique." Nancy 1, 1993. http://www.theses.fr/1993NAN10018.

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Nous nous sommes proposés, dans ce mémoire, d'étudier la commande floue qui se révèle maintenant comme une alternative intéressante pour la commande de procédés complexes. Le mémoire peut se décomposer en deux parties. La première est essentiellement une étude descriptive de la commande floue. Toutefois, dans cette partie, on décrit une méthode de synthèse des règles de commande qui s'avère intéressante. En fin de cette première partie, on propose une étude comparative du régulateur flou et du régulateur classique. Il ressort que le régulateur flou assure de meilleures performances et tolère mieux les perturbations structurelles. La deuxième partie présente la mise au point d'un régulateur flou pour commander un processus biologique. Les résultats obtenus par l'intermédiaire du contrôleur flou font apparaître de bonnes performances vis-à-vis de la concentration en biomasse et de la productivité, malgré la complexité du milieu biologique

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Onofre, Filho Marc?lio de Paiva. "L?gica Fuzzy para Controle de pH em um Processo Petrol?fero." Universidade Federal do Rio Grande do Norte, 2011. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15361.

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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
This work proposes the design, the performance evaluation and a methodology for tuning the initial MFs parameters of output of a function based Takagi-Sugeno-Kang Fuzzy-PI controller to neutralize the pH in a stirred-tank reactor. The controller is designed to perform pH neutralization of industrial plants, mainly in units found in oil refineries where it is strongly required to mitigate uncertainties and nonlinearities. In addition, it adjusts the changes in pH regulating process, avoiding or reducing the need for retuning to maintain the desired performance. Based on the Hammerstein model, the system emulates a real plant that fits the changes in pH neutralization process of avoiding or reducing the need to retune. The controller performance is evaluated by overshoots, stabilization times, indices Integral of the Absolute Error (IAE) and Integral of the Absolute Value of the Error-weighted Time (ITAE), and using a metric developed by that takes into account both the error information and the control signal. The Fuzzy-PI controller is compared with PI and gain schedule PI controllers previously used in the testing plant, whose results can be found in the literature.
Prop?em-se neste trabalho a concep??o, a avalia??o do desempenho e uma metodologia para sintonia dos par?metros iniciais das fun??es de pertin?ncia de sa?da de um controlador Fuzzy- PI, tipo Takagi-Sugeno-Kang, para o acompanhamento de refer?ncias de pH em um tanque reator com agita??o cont?nua. O controlador ? projetado para executar a neutraliza??o do pH em plantas industriais, principalmente em unidades encontradas em refinarias de petr?leo. O sistema emula, com base no modelo de Hammerstein, uma planta real que se ajusta ?s mudan?as no processo de neutraliza??o do pH, evitando ou reduzindo a necessidade de ressintonia. O desempenho do controlador ? avaliado pelos overshoots, pelos tempos de acomoda??o, pelos ?ndices Integral do valor absoluto do erro (IAE) e Integral do valor absoluto do erro com pondera??o do tempo (ITAE), e atrav?s de um ?ndice desenvolvido por Goodhart que leva em considera??o tanto informa??es do erro quanto do sinal de controle. O controlador Fuzzy-PI ? comparado com controladores PI e PI Escalonado utilizados previamente na planta de teste, cujos resultados est?o dispon?veis na literatura.

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Jesus, Josias Máximo de 1951. "Modelagem matemática de um reator de leito fixo para a síntese de anidrido ftálico e controle utilizando estratégias convencionais e lógica fuzzy." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266578.

