Academic literature on the topic 'Process control Dynamics Mathematical models'
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Journal articles on the topic "Process control Dynamics Mathematical models"
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Vinayak Kishan Nirmale. "Mathematical Models for Infectious Disease Dynamics and Control Strategies." Communications on Applied Nonlinear Analysis 32, no. 1s (2024): 54–62. http://dx.doi.org/10.52783/cana.v32.2101.
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The study explores mathematical models related to the dynamics of infectious diseases and approaches to manage them. These models are essential resources for comprehending how illnesses spread throughout people and assessing how well control strategies work. The study of infectious disease epidemiology heavily relies on mathematical modeling and analysis. Here, we give a clear overview of the spread of disease, explain how to mathematically model this stochastic process, and show how to utilize this mathematical representation to analyze the emergent dynamics of real-world epidemics. The advancement of mathematical analysis and modeling is crucial to our expanding comprehension of the evolution and ecology of pathogens. This study emphasizes how important mathematical modeling is to improving our knowledge of the dynamics of infectious diseases and how crucial it is to creating efficient management plans to lessen the burden of infectious diseases on public health.
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N. Sridhar, Lakshmi. "Dynamics of Leukemia Models." International Journal of Clinical Case Reports and Reviews 27, no. 03 (2025): 01–11. https://doi.org/10.31579/2690-4861/860.
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Millions of people are affected by leukemia. It is important to understand the progression dynamics of this disease to be able to minimize the damage that is caused by it. This article provides a mathematical framework to develop strategies to control leukemia. Bifurcation analysis is a powerful mathematical tool used to deal with the nonlinear dynamics of any process. Several factors must be considered, and multiple objectives must be met simultaneously. Bifurcation analysis and multiobjective nonlinear model predictive control (MNLMPC) calculations are performed on three leukemia models. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were performed using the optimization language PYOMO in conjunction with the state-of-the-art global optimization solvers IPOPT and BARON. The bifurcation analysis revealed the existence of limit points and branch in the models. The limit and branch points were beneficial because they enabled the multiobjective nonlinear model predictive control calculations to converge to the Utopia point in both problems, which is the most beneficial solution. A combination of bifurcation analysis and multiobjective nonlinear model predictive control for leukemia models is the main contribution of this paper.
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Park, Joseph, George Sugihara, and Gerald Pao. "Control of complex systems with generalized embedding and empirical dynamic modeling." PLOS ONE 19, no. 8 (2024): e0305408. http://dx.doi.org/10.1371/journal.pone.0305408.
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Effective control requires knowledge of the process dynamics to guide the system toward desired states. In many control applications this knowledge is expressed mathematically or through data–driven models, however, as complexity grows obtaining a satisfactory mathematical representation is increasingly difficult. Further, many data–driven approaches consist of abstract internal representations that may have no obvious connection to the underlying dynamics and control, or, require extensive model design and training. Here, we remove these constraints by demonstrating model predictive control from generalized state space embedding of the process dynamics providing a data–driven, explainable method for control of nonlinear, complex systems. Generalized embedding and model predictive control are demonstrated on nonlinear dynamics generated by an agent based model of 1200 interacting agents. The method is generally applicable to any type of controller and dynamic system representable in a state space.
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Prof.Divya, K.Pai. "Mathematical Model of Chemical Reactor Plant and Heat Exchanger Dynamics with Control Design Techniques." Journal of Instrumentation and Innovation Sciences 3, no. 3 (2018): 21–29. https://doi.org/10.5281/zenodo.1480812.
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<em>Mathematical Models are sets of equations that describe a process or System. All engineering programs need Mathematical model before real time implementation of any system, to determine how the process reacts to numerous inputs. Although the bodily gadget is available for experimentation the process usually may be very high priced. Mathematical modeling of a chemical reactor is acquired via the material balance equations and using differential equations, Laplace transforms, Linearizing the Non-Liner equations via Taylor series, obtainingthe transfer function models and finally the design of controller by Internal model principle.Robustness of the control design is verified through model mismatch. Modelling of heat exchanger dynamics is acquired by using the measured statistics and manipulate layout by using Feedforward+remarks control.</em>
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ZAVADSKY, SERGEY V., DMITRI A. OVSYANNIKOV, and SHENG-LUEN CHUNG. "PARAMETRIC OPTIMIZATION METHODS FOR THE TOKAMAK PLASMA CONTROL PROBLEM." International Journal of Modern Physics A 24, no. 05 (2009): 1040–47. http://dx.doi.org/10.1142/s0217751x09044486.
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Mathematical models of the structural parametric optimization of plasma dynamics are discussed. Optimization approach to plasma dynamic is based on the consideration of trajectory ensemble. This ensemble describes transient process in tokamak subject to the initial data and external disturbances. In the framework of this approach the optimization of dynamics of the trajectory ensemble in ITER tokamak is given. The trajectories of this ensemble are perturbed at the initial point set and the set of external disturbances.
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Jannatun, Irana Ira, Shahidul Islam Md., C. Misra J, and Kamrujjaman Md. "Mathematical Modelling of the Dynamics of Tumor Growth and its Optimal Control." International Journal of Ground Sediment & Water 2020, no. 11 (2020): 659–79. https://doi.org/10.5281/zenodo.4275629.
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<strong>Abstract: </strong>In the last few decades, the dynamics of tumor cells and their growths are presented via clinical, experimental, and theoretical approaches, which leads to the development of the new idea of multiple cancer therapies to control and reduce the death rate for earlier detection. In this paper, we discussed the dynamics of tumor cell growth and its treatment process. We analyzed some simple mathematical models and generalized the study to understand the growth of tumor cells. The main proposed model is a system of ordinary differential equations which combines interactions among natural killer cells, dendritic cells and cytotoxic CD8+T cells. The model is solved numerically to explain how the tumor cells spread and become more dangerous as well as the treatment process of cancer. It is also studied that how the cell behaves in the presence of different therapy and drugs. The optimal control of chemotherapy has been discussed. It has also been explained how much the model is effective in reducing tumor cells over time. Finally, a couple of spatially distributed models are discussed for tumor cell growth. <strong>Keywords: </strong>Mathematical models; tumor growth; chemotherapy; di usion; optimal control © International Journal of Ground Sediment & Water, ©Sun Jichao. The website is http://ijgsw.comze.com/
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N Sridhar, Lakshmi. "Dynamics of Chemoimmunotherapy Models." Journal of Innovative Clinical Trials and Case Reports 1, no. 1 (2025): 1–10. https://doi.org/10.63721/25jctc0106.
