Relevant bibliographies by topics / Process control

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Process control'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Process control"

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Shams, Shakila, M. Abdus Sobhan, Farruk Ahmed, and Ali Shihab Sabbir. "Advance Control & Process Measurement." International Journal of Science and Research (IJSR) 11, no. 7 (July 5, 2022): 1081–84. http://dx.doi.org/10.21275/sr22706074755.

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Abd EL-hamid, Ahmed S., Ahmed E. hussian, and Aly M. Radwan. "Generic Model Control of Biomethanation Process." International Journal of Engineering Research 4, no. 6 (June 1, 2015): 282–85. http://dx.doi.org/10.17950/ijer/v4s6/602.

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Kabdullinov, A. M., B. R. Nussupbekov, A. K. Khassennov, M. Stoev, and M. B. Karagaeva. "Automated control system for casting process." Bulletin of the Karaganda University. "Physics" Series 87, no. 3 (September 29, 2017): 65–70. http://dx.doi.org/10.31489/2017phys3/65-70.

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Rivera, Diego Y. "Designing Soft Controls for Process Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 1 (July 2000): 120–23. http://dx.doi.org/10.1177/154193120004400132.

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Honma, Chuichi. "Process control." JAPAN TAPPI JOURNAL 41, no. 10 (1987): 958–69. http://dx.doi.org/10.2524/jtappij.41.958.

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Lorraine, Hilary, and N. Domenico Bruzzone. "Process control." Technological Forecasting and Social Change 41, no. 1 (February 1992): 57–69. http://dx.doi.org/10.1016/0040-1625(92)90016-m.

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Montgomery, Douglas C., J. Bert Keats, George C. Runger, and William S. Messina. "Integrating Statistical Process Control and Engineering Process Control." Journal of Quality Technology 26, no. 2 (April 1994): 79–87. http://dx.doi.org/10.1080/00224065.1994.11979508.

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Jogi, Bhushan S., Lekrajsing R. Gour, and Nikhil Turkar. "Process Improvement Using Statistical Process Control in a Small Scale Industry." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 1885–92. http://dx.doi.org/10.31142/ijtsrd17144.

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Hussain, Mohamed Abdullah, Mumtaz Mohamed Ali El-Mukhtar, and Wrya Mohamed Ali. "Secure Client- Server Based Remote Process Control." Journal of Zankoy Sulaimani - Part A 11, no. 1 (August 12, 2007): 67–80. http://dx.doi.org/10.17656/jzs.10182.

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Machado, I. C., A. C. de Araujo, and A. E. C. Peres. "Optimizing advanced process control systems: Process and control audits." Mining, Metallurgy & Exploration 20, no. 3 (August 2003): 135–39. http://dx.doi.org/10.1007/bf03403145.

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Dissertations / Theses on the topic "Process control"

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Jones, Benjamin J. "Cheese process control." Thesis, University of Canterbury. Chemical and Process Engineering, 1999. http://hdl.handle.net/10092/6842.

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The cheese making process at Anchor Products Hautapu was having difficulty reaching the now outdated New Zealand Dairy Board uniformity targets. Even though there is no longer a direct financial benefit in reaching these targets consistently, there is the benefit of being able to show the customer that the product which they are receiving is as consistent as possible. This project was carried out by systematically investigating each section of the cheese making process, looking for variations that were likely to affect the final product. Where variations were found methods for eliminating them or minimising their effect on the final product were developed. The largest source of variation was found to be caused by fluctuations in the curd depths on the belts of the Alf-O-Matic cheddaring machine. Overlapping the ends of consecutive cheese making tanks as well as the re-calculations of the pump out flow rates have been proposed to remedy this problem. Where the curd is drawn off from the end of the Alf-O-Matic cheddaring machine was also found to be causing variation in the product due to particle stratification affecting the salt levels of blocks that were being produced. Recommendations for methods to reduce the level of stratification have been suggested including using a capacitance probe to control the curd level. Further variations were occurring within the cheese making tanks with cutting and stirring speeds differing from tank to tank. The tanks were also being flushed with cold water causing moisture spikes in the product. Both of these problems have been eliminated by changes to the PLC program. Small improvements have been seen in the process with the changes that have already been carried out. Large improvements are expected if the rest of the recommendations are implemented. The largest improvements should be seen with the realisation of an overlapped pump out system.
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Manchanda, Sunil. "Nonlinear process control." Thesis, University of Newcastle Upon Tyne, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336269.

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Pemajayantha, V., of Western Sydney Nepean University, Faculty of Commerce, and School of Quantitative Methods and Mathematical Sciences. "Multivariate process control with input-output relationships for optimal process control." THESIS_FCOM_QMS_Pemajayantha_V.xml, 1998. http://handle.uws.edu.au:8081/1959.7/552.