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Orientadores: Flávio Vasconcelos da Silva, Pedro Leite de Santana
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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Resumo: Os reatores de leito fixo constituem importantes sistemas da engenharia química, com muitas aplicações em diversos processos industriais, notadamente no campo das indústrias de refino do petróleo e petroquímica. Do ponto de vista da analise de processos químicos, se mostra relevante o estabelecimento de modelos matemáticos representativos que possam ser aplicados ao projeto, a otimização e ao controle desses sistemas. Neste trabalho, apresenta-se a modelagem matemática fenomenológica de um reator de leito fixo para a síntese de anidrido ftalico por oxidação de o - xileno, considerando-se as resistências difusionais mássicas e térmicas externas associadas ao processo de reação catalítica, o qual e realizado com catalisadores não porosos a base de óxidos de vanádio e titânio. O modelo matemático foi implementado como um modulo computacional para simulação do processo nos estados estacionário e dinâmico, a partir do qual se fez um estudo de sensibilidade paramétrica que mostrou à forte influencia da temperatura e da concentração de o - xileno na corrente de alimentação, bem como da temperatura do fluido térmico, no comportamento global do reator. Essas constatações permitiram a proposição de estruturas de controle com o objetivo de regular a concentração do produto na saída do reator e manter uma condição térmica operacional segura. Para o controle do reator foram consideradas duas estruturas: (i) um esquema de controle direto da concentração utilizando um controlador convencional PI e um controlador por lógica fuzzy (Fuzzy-PI) e (ii) um esquema de controle cascata concentração-temperatura utilizando também controladores convencionais PI e Fuzzy-PI nas duas malhas (primaria e secundaria) que compõem a estrutura cascata. Cada controlador teve seu desempenho analisado mediante perturbações do tipo degrau impostas nas condições de alimentação dos reagentes (composição e temperatura). Os resultados evidenciaram um bom desempenho das estruturas de controle cascata, que se mostraram eficientes para controlar a concentração do produto na saída do reator e garantir ao mesmo tempo um regime térmico seguro. Na presença de ruído, os controladores Fuzzy-PI apresentaram um desempenho superior ao dos controladores PI convencionais
Abstract: Fixed bed reactors are important systems in chemical engineering with several applications in many industrial processes, mainly in petroleum refining and petrochemical industries. A comprehensive view on these systems through mathematical modeling is crucial for design, optimization and control issues. This work presents a phenomenological mathematical model for a fixed bed reactor applied to the phthalic anhydride synthesis from o-xylene oxidation using supported non-porous V2O5-TiO2 catalyst. All resistances due to mass and heat flows from the fluid phase to the catalytic particle surface are considered in the mathematical formulation. The mathematical model consisted of a Matlab based computational code for the process simulation both in dynamic and steady-state conditions, providing a parametric sensitivity study that showed the intensive influence of the feed conditions in terms of temperature and o-xylene concentration, as well as the effect of thermal fluid temperature on the global reactor behavior. These observations provided control structures to regulate product concentration leaving the reactor and to avoid the formation of excessive hot spots along the catalytic bed, what is a necessary condition to maintain a safe thermal system operation. It were proposed two structures to control the reactor: (i) a straightforward scheme to control the concentration of phthalic anhydride in the reactor outlet using conventional PI and fuzzy PI controllers, and (ii) another scheme based on a cascade control temperature-concentration also using conventional and fuzzy PI controllers in two (primary and secondary) loops. The behavior of each controller was analyzed by imposing stepwise perturbations in inner o-xylene concentration and in the temperature of the feed. The results showed a good performance of cascade-type controllers, providing the proper regulation of the controlled variable and a safe thermal regime for the system operation. On the other hands, fuzzy logic controllers exhibited better performance for the reactor regulation when measurement noise was taken in account
Doutorado
Sistemas de Processos Quimicos e Informatica
Doutor em Engenharia Química

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46

Wang, Liren. "An approach to neuro-fuzzy feedback control in statistical process control." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/an-approach-to-neurofuzzy-feedback-control-in-statistical-process-control(7d9c736f-e85d-4873-a6bb-9bcea107d371).html.

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It is a difficult challenge to develop a feedback control system for Statistical Process Control (SPC) because there is no effective method that can be used to calculate the accurate magnitude of feedback control actions in traditional SPC. Suitable feedback adjustments are generated from the experiences of process engineers. This drawback means that the SPC technique can not be directly applied in an automatic system. This thesis is concerned with Fuzzy Sets and Fuzzy Logic applied to the uncertainty of relationships between the SPC (early stage) alarms and SPC implementation. Based on a number of experiments of the frequency distribution for shifts of abnormal process averages and human subjective decision, a Fuzzy-SPC control system is developed to generate the magnitude of feedback control actions using fuzzy inference. A simulation study which is written in C++ is designed to implement a Fuzzy-SPC controller with satisfactory results. To further reduce the control errors, a NeuroFuzzy network is employed to build NNFuzzy- SPC system in MATLAB. The advantage of the leaning capability of Neural Networks is used to optimise the parameters of the Fuzzy- X and Fuzzy-J? controllers in order to obtain the ideal consequent membership functions to adapt to the randomness of various processes. Simulation results show that the NN-Fuzzy-SPC control system has high control accuracy and stable repeatability. To further improve the practicability of a NN-Fuzzy-SPC system, a combined forecaster with EWMA chart and digital filter is designed to reduce the NN-Fuzzy-SPC control delay. For the EWMA chart, the smoothing constant 0 is investigated by a number of experiments and optimised in the forecast process. The Finite Impulse Response (FIR) lowpass filter is designed to smooth the input data (signal) fluctuations in order to reduce the forecast errors. An improved NN-Fuzzy-SPC control system which shows high control accuracy and short control delay can be applied in both automatic control and online quality control.