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Chemoimmunotherapy is chemotherapy combined with immunotherapy. Chemotherapy uses different drugs to kill or slow the growth of cancer cells; immunotherapy uses treatments to stimulate or restore the ability of the immune system to fight cancer. Both chemotherapy and immunotherapy are highly nonlinear processes that several factors affect. The two treatments together would be very highly nonlinear. It is necessary to understand and control this combined treatment. Bifurcation analysis is a powerful mathematical tool used to deal with the nonlinear dynamics of any process. Several factors must be considered, and multiple objectives must be met simultaneously. Bifurcation analysis and multiobjective nonlinear model predictive control (MNLMPC) calculations are performed on two chemoimmunotherapy models. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were performed using the optimization language PYOMO in conjunction with the state-of-the-art global optimization solvers IPOPT and BARON.. The bifurcation analysis revealed branch and limit points in the two models. The branch and limit points were beneficial because they enabled the multiobjective nonlinear model predictive control calculations to converge to the Utopia point in both the problems, which is the best solution
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Barrios Sánchez, Jorge Manuel, Roberto Baeza Serrato, and Marco Bianchetti. "Design and Development of a Mathematical Model for an Industrial Process, in a System Dynamics Environment." Applied Sciences 12, no. 19 (2022): 9855. http://dx.doi.org/10.3390/app12199855.
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This research proposes a methodology based on control engineering, transforming the simulation model of system dynamics into a mathematical model expressed as a system transfer function. The differential equations of a time domain present in the Forrester diagram are transformed into a frequency domain based on the Laplace transform. The conventional control engineering technique is used to present and reduce the dynamic system in a block diagram as a mechanism for determining the structure of the system. The direct path equation and the feedback equation are determined to obtain mathematical models that explain the trajectory of the behavior of each state variable through a transfer function in response to the different inputs of the system. The research proposal is based on presenting an alternative of analytical validation for more robust decision-making to systems dynamics models, based on the explanation of the system structure through a transfer function and its analysis of stability and external controllability for the system dynamics model under study. The results are visually analyzed in a root diagram.
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Kochuk, Serhii, Dinh Dong Nguyen, Artem Nikitin, and Rafael Trujillo Torres. "Identification of UAV model parameters from flight and computer experiment data." Aerospace technic and technology, no. 6 (November 29, 2021): 12–22. http://dx.doi.org/10.32620/aktt.2021.6.02.
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The object of research in the article is various well-known approaches and methods of structural and parametric identification of dynamic controlled objects - unmanned aerial vehicles (UAVs). The subject of the research is the parameters of linear and nonlinear mathematical models of spatial and isolated movements, describing the dynamics and aerodynamic properties of the UAV and obtained both from the results of flight experiments and using computer object-oriented programs for 3-D UAV models. The goal is to obtain mathematical models of UAV flight dynamics in the form of differential equations or transfer functions, check them for reliability and the possibility of using them in problems of synthesis of algorithms for automatic control systems of UAVs. Tasks to be solved: evaluation of the analytical (parametric), direct (transient), as well as the identification method using the 3-D model of the control object. Methods used structural and parametric identification of dynamic objects; the determination of static and dynamic characteristics of mathematical models by the type of their transient process; the System Identification Toolbox package of the MatLab environment, the Flow Simulation subsystem of the SolidWorks software and the X-Plane software environment. The experimental parameters of UAV flights, as well as the results of modeling in three-dimensional environments, are the initial data for the identification of mathematical models. The following results were obtained: the possibility of analytical and computer identification of mathematical models by highly noisy parameters of the UAV flight was shown; the mathematical models of UAVs obtained after identification is reliable and adequately reproduce the dynamics of a real object. A comparative analysis of the considered UAV identification methods is conducted, their performance and efficiency are confirmed. Conclusions. The scientific novelty of the result obtained is as follows: good convergence, reliability and the possibility of using the considered identification methods for obtaining mathematical models of dynamic objects to synthesize algorithms for automatic control systems of UAVs is shown.
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Jia, Xiao Yi, Yu Tian Lin, Hui Bin Lin, Ling Gao, Jian Qun Lin, and Jian Qiang Lin. "Mathematical Modeling of CSTR Bioreactor Control for Production of Recombinant Protein." Advanced Materials Research 894 (February 2014): 311–15. http://dx.doi.org/10.4028/www.scientific.net/amr.894.311.
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Fermentation process using recombinant strain for production of recombinant protein is widely used in commercialization of the biotechnologies. The continuous stirred tank reactor (CSTR) is a typical microbial cultivation method, has the major advantage of high productivity. Mathematical modeling and simulation is useful for analysis and optimization of the CSTR fermentation process. Most of the mathematical models developed for CSTR are black box models without information of the intracellular dynamics and regulations. In this research, a mathematical model is built based on gene regulation for recombinant protein production using CSTR, and simulation is made using this model.
Dissertations / Theses on the topic "Process control Dynamics Mathematical models"
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Beh, Christopher Chun Keong. "Vacuum swing adsorption process for oxygen enrichment : a study into the dynamics, modelling and control." Monash University, Dept. of Chemical Engineering, 2003. http://arrow.monash.edu.au/hdl/1959.1/9533.
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Cuadros, Bohórquez José Fernando. "Estratégia alternativa de otimização em duas camadas de uma unidade de craqueamento catalítico-FCC : implementação de algoritmos genéticos e metodologia híbrida de otimização." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266651.