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This thesis examines the existing theories and applications of Multivariate Statistical Process Control, outlines areas of difficulty and proposes a new technique of multivariate process control chart with input-output relationship for optimal process control. The process control techniques developed up to the present time focused on the fast detection of out-of-control signals, and achieved considerable success in that respect. The techniques reported on multivariate process control thus far include extensions of univariate process control charts to their multivariate counterparts, ranging from classical Shewharts charts to modern Cumulative Sum Process Control charts. Alternative approaches in this area include Principal Component Approach, Partial Regression approach, Baysian modelling and sequential tests on detection of change points. Although each method has its own limitation, these new developments have significantly contributed to the achievement of a constant high quality of products and services. The techniques of process control are yet incomplete. They require continuous attention, as production and service technologies are being continuously developed.In particular, the level of automation, re-engineering of production processes and ever demanding economic optimality of technology demand the re-engineering of statistical process control. The CFM chart developed in this thesis would open the door to this area of science and lays a critical foundation for future research<br>Doctor of Philosophy (PhD)
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Pemajayantha, V. "Multivariate process control with input-output relationships for optimal process control." Thesis, View thesis, 1998. http://handle.uws.edu.au:8081/1959.7/552.

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This thesis examines the existing theories and applications of Multivariate Statistical Process Control, outlines areas of difficulty and proposes a new technique of multivariate process control chart with input-output relationship for optimal process control. The process control techniques developed up to the present time focused on the fast detection of out-of-control signals, and achieved considerable success in that respect. The techniques reported on multivariate process control thus far include extensions of univariate process control charts to their multivariate counterparts, ranging from classical Shewharts charts to modern Cumulative Sum Process Control charts. Alternative approaches in this area include Principal Component Approach, Partial Regression approach, Baysian modelling and sequential tests on detection of change points. Although each method has its own limitation, these new developments have significantly contributed to the achievement of a constant high quality of products and services. The techniques of process control are yet incomplete. They require continuous attention, as production and service technologies are being continuously developed.In particular, the level of automation, re-engineering of production processes and ever demanding economic optimality of technology demand the re-engineering of statistical process control. The CFM chart developed in this thesis would open the door to this area of science and lays a critical foundation for future research
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Pemajayantha, V. "Multivariate process control with input-output relationships for optimal process control /." View thesis, 1998. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030908.115857/index.html.

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Thesis (PhD (Philosophy))-- University of Western Sydney, Nepean, 1998.<br>"Thesis submitted for the fulfilment of the requirement of Doctor of Philosophy in quantitative methods, School of Quantitative Methods and Business Operations, Faculty of Commerce, University of Western Sydney, Nepean" Bibliography : p 233-257.
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Modlitba, Martin. "Řízení technologického procesu systémem Control Web 7." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-240824.

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The master work topic is dealt with process control system Control Web 7. After the introductory preface is followed by the specification and description of controlled process (the specification). The following is verbally with the assistance of the flowchart describes the concept of the whole system under manufacture. Then there is outlined how to proceed with an application programming and is described also Control Web 7. The last part of the master work is devoted to final inspection using the program module VisionLab compared with competing vision systems, which is an addition to the family of Control Web.
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Sabri, Dina O. "Process control using an optomux control board." Virtual Press, 1987. http://liblink.bsu.edu/uhtbin/catkey/484759.

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In this thesis process control concepts were used to develop software that could be adapted to a real world situation. The software was used to control a simple temperature regulating experiment. This experiment was used to demonstrate the use of OPTOMUX analog and digital input/output devices in controlling a process. The goal of this experiment was to use the input/output devices in controlling the temperature of the box within specified tolerances for a designated period of time. To accomplish optimal use of equipment and optimal control, a mathematical model was derived to predict the behavior of the process under control. The pattern observed while the temperature was increasing toward room temperature closely resembled an exponential function. For temperatures above room temperatures the curve then approximated a square root function. The pattern followed when decreasing the temperature-was exponential throughout. The time required to collect all the significant data in the case of increasing the temperature was two hours. In the case of decreasing temperature, one hour. Beyond these time limits the temperature remained essentially constant. The maximum temperature value that could be reached was six degrees above room temperature and the minimum two degrees below room temperature.
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Sislian, Rodrigo. "Estudo de sistema de limpeza CIP usando identificação de sistemas." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266701.