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47

Lima, Nádson Murilo Nascimento. "Desenvolvimento e análise de controle híbrido preditivo por lógica fuzzy de processos de polimerização." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266926.

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Orientador: Rubens Maciel Filho
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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Resumo: A síntese de controladores representa uma importante vertente dos desenvolvimentos atuais no campo da pesquisa acadêmica e industrial. Um controlador bem projetado pode significar sucesso no que se refere aos objetivos de produção, sendo gerados materiais com as especificações desejadas e proporcionando que o sistema opere sob certas restrições, levando em consideração aspectos relativos à operabilidade, segurança e minimização de resíduos. Para tanto, sabe-se que as etapas de modelagem são fundamentais para a delineação de estratégias de controle. Contudo, a obtenção de representações matemáticas precisas e, ao mesmo tempo, aplicáveis para o controle da maioria dos processos de interesse da engenharia química é uma tarefa árdua, devido à presença de comportamentos dinâmicos não lineares e variantes ao longo do tempo e, por vezes, do espaço. Deste modo, busca-se a obtenção de modelos mais simples, porém dotados da imprescindível representatividade inerente aos sistemas de produção, a fim de serem projetadas estruturas de controle adequadas para cada necessidade específica. Esta tese enfoca o desenvolvimento de quatro controladores avançados híbridos preditivos não lineares multivariáveis, baseados em modelos nebulosos (fuzzy) funcionais não lineares multivariáveis, para processos de polimerização. Tais sistemas apresentam dinâmicas altamente complexas e de difícil modelagem matemática, dificultando, assim, a aplicação, com sucesso, de metodologias clássicas de controle ou avançadas baseadas em modelos convencionais. Foram considerados dois casos de estudo para a análise de desempenho das configurações de controle propostas: o processo de copolimerização em solução do metacrilato de metila com o acetato de vinila, e a copolimerização industrial do eteno/1-buteno com catalisador Ziegler-Natta solúvel. Os modelos fenomenológicos de ambos os processos já se encontram descritos na literatura, sendo considerados como plantas virtuais para a geração de dados dinâmicos e implementação dos desenvolvimentos sugeridos. A partir de simulações computacionais, os modelos nebulosos dinâmicos funcionais não lineares multivariáveis foram construídos ¿ os quais demonstraram excelentes capacidades para a predição das saídas dos processos como uma função dos dados dinâmicos de entrada ¿ sendo, posteriormente, acoplados à estrutura de controle preditivo MPC (Model-based Predictive Control). A escolha da metodologia MPC como base para o desenvolvimento das estratégias propostas deve-se ao fato de sua notória aplicabilidade industrial a processos químicos multivariáveis, além de possibilitar a incorporação de restrições operacionais nas variáveis controladas e manipuladas. Por fim, foram comparados os desempenhos entre os controladores híbridos delineados e duas estratégias de controle preditivo bastante difundidas na literatura. Os problemas regulatório e servo foram analisados, observando-se resultados satisfatórios em ambas as condições. Isto demonstra o alto potencial dos algoritmos propostos para o controle multivariável de sistemas não lineares.
Abstract: Controllers design has currently a great importance in the field of açademic and industrial research. A well-projected controller may mean the success regarding the aims of production, and it also provides the production of materials with desirable specifications and it allows that the system operates under specific restrictions, considering aspects related to operability, safety and minimization of residue. Then, it is known that modeling stages are fundamental for delineation of controller strategies. However, the obtaining of precise and applicable mathematical representations to the control of most of relevant process in chemical engineering is a challenging task, because of the presence of nonlinear dynamic behaviors and space-time mutable. Therefore, an effort is done to obtain the simpler models, but provided with the essential representativity inherent to production systems. Thus, suitable control structures could be designed for each specific necessity. This work focus on the development of four multivariable nonlinear predictive hybrid advanced controllers, based on multivariable nonlinear functional fuzzy models, to polymerization processes. Such systems present high complex dynamic and hard mathematical modeling, making difficult to apply classic controllers or advanced control methodologies based on conventional models. Two study cases for the performance analysis of the proposed controllers were considered: the copolymerization process of methyl methacrylate and vinyl acetate, and industrial copolymerization of ethene/1 butene with Ziegler-Natta catalysis. The phenomenologic models of the two processes already are described in relevant literature, and they are considered as virtual plants to create dynamic data and to implement the suggested developments. Based on computational simulations, multivariable nonlinear functional dynamic fuzzy models were made - they demonstrated excellent capacity for outputs prediction from input dynamic data - and they were, subsequently, inserted in the MPC (Model-based Predicitve Control) control structure. The choice of MPC methodology to develop the proposal structures is because of its wellknown industry applicability to multivariable chemical processes, beyond it makes possible the incorporation of restrictions in controlled and manipulated variables. Finally, the performance among outlined hybrid controllers and two predictive control strategies fairly widespread in the literature were compared. The regulatory and servo problems were analyzed and satisfactory results were observed in both conditions. This demonstrates the high potential of proposed algorithms to control multivariable nonlinear systems.
Doutorado
Desenvolvimento de Processos Químicos
Doutor em Engenharia Química