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Orientadores: Rubens Maciel Filho, Delba Nisi Cosme Melo<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química<br>Made available in DSpace on 2018-08-21T11:54:20Z (GMT). No. of bitstreams: 1
CuadrosBohorquez_JoseFernando_D.pdf: 6686167 bytes, checksum: 1899259fbe648f651c06a5f9ca3e2c29 (MD5)
Previous issue date: 2012<br>Resumo: Esta pesquisa teve por finalidade o desenvolvimento de uma metodologia de otimização em duas camadas. A otimização preliminar foi baseada na técnica de planejamento de experimentos junto com a metodologia por superfície de resposta com a finalidade de identificar uma possível região de busca do ponto de operação ótimo, o qual foi obtido através da implementação de métodos híbridos de otimização desenvolvidos mediante associação do modelo determinístico de otimização por programação quadrática sucessiva (SQP) com a técnica dos algoritmos genéticos (GA) no modelo do processo de craqueamento catalítico fluidizado- FCC. Este processo é caracterizado por ser um sistema heterogêneo e não isotérmico, cuja modelagem detalhada engloba as equações de balanço de massa e energia das partículas do catalisador, como também para a fase líquida e gasosa, sendo um dos casos de estudo para a aplicação da metodologia de otimização desenvolvida. Como caso de estudo principal foi considerado o modelo do conversor do processo de FCC desenvolvido por Moro e Odloak (1995). Mediante a metodologia de otimização do processo baseado no uso do modelo determinístico da planta, foram definidas estratégias e políticas operacionais para a operação da unidade de FCC em estudo. Procurou-se alto nível de desempenho e segurança operacional, através da integração das etapas de operação, otimização e controle no contexto de otimização em tempo real do processo. As otimizações foram divididas em quatro etapas: 1) Análises preliminares dos fatores e das variáveis de resposta do modelo do conversor foram realizadas usando a técnica de planejamento de experimentos, com o objetivo de compreender a interação entre elas, assim como obter modelos simplificados das variáveis de resposta. A geração dos modelos simplificados é devido à necessidade de ganho no tempo computacional permitindo o conhecimento prévio da região de otimização já que em casos industriais pode não ser possível representar adequadamente o processo por modelos determinísticos; 2) Otimização usando algoritmos genéticos implementados no modelo simplificado da conversão, e no modelo determinístico com e sem restrições; 3) Otimização considerando o método de otimização SQP implementado no modelo simplificado da conversão e no modelo determinístico com restrições; e 4) otimização multi-objetivo do conversor usando x a técnica dos algoritmos genéticos, com o objetivo de maximizar a conversão, assim como a minimização da vazão dos gases de combustão, especificamente o monóxido de carbono (CO). Das otimizações foram obtidos ganhos em torno de 8% na conversão quando comparado com os valores de conversão sem otimização. Finalmente, foi realizada a integração do modelo do processo, com a otimização e o controle, dando como resultado a otimização em tempo real do conversor de FCC. A variável de otimização foi a conversão e, através da implementação do controle por matriz dinâmica com restrições (QDMC), aplicando a metodologia de controle inferencial. As variáveis escolhidas como variável controlada foi a temperatura de reação e como variável manipulada foi a temperatura da alimentação, com perturbações na vazão de alimentação do ar de regeneração. Valores de conversão da ordem de 88% foram atingidos para o esquema de otimização em tempo real, o método de otimização por algoritmo genético apresentou um desempenho satisfatório, com tempos e cargas computacionais razoáveis para implementação desta metodologia, em nível industrial<br>Abstract: The purpose of this research was the develop of an optimization methodology. Experimental design technique along with a hybrid optimization methodology obtained by association of sequential quadratic programming (SQP) with genetic algorithms (GA), were implemented in the model of a Fluid Catalytic Cracking process (FCC) developed by Moro and Odloak (1995). This process is described for a heterogeneous, non isothermal system, in which a detailed modeling comprises mass and energy balance equations for catalyst particles, liquid and gaseous phases that makes this process model, a case study for implementing the optimization methodology developed. The process optimization methodology developed; along with the deterministic model of the plant were applied to define operational strategies and policies for the operation of the FCC unit studied aiming to obtain high performance and operational safety, through the integration of control, operation and optimization stages in the context of real-time optimization (RTO) process. Optimizations were divided into four stages: 1) Preliminary analysis of factors and response variables of converter modeling were performed using experimental design technique aiming to understand the factors and response variables interaction, as well as to obtain response variables simplified models to be used as objective function in optimization stages, 2) a optimization using genetic algorithms was implemented in the simplified conversion model, in the deterministic modeling and the deterministic model considering factors restrictions, 3) a optimization considering a local search methodology like sequential quadratic programming (SPQ) was implemented in the simplified model of process conversion and also it was consided the deterministic model with restrictions. As initial estimative, the optimum factor values obtained with genetic algorithms were considered as well as two random points in the search space, and 4) a multi objective optimization considering genetic algorithms technique in order to maximize conversion and minimize combustion gases emissions, specifically carbon monoxide was developed. Applying this optimization methodology was obtained increments of around 8% in the feed conversion when compared with conversion values without optimization. xii Finally, it was developed the integration of optimization, control and process modeling giving as result the real time optimization (RTO) of FCC converter. The variable maximized by genetic algorithms was the feed conversion and the control technique implemented was based on the matrix named (QDMC) in conjunction with inferential control methodology. It was considered as controlled variable the reaction temperature adjusting the feed temperature (manipulated variable), for disturbances in the feed flow of the regeneration air. Feed conversion in the order of 88% were achieved for the real time optimization scheme considered, in which, the genetic algorithm showed an excellent performance in reasonable computational times and computational loads for implementation at industrial level<br>Doutorado<br>Desenvolvimento de Processos Químicos<br>Doutor em Engenharia Química
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Roberts, Gwendolyn Rose 1963. "A comparison of multiple univariate and multivariate geometric moving average control charts." Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276779.
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This study utilizes a Monte Carlo simulation to examine the performance of multivariate geometric moving average control chart schemes for controlling the mean of a multivariate normal process. The study compares the performance of the proposed method with a multivariate Shewhart chart, a multiple univariate cumulative sum (CUSUM) control chart, a multivariate CUSUM control chart and a multiple univariate geometric moving average control chart.
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Chang, Min-Yung. "Active vibration control of composite structures." Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-09162005-115021/.
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Sinangil, Mehmet Selcuk. "Modeling and control on an industrial polymerization process." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/10150.
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Du, Plessis Sydney Charles. "Investigation of process parameters and development of a mathematical model for the purposes of control design and implementation for a wastewater treatment process." Thesis, Cape Peninsula University of Technology, 2009. http://hdl.handle.net/20.500.11838/1194.