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Orientadores: Flávio Vasconcelos da Silva, Rubens Gedraite<br>Dissertação (mestrado) - Universidade Estaual de Campinas, Faculdade de Engenharia Química<br>Made available in DSpace on 2018-08-21T17:13:53Z (GMT). No. of bitstreams: 1 Sislian_Rodrigo_M.pdf: 5208613 bytes, checksum: 98e7f9c71ee28be7ab6cc618b41ea7bd (MD5) Previous issue date: 2012<br>Resumo: A presença de resíduos em superfícies mal higienizadas pode aumentar a incidência de microrganismos e ocasionar problemas operacionais nos equipamentos de processo. A identificação da dinâmica do processo pode contribuir para a melhoria da sua eficiência, racionalizando o uso de água e energia empregada nas operações de enxágue. Atualmente, a maioria dos processos de limpeza CIP é conduzida com base em procedimentos padronizados considerando a experiência dos operadores de processo no que tange ao tempo de funcionamento do ciclo. Este trabalho aborda, em um primeiro momento, a implantação da instrumentação e do sistema de controle necessários para monitorar e controlar o processo e, na sequência, o levantamento experimental do comportamento do sistema a estímulos na vazão e/ou temperatura de operação do processo. Para tal foi utilizado um trocador de calor instalado em planta piloto com o objetivo de relacionar, ao longo do tempo, a variação da alcalinidade (pH) da água empregada para remoção do detergente alcalino utilizado no processo de limpeza com a temperatura e vazão da mesma. Neste trabalho a planta utilizada possui dimensões de uma planta semi-industrial típica; tal característica possibilita que se considerem as dinâmicas e fenômenos encontrados em plantas reais, obtendo-se resultados de grande interesse prático. Os equipamentos utilizados (sensores, interfaces e atuadores) são padrão de mercado, adequadamente combinados e instalados de maneira a permitir o estudo de vários aspectos relacionados às etapas de um processo CIP. Testes foram realizados na planta partindo dos parâmetros de sintonia calculados pelo método ITAE por Rovira para o controle de vazão do fluido de processo. Os valores finais dos parâmetros PID que apresentaram o melhor resultado e foram utilizados na planta foram: Kp= 2,68 e Ti= 0,101 s. Devido à diferença na dinâmica para aumento e redução da temperatura para o controle de temperatura do fluido de processo, partiu-se dos parâmetros de sintonia calculados pelo Método CHR sem sobre valor. Os valores finais dos parâmetros PID que apresentaram o melhor resultado nos testes e foram utilizados na planta foram: para o aumento da temperatura Kp = 6,394, Ti = 3,640 s e Td=0,621 s, e para a diminuição da temperatura, foi utilizado o controlador proporcional com o parâmetro Kp = 0,08. A cinética da remoção foi avaliada a partir da variação do pH medido. Foram identificadas as dinâmicas da planta para diferentes condições operacionais que mostram que os parâmetros dinâmicos do sistema são fortemente influenciados pelas vazões e pouco afetados pelas temperaturas utilizadas, com maior contribuição para valores mais elevados de vazão (16 L.min-1), onde há menor consumo de água. Apesar de a identificação aproximada apresentar um modelo (com erro) que representa a resposta do processo, motivou-se o uso de uma metodologia de identificação mais refinada com o objetivo de comparação. Esta foi obtida através de modelos baseados no sistema de Inferência Fuzzy Neuro-Adaptativo (ANFIS) através do aplicativo Simulink/MATLAB'MARCA REGISTRADA'. Os resultados obtidos com os modelos foram validados por comparação com os dados experimentais. Para este processo duas entradas (a saída atrasada em uma amostragem - pH [k-1] - e a vazão atual - F[k]) e uma saída (o pH atual - pH[k]) para o treinamento da rede, mostraram ser mais adequadas para modelar a resposta da dinâmica do pH na etapa de enxague estudada. O erro médio foi de 0,011 quando comparados os dados experimentais coletados com o modelo obtido (tanto com o uso do algoritmo Grid partition quanto com o algoritmo Subtractive Clustering e com o uso de 3 ou 5 funções de pertinência do tipo triangular)<br>Abstract: The presence of residues in poorly cleaned surfaces may increase the micro-organisms incidence and cause operational problems in process equipments. The process dynamics identification can contribute to improve efficiency, rationalizing the energy and water used in rinsing operations. Nowadays, most of CIP cleaning process is conducted based on standard procedures considering the process operators' experience regarding the operating time cycle. This paper discusses, at first, the instrumentation and control system implementation required to monitor and control the process and, after that, the experimental tests to analyze the system behavior to stimuli in flow and/or process operating temperature. For that it was used a heat exchanger installed in a pilot plant in order to relate, over time, the water alkalinity (pH) variation used to remove alkaline detergent used in the cleaning process with the temperature and flow rate of the same. The plant used in this study has the typical dimensions of a semi-industrial plant; this characteristic makes it possible to consider the dynamic and phenomena found in real plants, obtaining results of great practical interest. It was used industry standard equipments (sensors, actuators and interfaces) properly combined and installed so as to allow the study of various aspects related to the CIP process stages. Tests were done in the plant starting with the tuning parameters calculated by the ITAE by Rovira method to control the process fluid flow. The final PID parameters values that presented the best results and were used in the plant were: Kp = 2.68 and Ti = 0.101 s. Due to the difference in dynamics for increasing and decreasing temperature to control the process fluid temperature, It was started from the tuning parameters calculated by the CHR method without over value. The final PID parameter values that had the best results in the plant and were used were: for the temperature increase Kp = 6,394, Ti = 3,640 s and Td=0,621 s, and for decreasing temperature it was used a proportional controller with the parameter Kp = 0.08. The kinetics removal was evaluated starting from the measured pH variation. The plant dynamics were identified for different operating conditions which shows that the system's dynamic parameters are strongly influenced by the flow and little affected by the temperatures used, with a greater contribution for higher flow levels (16 L.min-1), where there is less water consumption. Although the approximate identification provide a suitable model (with error) that represents the process response, there was a motivation for the use of a more refined identification methodology with the objective of comparing. It was obtained by Adaptive Neuro-Fuzzy Inference System (ANFIS) model-based via Simulink/MATLAB'TRADE MARK' software. The results obtained with those models were validated by comparison with the experimental data. For this process two inputs (the output delayed by one sample - pH [k-1] - and the current flow - F[k]) and one output (the current pH - pH [k]) to the network training, revealed to be more appropriate to model the pH dynamics response in the rinse step studied. The average error was 0,011 when comparing the experimental collected data with the obtained model (either using the Grid Partition algorithm and the Subtractive Clustering algorithm and using 3 or 5 triangular membership functions)<br>Mestrado<br>Sistemas de Processos Quimicos e Informatica<br>Mestre em Engenharia Química
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Stendal, Ludvig. "Learning about process control." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Social Sciences and Technology Management, 2003. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-195.