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48

WAKABAYASHI, CLAUDIO. "ANÁLISE E CONTROLE FUZZY DE PROCESSOS: ESTUDO DE CASO EM UM REATOR DE POLIMERIZAÇÃO." Escola Politécnica / Instituto de Matemática, 2007. http://repositorio.ufba.br/ri/handle/ri/21579.

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Este trabalho buscou dois objetivos principais. O primeiro deles consistiu em comprovar a potencialidade da aplicação de controladores PI-fuzzy em reatores de polimerização semi-batelada, com trajetórias de setpoint s (valor de referência) prédefinidas, tendo os parâmetros das funções de pertinência e fatores de escala determinados pelo método de sintonia ótima. O segundo objetivo compreendeu em possibilitar a utilização do programa de simulação em ambiente computacional, como ferramenta de trabalho para P & D de novos produtos. O controle de reatores de polimerização é desafiante devido a vários fatores como a natureza multivariável e não linear, a existência de interações e tempos mortos, a cinética de reação complexa e a ausência de medições diretas das variáveis que caracterizam a estrutura macromolecular do polímero, dentre outros. Para o caso específico de reatores que operam em batelada ou semi-batelada, existem dificuldades adicionais associadas às necessidades de que algumas variáveis de processo obedeçam a trajetórias de setpoints pré-definidas e necessárias para a garantia das especificações do produto final. O sistema em estudo deste trabalho compreende um reator semi-batelada, de escala comercial, para a produção de nylon a partir da ε − caprolactama. O problema de controle consiste em conduzir o sistema reacional ao longo de cada batelada, de modo que as trajetórias dos setpoints da temperatura da massa reacional e da pressão na fase gasosa sejam obedecidas por essas variáveis de processo, caracterizando-se, portanto, como um problema de controle tipicamente servo, com setpoints variáveis. Dois estudos de caso foram testados e analisados. No primeiro caso, óleo quente e óleo frio são ambos disponibilizados e utilizados separadamente para o aquecimento ou resfriamento do meio reacional, respectivamente, conforme a temperatura esteja menor ou maior do que o valor do setpoint em um dado instante. No segundo caso, baseado em situações reais de unidades industriais, é disponibilizado apenas óleo quente ao longo de toda a batelada para o aquecimento da massa reacional. Para cada uma das configurações descritas, o sistema de controle compreendeu duas malhas de controle, sendo uma para o controle da temperatura na massa reacional e outra para o controle da pressão na fase vapor do reator. Para cada um dos estudos, foram comparadas duas estratégias de controle, quais sejam: controladores convencionais com algoritmo PID e controladores PI baseados em lógica fuzzy (nebulosa) (controle PI-fuzzy). Para representar um caso real de um reator de escala comercial, foi adotado um modelo fenomenológico em todos os testes de simulação em malha fechada. O modelo é composto pelas equações da cinética de reação, balanços de massa e de energia, e correlações adicionais para a viscosidade e coeficientes de troca térmica. Foram consideradas as vaporizações da água e da ε − caprolactama presentes na massa reacional, e a variação da viscosidade na massa reacional, assim como o seu efeito sobre as taxas de transferência de massa e de energia. O modelo completo foi ajustado com base em um reator de nylon de escala comercial, já existente, e as condições do processo, assim como as trajetórias de pressão e temperatura, são equivalentes à realidade operacional praticada. Os resultados obtidos comprovam a potencialidade da abordagem do controle PI-fuzzy para reatores de polimerização semi-batelada com setpoint variável. Os ajustes das regras e dos parâmetros das funções de pertinência mostraram-se capazes de produzir um bom desempenho em relação às trajetórias de setpoint especificadas, tendo-se, inclusive, redução de overshoot (sobre-sinal) na temperatura.