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Thesis (DTech (Electrical Engineering))--Cape Peninsula University of Technology, 2009<br>The problem for effective and optimal control of wastewater treabnent plants is very
important recently because of the increased requirements to the qualitY of the effluent
The activated sludge process is a type of wastewater process characterized with
complex dynamics and because of this proper control design and implementation
strategies are necessary and important for its operation. Since the early seventies, when
a major leap forward was made by the widespread introduction of dissolved oxygen
control, little progress has been made.
The most critical phase in the solution of any control problem is the modelling stage. The
primary building block of any modem control exercise is to construct and identify a
model for the system to be controlled. The existing full Activated Sludge Model 1
(ASM1) and especially University of Cape Town (UCT) models of the biological
processes in the activated sludge process, called in the thesis biological models, are
highly complex because they are characterised with a lot of variables that are difficult to
be measured on-line, complex dependencies and nonlinear interconnections between
the biological variables, many kinetic parameters that are difficult to be determined, .
different time scales for the process dynamics.
The project considers reduction of the impact of the complexity of the process model
over the methods for control design and proposes a solution to the above difficulties by
development of a reduced model with small number of variables, but still with the same
characteristics as the original full model for the purposes of real time.
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Tai, Hoi-lun Allen, and 戴凱倫. "Quantitative analysis in monitoring and improvement of industrial systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B4394193X.
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Ruiz, Orlando E. "Numerical analysis of the dropwise evaporation process." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/18879.
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Hecker, Rogelio Lorenzo. "Power feedback control in cylindrical grinding process." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/16619.
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Thompson, Christopher David. "An analytical and experimental investigation of respiratory dynamics using P/D control and carbon dioxide feedback." Thesis, Virginia Tech, 1988. http://hdl.handle.net/10919/43059.
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This thesis addresses the problem of defining the control law for human respiration.
Seven different drivers have been identified as possibly having an input to the
respiratory controller. These seven represent a combination of feedforward and feedback
inputs arising from neural and humoral mechanisms.
Using the assumption that carbon dioxide concentrations in the arterial blood have the
strongest effect, a control equation with proportional and derivative components based on
this driver was evaluated. The methodology for the evaluation was to create a model of
the respiratory system incorporating the P/D controller, obtain experimental data of one
test subject's respiratory response to exercise, then compare model generated output with
experimental data, and adjust the parameters in the control equation to yield optimal
model performance.
The usual practice of testing controller performance has been to apply single step loads
to a model and evaluate its response. A multi-step protocol was used here to provide a
better, more generalized test of controller performance. This thesis may represent the
first documented use of an approach of this type for evaluating respiratory controller
performance.
Application of a multi-step protocol revealed a non-linear controller was needed to keep
pace with system changes. Respiratory system operation was effectively managed using
a controller of the form:
VENTILATION = F(dCO2/dT,Q) + F(CO2,Q) + CONSTANT.<br>Master of Science
Books on the topic "Process control Dynamics Mathematical models"
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Volgin, L. N. Optimalʹnoe diskretnoe upravlenie dinamicheskimi sistemami. "Nauka," Glav. red. fiziko-matematicheskoĭ lit-ry, 1986.
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W, Barnett Michael, Stenstrom Michael K, and Andrews John F, eds. Dynamics and control of wastewater systems. 2nd ed. Technomic Publishing Co., Inc., 1998.
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Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. National Aeronautics and Space Administration, 1997.
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Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. National Aeronautics and Space Administration, 1997.
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Kecman, V. State-space models of lumped and distributed systems. Springer-Verlag, 1988.
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Denkena, Berend. Process Machine Interactions: Predicition and Manipulation of Interactions between Manufacturing Processes and Machine Tool Structures. Springer Berlin Heidelberg, 2013.
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Beyreuther, Roland. Dynamics of fibre formation and processing: Modelling and application in fibre and textile industry. Springer, 2007.
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Kolpakov, Vasiliy. Economic and mathematical and econometric modeling: Computer workshop. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/24417.
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The textbook presents mathematical research methods and models of economic objects and processes designed for the analysis and prediction of economic factors and develop control solutions as in the deterministic conditions, and in conditions of some uncertainty, and dynamics. Each Chapter of the book consists of a theoretical framework, discussed in detail several examples and tasks for independent work. As workbench simulation uses standard office the program Excel and Mathcad. Tutorial focused on independent performance of students individual tasks on disciplines "Economic-mathematical methods" and "Econometrics".
 Meets the requirements of Federal state educational standard of higher education of the last generation.
 The publication is intended for students and postgraduate students in economic disciplines. It can also be useful as they perform final qualifying works. The book will be useful for practitioners engaged in the analysis of the current financial and economic condition and future development of firms and businesses.
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Rao, Vadrevu Sree Hari. Dynamic models and control of biological systems. Springer, 2009.
Find full textBook chapters on the topic "Process control Dynamics Mathematical models"
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Pletnev, L. V., N. I. Gamayunov, and V. M. Zamyatin. "Computer Simulation of Evaporation Process into the Vacuum." In Mathematical Models of Non-Linear Excitations, Transfer, Dynamics, and Control in Condensed Systems and Other Media. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4799-0_13.
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Arinstein, Arkadii E. "Phenomenological Description for Process of Multiple Disintegration* of Solids Under Intensive Stress Action Such as Compression & Shear." In Mathematical Models of Non-Linear Excitations, Transfer, Dynamics, and Control in Condensed Systems and Other Media. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4799-0_26.
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Rao, Ming, and Haiming Qiu. "Mathematical Models and Transfer Functions." In Process Control Engineering. Routledge, 2022. http://dx.doi.org/10.1201/9780203741931-2.
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Albi, Giacomo, Emiliano Cristiani, Lorenzo Pareschi, and Daniele Peri. "Mathematical Models and Methods for Crowd Dynamics Control." In Crowd Dynamics, Volume 2. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50450-2_8.
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Raia, Gaetano, Gianluca Rigano, David Vincenzoni, and Maurizio Martina. "A Case Study on Formal Equivalence Verification Between a C/C++ Model and Its RTL Design." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71177-0_23.
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AbstractIn the field of communication system products, most datapath Digital Signal Processing algorithms are initially developed at a high-level in MATLAB® or C/C++. Subsequently, design engineers use these models as a reference for implementing Register Transfer Level designs. The conventional approach to verify their equivalence involves extensive Universal Verification Methodology dynamic simulations, which can last for months and require significant verification efforts. However, some elusive errors might still occur because it is infeasible to explore all input combinations with this method. On the other hand, Formal Equivalence Verification aims to verify that a Register Transfer Level design is functionally equivalent to the reference high-level C/C++ model across all possible legal states. With recent advancements in formal solver technology, Formal Equivalence Verification provides a distinct benefit by using mathematical methods to ensure that the Register Transfer Level (timed) matches the original high-level C/C++ model (untimed). This drastically reduces the verification time and ensures the exhaustive coverage of the design state space. This paper presents an in-depth exploration of complex Finite State Machine with datapath verification, specifically focusing on Multiplier-Accumulator, Tone Generator, and Automatic Gain Control, by employing the formal equivalence methodology. Although these signal processing blocks were previously verified throughout Universal Verification Methodology dynamic simulations, Formal Equivalence Verification was able to identify hard-to-find bugs in just a few weeks by utilizing the new workflow, thereby streamlining the verification process.
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Roy, Priti Kumar. "Insight of Delay Dynamics." In Mathematical Models for Therapeutic Approaches to Control HIV Disease Transmission. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-852-6_5.
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Ledzewicz, Urszula, and Heinz Schättler. "An Optimal Control Approach to Cancer Chemotherapy with Tumor–Immune System Interactions." In Mathematical Models of Tumor-Immune System Dynamics. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1793-8_7.
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Sun, Jiayue, Shun Xu, Yang Liu, and Huaguang Zhang. "Neural Networks-Based Immune Optimization Regulation Using Adaptive Dynamic Programming." In Adaptive Dynamic Programming. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5929-7_2.
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AbstractThis chapter investigates optimal regulation scheme between tumor and immune cells based on ADP approach. The therapeutic goal is to inhibit the growth of tumor cells to allowable injury degree, and maximize the number of immune cells in the meantime. The reliable controller is derived through the ADP approach to make the number of cells achieve the specific ideal states. Firstly, the main objective is to weaken the negative effect caused by chemotherapy and immunotherapy, which means that minimal dose of chemotherapeutic and immunotherapeutic drugs can be operational in the treatment process. Secondly, according to nonlinear dynamical mathematical model of tumor cells, chemotherapy and immunotherapeutic drugs can act as powerful regulatory measures, which is a closed-loop control behavior. Finally, states of the system and critic weight errors are proved to be ultimately uniformly bounded with the appropriate optimization control strategy and the simulation results are shown to demonstrate effectiveness of the cybernetics methodology.
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Austin, Daren J. "Mathematical Models in the ICU: Dynamics, Infection Control and Antibiotic Resistance." In Infection Control in the ICU Environment. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0781-9_19.
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Leonov, G. A., and N. V. Kondrat’eva. "Electromechanical and Mathematical Models of Salient-Pole Synchronous Motors." In Advanced Dynamics and Model-Based Control of Structures and Machines. Springer Vienna, 2011. http://dx.doi.org/10.1007/978-3-7091-0797-3_17.
Full textConference papers on the topic "Process control Dynamics Mathematical models"
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Zambrano, Consuelo Del Pilar Vega, Nikolaos A. Diangelakis, and Vassilis M. Charitopoulos. "Closed-Loop Data-Driven Model Predictive Control For A Wet Granulation Process Of Continuous Pharmaceutical Tablet Production." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.192802.
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In 2023, the International Council for Harmonisation (ICH) guideline for the development, implementation, and lifecycle management of pharmaceutical continuous manufacturing (PCM), was implemented in Europe. It promotes quality-by-design (QbD) and quality by control (QbC) strategies as well as the appropriate use of mathematical modelling. This development urges a harmonizing understanding across academia and industry for adoption of interpretable models instead of black-box models for advanced control strategies such as model predictive control (MPC), especially when applied in Good Manufacturing Practice (GMP) regulated areas. To this end, we first propose a comprehensive model development using Dynamic Mode Decomposition with Control (DMDc)to represent complex dynamics in a lower-dimensional space, disambiguating between underlying dynamics and actuation effects. Using data from a digital twin of PCM, our model demonstrates low computational complexity while effectively capturing nonlinear dynamics with significant improvements observed in the performance metrics. Additionally, we demonstrate enhanced uncertainty propagation performance when compared to state-space models obtained with N4SID and Sparse Identification of Nonlinear Dynamical systems with control algorithms. Finally, we develop a closed-loop workflow that seamlessly connects data exchanges between Python (DMDc), GAMS (MPC optimisation) & gPROMS to evaluate the controller performance with setpoint tracking and disturbance rejection studies achieving high accuracy in real-time monitoring and control of granule size. This study offers a novel, interpretable control strategy for PCM. The results demonstrate the potential for real-time release testing, reduced reliance on end-product testing, and improved process control in the pharmaceutical industry.
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Liu, Rui, Xi Chen, and Antonios Armaou. "Accelerated Process Modelling for Light-Mediated Controlled Radical Polymerization." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.128107.
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Mathematical modelling and simulation are pivotal components in process systems engineering. Focusing on polymerization process systems, identifying microscopic properties of polymers is highly sought after for advancing kinetic comprehension and facilitating industrial applications. Among various computational methods predicting polymeric properties microscopically, kinetic Monte Carlo (kMC) offers a stochastic framework to characterize individual polymer chains and track dynamic system evolution, providing mechanistic insights into complex polymerization kinetics. In this study, an accurately accelerated Superbasin-aided kMC model is developed for enhancing the kinetic understanding of the advanced photo-iniferter RAFT (PI-RAFT) polymerization. The contribution is twofold, presenting advancements in both the mathematical modelling techniques for complex dynamic process systems and the mechanistic understanding of photo-induced polymerizations. Leveraging the increased computational performance and the gained kinetic understanding, batch blending is examined as a viable approach to produce polymers with desired microscopic properties. This work bridges stochastic molecular simulations with scalable polymerization processes, laying groundwork for multiscale modelling in polymer engineering, and also the foundation for process design, control, and optimization.
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Belda, Kvetoslav, and Oliver Rovny. "Predictive control of 5 DOF robot arm of autonomous mobile robotic system motion control employing mathematical model of the robot arm dynamics." In 2017 21st International Conference on Process Control (PC). IEEE, 2017. http://dx.doi.org/10.1109/pc.2017.7976237.