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49

Nobre, Farley Simon Mendes. "Projeto e analise de controladores nebulosos e sua aplicação para controle de juntas roboticas." [s.n.], 1997. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261944.

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Orientador: Alvaro Geraldo Badan Palhares
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Este trabalho apresenta uma metodologia para projeto e análise de uma classe de controladores nebulosos e contribui para justificar formalmente as suas vantagens. A metodologia constituída de métodos para especificação das regras linguísticas de controle e para estudos qualitativo e quantitativo que proporcionam análise de estabilidade e especificação de critérios de desempe¿n ANTPOT. H¿o. O método para projeto das regras de controle se baseia nas informações qualitativas contidas na resposta ao degrau de um processo linear subamortecido. Algumas informações que relatam a experiência básica de um especialista são também empregadas. Resultante do método anterior, a análise qualitativa se resume ao estudo qualitativo do conjunto de regras e se baseia em um plano de fase de estados linguísticos. Esta análise permite destacar a influência de cada regra sobre o comportamento e desempe¿n ANTPOT. H¿o do sistema de controle e contribui para modificação das regras. O método para análise quantitativa proposto contribui por apresentar uma maneira alternativa para a descrição matemática do conjunto de regras linguísticas. Os resultados obtidos por este método proporcionam um estudo mais rigoroso sobre o controlador e portanto fornecem novas perspectivas para estudos futuros como a análise da estabilidade. Um procedimento para a especificação de critérios de desempe¿n ANTPOT. H¿o é apresentado. Além disso, é demonstrado que controladores clássicos do tipo PID são funções particulares de controladores nebulosos. Os conceitos propostos neste trabalho são então aplicados no desenvolvimento de um controlador nebuloso para o controle de juntas de um manipulador robótico. Os resultados de simulação enfatizam as vantagens e o melhor desempe¿n ANTPOT. H¿o deste controlador quando comparado a controladores clássicos do tipo PID
Abstract: This work presents a methodology for analysis and design of a class of fuzzy controllers and it contributes to formally justify their advantages. The methodology is constituted by methods for linguistic control rule designing and for qualitative and quantitative studies that provide stability analysis and performance criteria. The approach used for rule base design is based on the qualitative information described by the step response of a linear and underdamped processo Moreover, some metarules that relate the basic experience of a human operator are also used. As a result of the previous approach, the qualitative analysis is concerned with the qualitative study of the rules and it is based in a phase plane of linguistic states. This analysis allows to point out the role of each rule on the control system performance and behaviour and it also contributes to rule modification. The approach proposed for quantitative analysis introduces an alternative way to get the mathematical description of the linguistic rules. As a result, it provides a rigorous study of the fuzzy controller and therefore it open new study perspectives for stability analysis. A procedure for performance criteria design is presented. Besides this, it is demonstrated that classical controllers of PID type are specific functions of fuzzy controllers. The concepts proposed in this thesis are applied to the development of a fuzzy controller to joint control of a robotic manipulator. The simulation results enphasize the advantages and the better performance of this controller when compared with classical controllers of PID type
Mestrado
Mestre em Engenharia Elétrica

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50

Barajas, Leandro G. "Process Control in High-Noise Environments Using A Limited Number Of Measurements." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/7741.

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The topic of this dissertation is the derivation, development, and evaluation of novel hybrid algorithms for process control that use a limited number of measurements and that are suitable to operate in the presence of large amounts of process noise. As an initial step, affine and neural network statistical process models are developed in order to simulate the steady-state system behavior. Such models are vitally important in the evaluation, testing, and improvement of all other process controllers referred to in this work. Afterwards, fuzzy logic controller rules are assimilated into a mathematical characterization of a model that includes the modes and mode transition rules that define a hybrid hierarchical process control. The main processing entity in such framework is a closed-loop control algorithm that performs global and then local optimizations in order to asymptotically reach minimum bias error; this is done while requiring a minimum number of iterations in order to promptly reach a desired operational window. The results of this research are applied to surface mount technology manufacturing-lines yield optimization. This work achieves a practical degree of control over the solder-paste volume deposition in the Stencil Printing Process (SPP). Results show that it is possible to change the operating point of the process by modifying certain machine parameters and even compensate for the difference in height due to change in print direction.

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Dissertations / Theses on the topic 'Fuzzy processes'

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