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Willkomm, Johannes, Matthias Wahler, and Jürgen Weber. "Process-Adapted Control to Maximize Dynamics of Speed- and Displacement-Variable Pumps." In ASME/BATH 2014 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fpmc2014-7821.
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Within the last years, speed-variable pump drives were investigated in numerous applications. In combination with a variable displacement pump, the volume flow and the drive speed can be decoupled. In this paper the resulting degree of freedom will be used to maximize the dynamics of so called “Hydro-Gear” drive systems. By means of a novel process-adapted control concept, a dynamic model of all drive components will be transformed to a mathematical optimization problem. It will be shown that the optimum use of the two control variables depends on the actual system and process conditions. In comparison to known control strategies of speed- and displacement-variable pumps the new process-adapted approach can exemplary double the maximum volume flow gradient while reducing the necessary motor speed by over 40% at the same time.
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Yeh, Hung-Ping, Yuan-Che Chien, and Jia-Ying Tu. "Identification of Hysteresis Dynamics Using Duffing-Like and Bouc-Wen Models." In ASME 2016 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/isps2016-9588.
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Two methods of identifying hysteresis behavior for dynamic systems is introduced and compared in this paper, which are Duffing-like and Bouc-Wen models. Many structural and mechanical systems including the unwanted hysteresis dynamics, such as rubber isolators, magnetoresistive sensors, and magnetorheological dampers, complicate the design, analysis, control, and application work. Hysteresis can be said a general property of dynamic systems, and many mathematical models have been proposed in the material, mechanical, and civil engineering literature, in order to identify the hysteresis dynamics. However, some of the models used in literature, including discontinuous and piecewise terms, are not able to duplicate the hysteresis dynamics accurately. In addition, the complicated and coupling effects of the parameters renders the identification work inefficient and inaccurate. As a result, these models still present difficulty in the process of mechanical design, parameter analysis, and control implementation. The innovative Duffing-like model has been proposed, which is developed based on the original Duffing equation and is a second-order nonlinear ordinary differential equation. Parameters are decoupled in the model, such that the linear and nonlinear effects of the parameters on the hysteresis curve can be investigated. By adjusting the parameters individually, the stiffness, damping, and yielding dynamics are observed. By understanding the influence of each parameter, it is able to advance a standard process for identifying the hysteresis behavior for different dynamic systems. Therefore, in order to reflect the advantages of Duffing-like model, this paper compares the Duffing-like and Bouc-Wen models. Bouc-Wen model was proposed in literature since 1997, and a variety of modification, identification, and application work based on the Bouc-Wen model haven been discussed. However, the Bouc-Wen model is essentially a discontinuous equation and the parameters do not have explicit physical meaning. Therefore, the efficacy of control application using the Bouc-Wen model is very limited. In order to show can compare the Duffing-like and Bouc-Wen models, the parameter identification procedure and numerical simulation results are presented in the paper. In addition, the hysteresis system of a 400-N magnetorheological damper is used as an example for the identification and simulation study. The simulation results show that the Bouc-Wen model, which contains discontinuous functions and difficult-to-track parameters, is not able to provide a reliable basis for parameter tuning. In contract, the Duffing-like model allows the physical meaning of the parameters to be defined in a systematic manner.
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Vásquez, Rafael E., Norha L. Posada, Fabio Castrillón, and David Giraldo. "Development of a Laboratory Equipment for Dynamic Systems and Process Control Education." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-38924.
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This paper addresses the development of an equipment to teach control engineering fundamentals. The design requirements were determined by users that perform academic, research and industrial training tasks in the area of dynamic systems and process control. Such requirements include: industrial instrumentation; measurement of controlled and manipulated variables, and disturbances; process reconfigurability; different control technologies; several control strategies; appropriate materials for visualization; and compact shape to optimize lab space. The selected process is a tank system that allows one to choose among several dynamic behaviors: first, second, and third order, linear and nonlinear behavior, and dead time; the mathematical model that represents the dynamics of the system is presented. A traditional 3-stage design methodology that includes conceptual, basic and detailed design was followed. The developed equipment allows the user to select from three different technological alternatives to control the system: a PLC, an industrial controller, and a computer. With such flexibility, several control strategies can be implemented: feedback, feedforward, PID, LQG, nonlinear control (gain scheduling, sliding mode, etc.), fuzzy logic, neural networks, dynamic matrix control, etc. The developed system is being used to teach undergrad courses, grad courses, and industrial training. Additionally, the equipment is useful in research projects where grad students and researches can implement and test several advanced control techniques.
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Celik, Ismail B., Asaf Varol, Coskun Bayrak, and Jagannath R. Nanduri. "A One Dimensional Mathematical Model for Urodynamics." In ASME/JSME 2007 5th Joint Fluids Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/fedsm2007-37647.
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Millions of people in the world suffer from urinary incontinence and overactive bladder with the major causes for the symptoms being stress, urge, overflow and functional incontinence. For a more effective treatment of these ailments, a detailed understanding of the urinary flow dynamics is required. This challenging task is not easy to achieve due to the complexity of the problem and the lack of tools to study the underlying mechanisms of the urination process. Theoretical models can help find a better solution for the various disorders of the lower urinary tract, including urinary incontinence, through simulating the interaction between various components involved in the continence mechanism. Using a lumped parameter analysis, a one-dimensional, transient mathematical model was built to simulate a complete cycle of filling and voiding of the bladder. Both the voluntary and involuntary contraction of the bladder walls is modeled along with the transient response of both the internal and external sphincters which dynamically control the urination process. The model also includes the effects signals from the bladder outlet (urethral sphincter, pelvic floor muscles and fascia), the muscles involved in evacuation of the urinary bladder (detrusor muscle) as well as the abdominal wall musculature. The necessary geometrical parameters of the urodynamics model were obtained from the 3D visualization data based on the visible human project. Preliminary results show good agreement with the experimental results found in the literature. The current model could be used as a diagnostic tool for detecting incontinence and simulating possible scenarios for the circumstances leading to incontinence.
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Wu, Shuihua, Kazem Kazerounian, Zhongxue Gan, and Yunquan Sun. "A Free Form Robotic Grinding System: A Mathematical Model and an Actual System." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-29038.
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This paper presents a robotic grinding system for work pieces with free-form geometries. A mathematical model representing the kinematics and dynamics of the system is built in the first part of the paper. Offline programming, calibration and a novel method for robotic error compensation are utilized to accurately generate the robotic grinding path. Models of the robot, the grinder and the grinding process are integrated to obtain the dynamics of the system. In the second part of the paper, an actual system is presented, with experiments done to verify the kinematic accuracy of the system. A controller based on the target tracking theory is designed to extend the system’s capability of material removal control, the effectiveness of which is shown by the simulation results. In the future, the target-tracking control strategy will be integrated with the actual system to develop a robotic grinding system capable of material removal control for free-form work pieces.
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Samuel, Robello, Fedor Baldenko, and Dmitry Baldenko. "Mud Motor PDM Dynamics: A Control Model for Automation." In IADC/SPE International Drilling Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/208789-ms.
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Abstract Automating drilling operations using modern technology with intelligent control systems is prioritized with respect to drilling process development. The process becomes more complex when a mud motor is used because of the dynamic properties of the control objects, which are interconnected from the surface feed mechanism to the bit. Development of a model that better predicts the dynamic condition when a mud motor is used is discussed. The dynamic characteristics of a positive displacement motor (PDM) are considered as a single system by coupling the hydromechanical processes. This includes, transient pressure, drillstring dynamics, kinematics of the PDM rotor, the mud pump and dampener, dynamic characteristics of the bit, and internal and external system disturbances. The mathematical model includes the hydraulic and mechanical subsystems as well as the relationships of these subsystems. The subsystems include the equations of the processes in their constituent links (i.e., drillstring, hydraulic line, mud pump, downhole motor, bit, and bit feed mechanism). The nonlinear system of differential equations are solved using numerical methods with appropriate boundary conditions in a two-way loop for regulating the load on the bit and flowrate. The study shows that the transient behavior of the mud motor must be accounted for when automating the drilling process. It has been observed that an instantaneous change in the load results in the transition of the hydraulic motor to a new steady-state mode gradually (for tens of seconds) and is influenced by the transient pressures in the string. The transient process (torsional vibrations) in the PDM occurs until the flow rate stabilizes at the top of motor. A similar hydromechanical effect, if the PDM does not have a sufficient torque reserve, can lead to deceleration of the PDM and might require correction of the weight on bit (WOB) and flow rate. The study also showed that the effect of pulsation dampener resulting in an uneven flow leads to uneven rotation of the mud motor shaft, even if a constant load on the bit is maintained. This study revealed that evaluation of the influence of the deviation of one of the operating parameters on the behavior of the dynamic system is important (Tokhonov, 2019). The choice of optimal control algorithms depends on the accuracy of determining the transfer functions, in particular, with respect to the change in the WOB. Further transfer functions derived can be used to control the surface parameters for drilling.
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Simeunovic´, G., P. Zi´tek, and D. Lj Debeljkovic´. "Differential-Discrete Mathematical Model of the Recuperative Counter-Flow Heat Exchanger." In 16th International Conference on Nuclear Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/icone16-48256.
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The heat exchangers are always constructive elements of nuclear power plants and their dynamics is very important since they introduce a significant time lag in control actions. That’s why their dynamics cannot be ignored. On the basis of the accepted and critically clarified assumptions, a linearized mathematical model of the recuperative counter flow heat exchanger has been derived, taking into account the wall dynamics. The model is based on the fundamental law of energy conservation, covers all heat accumulation storages in the process, and leads to the set of partial differential equations, which solution is not possible in closed form. In order to overcome the solution difficulties, the procedure of differential discrete modelling is applied, leading to the set of ordinary differential equations of a rather high order. These equations are transformed into the state space form suitable for the Matlab environment. The experimental investigations were made, putting some different typical input variables. Specifying the input temperatures and output variables, under the constant initial conditions, the step transient responses have been simulated and presented in graphic form for the particular positions in the heat exchanger in order to compare these results with the experimental data collected from the real process.
Reports on the topic "Process control Dynamics Mathematical models"
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Osypova, Nataliia V., and Volodimir I. Tatochenko. Improving the learning environment for future mathematics teachers with the use application of the dynamic mathematics system GeoGebra AR. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4628.
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Immersive technologies and, in particular, augmented reality (AR) are rapidly changing the sphere of education, especially in the field of science, technology, engineering, arts and mathematics. High- quality professional training of a future mathematics teacher who is able to meet the challenges that permeate all sides, the realities of the globalizing information society, presupposes reliance on a highly effective learning environment. The purpose of the research is to transform the traditional educational environment for training future mathematics teachers with the use of the GeoGebra AR dynamic mathematics system, the introduction of cloud technologies into the educational process. The educational potential of GeoGebra AR in the system of professional training of future mathematics teachers is analyzed in the paper. Effective and practical tools for teaching mathematics based on GeoGebra AR using interactive models and videos for mixed and distance learning of students are provided. The advantages of the GeoGebra AR dynamic mathematics system are highlighted. The use of new technologies for the creation of didactic innovative resources that improve the process of teaching and learning mathematics is presented on the example of an educational and methodological task, the purpose of which is to create didactic material on the topic “Sections of polyhedra”. While solving it, future teachers of mathematics should develop the following constituent elements: video materials; test tasks for self-control; dynamic models of sections of polyhedra; video instructions for constructing sections of polyhedra and for solving basic problems in the GeoGebra AR system. The article highlights the main characteristics of the proposed educational environment for training future mathematics teachers using the GeoGebra AR dynamic mathematics system: interdisciplinarity, polyprofessionalism, dynamism, multicomponent.
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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.
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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capabilities of deep learning architectures. Additionally, it explores biologically plausible learning mechanisms such as Hebbian learning, spike-timing-dependent plasticity (STDP), and neuromodulation, drawing insights from neuroscience and cognitive computing. The role of spiking neural networks (SNNs) and neuromorphic computing in low-power AI and real-time decision-making is also analyzed, with applications in robotics, brain-computer interfaces, edge AI, and cognitive computing. Case studies highlight the industrial adoption of biologically inspired AI, focusing on adaptive neural controllers, neuromorphic vision, and memory-based architectures. This research underscores the importance of integrating theoretical learning principles with biologically motivated AI models to develop more interpretable, generalizable, and scalable intelligent systems. Keywords Neural computation, learning theory, expressivity, deep learning, recurrent neural networks, spiking neural networks, biologically inspired AI, infinite-width networks, kernel methods, attractor networks, synaptic plasticity, STDP, neuromodulation, cognitive computing, dynamical systems, function approximation, generalization, AI stability, neuromorphic computing, robotics, brain-computer interfaces, edge AI, biologically plausible learning.
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Perdigão, Rui A. P. Beyond Quantum Security with Emerging Pathways in Information Physics and Complexity. Synergistic Manifolds, 2022. http://dx.doi.org/10.46337/220602.
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Information security and associated vulnerabilities have long been a pressing challenge, from the fundamental scientific backstage to the frontline across the most diverse sectors of society. At the tip of the iceberg of this problem, the citizens immediately feel that the reservation of privacy and the degradation of the quality and security of the information and communication on which they depend for the day-to-day activities, already of crucial relevance, are at stake. Naturally though, the challenges do not end there. There is a whole infrastructure for storing information, processing and communication, whose security and reliability depend on key sectors gearing modern society – such as emergency communication systems (medical, civil and environmental protection, among others), transportation and geographic information, the financial communications systems at the backbone of day-to-day transactions, the information and telecommunications systems in general. And crucially the entire defence ecosystem that in essence is a stalwart in preventing our civilisation to self-annihilate in full fulfilment of the second principle of thermodynamics. The relevance of the problem further encompasses the preservation of crucial values such as the right to information, security and integrity of democratic processes, internal administration, justice, defence and sovereignty, ranging from the well-being of the citizen to the security of the nation and beyond. In the present communication, we take a look at how to scientifically and technically empower society to address these challenges, with the hope and pragmatism enabled by our emerging pathways in information physics and complexity. Edging beyond classical and quantum frontiers and their vulnerabilities to unveil new principles, methodologies and technologies at the core of the next generation system dynamic intelligence and security. To illustrate the concepts and tools, rather than going down the road of engineered systems that we can ultimately control, we take aim at the bewildering complexity of nature, deciphering new secrets in the mathematical codex underlying its complex coevolutionary phenomena that so heavily impact our lives, and ultimately bringing out novel insights, methods and technologies that propel information physics and security beyond quantum frontiers.
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Messina, Francesca, Ioannis Georgiou, Melissa Baustian, et al. Real-time forecasting model development work plan. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47599.
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The objective of the Lowermost Mississippi River Management Program is to move the nation toward more holistic management of the lower reaches of the Mississippi River through the development and use of a science-based decision-making framework. There has been substantial investment in the last decade to develop multidimensional numerical models to evaluate the Lowermost Mississippi River (LMMR) hydrodynamics, sediment transport, and salinity dynamics. The focus of this work plan is to leverage the existing scientific knowledge and models to improve holistic management of the LMMR. Specifically, this work plan proposes the development of a real-time forecasting (RTF) system for water, sediment, and selected nutrients in the LMMR. The RTF system will help inform and guide the decision-making process for operating flood-control and sediment-diversion structures. This work plan describes the primary components of the RTF system and their interactions. The work plan includes descriptions of the existing tools and numerical models that could be leveraged to develop this system together with a brief inventory of existing real-time data that could be used to validate the RTF system. A description of the tasks that would be required to develop and set up the RTF system is included together with an associated timeline.
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Castellano, Mike J., Abraham G. Shaviv, Raphael Linker, and Matt Liebman. Improving nitrogen availability indicators by emphasizing correlations between gross nitrogen mineralization and the quality and quantity of labile soil organic matter fractions. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597926.bard.
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A major goal in Israeli and U.S. agroecosystems is to maximize nitrogen availability to crops while minimizing nitrogen losses to air and water resources. This goal has presented a significant challenge to global agronomists and scientists because crops require large inputs of nitrogen (N) fertilizer to maximize yield, but N fertilizers are easily lost to surrounding ecosystems where they contribute to water pollution and greenhouse gas concentrations. Determination of the optimum N fertilizer input is complex because the amount of N produced from soil organic matter varies with time, space and management. Indicators of soil N availability may help to guide requirements for N fertilizer inputs and are increasingly viewed as indicators of soil health To address these challenges and improve N availability indicators, project 4550 “Improving nitrogen availability indicators by emphasizing correlations between gross nitrogen mineralization and the quality and quantity of labile organic matter fractions” addressed the following objectives: Link the quantity and quality of labile soil organic matter fractions to indicators of soil fertility and environmental quality including: i) laboratory potential net N mineralization ii) in situ gross N mineralization iii) in situ N accumulation on ion exchange resins iv) crop uptake of N from mineralized soil organic matter sources (non-fertilizer N), and v) soil nitrate pool size. Evaluate and compare the potential for hot water extractable organic matter (HWEOM) and particulate organic matter quantity and quality to characterize soil N dynamics in biophysically variable Israeli and U.S. agroecosystems that are managed with different N fertility sources. Ultimately, we sought to determine if nitrogen availability indicators are the same for i) gross vs. potential net N mineralization processes, ii) diverse agroecosystems (Israel vs. US) and, iii) management strategies (organic vs. inorganic N fertility sources). Nitrogen availability indicators significantly differed for gross vs. potential N mineralization processes. These results highlight that different mechanisms control each process. Although most research on N availability indicators focuses on potential net N mineralization, new research highlights that gross N mineralization may better reflect plant N availability. Results from this project identify the use of ion exchange resin (IERs) beads as a potential technical advance to improve N mineralization assays and predictors of N availability. The IERs mimic the rhizosphere by protecting mineralized N from loss and immobilization. As a result, the IERs may save time and money by providing a measurement of N mineralization that is more similar to the costly and time consuming measurement of gross N mineralization. In further search of more accurate and cost-effective predictors of N dynamics, Excitation- Emission Matrix (EEM) spectroscopy analysis of HWEOM solution has the potential to provide reliable indicators for changes in HWEOM over time. These results demonstrated that conventional methods of labile soil organic matter quantity (HWEOM) coupled with new analyses (EEM) may be used to obtain more detailed information about N dynamics. Across Israeli and US soils with organic and inorganic based N fertility sources, multiple linear regression models were developed to predict gross and potential N mineralization. The use of N availability indicators is increasing as they are incorporated into soil health assessments and agroecosystem models that guide N inputs. Results from this project suggest that some soil variables can universally predict these important ecosystem process across diverse soils, climate and agronomic management. BARD Report - Project4550 Page 2 of 249