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<p>The research site has been the Södra Cell Tofte pulp mill. The main focus in this thesis is how to learn about process control. The need for research on this theme is given implicitly in the foundation and construction of the INPRO programme. Norwegian engineering education is discipline oriented, and the INPRO programme aimed at integrating the three disciplines engineering cybernetics, chemical engineering, and organisation and work life science in a single PhD programme. One goal was to produce knowledge of modern production in chemical process plants based on socio-technical thinking.</p><p>In the introduction I outline how my research questions have been developed and the need for doing research in the field of improving process understanding in a continuous process plant.</p><p>This thesis provides answer to the three questions:</p><p>1. What are the learning systems for workers in a process plant?</p><p>2. What is the implication for learning of different socio-technical structures?</p><p>3. How can learning be further improved for workers in the process industry?</p><p>In order to answer these questions and to provide a background for why these questions are important to Tofte, I describe and analyse the case plant Södra Cell Tofte. I find it necessary to make this part rather extensive in order for the reader to understand the context under which Tofte has been developing its learning arenas or learning systems. I use a sociotechnical framework in doing this. I want to introduce and use this framework as I regard it as useful for one of my purposes with this work: Assisting the production unit at Tofte to improve learning. I go through technological improvements that have been carried out from 1980 onwards, and one major organisational change that has taken place. The downsizing and reorganisation that took place in 1992 is of importance as well as the organisational development effort named “Employeeship” that took place in 1996. I had a leave from the INPRO project for almost a year following and evaluating this particular project. The situation at Tofte in 1994 was lack of good learning systems, and after a major reorganisation in 1992 the organisation defined a need for better responsibility distribution and co-operation. </p><p>Chapters 3 and 4 present and discuss theories in order to give a broader background for the research issues in this thesis. In Chapter three I discuss features and characteristics of a continuous process plant as these have consequences on how knowledge and skills can be developed and why process understanding is a necessity. I present socio-technical system thinking (STS) as one way of regarding organisation and management of a process plant, and I further discuss why I find this approach appropriate for providing learning primarily at the shop floor level as an integrated part of daily production.</p><p>In Chapter four I argue that knowledge and skills in production are becoming increasingly important in highly automated remotely controlled process plants and develop a theory of “process understanding”. Process understanding is defined as the ability to predict what is going to happen. In order to predict what is going to happen with a system one firstly need to define the system boundaries. This system can then be regarded as a mental model. One must know and analyse input variables (know where and how to get relevant process information), and by this anticipate, like in a mental simulation, what will happen with the parameters within a defined time period. Different possible options may be mentally tested including what will happen if no corrective actions are taken. An ability to predict what is going to happen with the product, to process equipment or any other process variable, has to be developed and refined in order to operate a process plant optimally. Theories state that a variety of knowledge and skills are required and that some skills can be acquired only through years of experience. The “knowing why” within a process plant also has to be strengthen in order to develop a better process understanding, but as an addition to the experience based “knowing how”.</p><p>Models of learning regarding the demands given by the production systems in order to develop such process understanding are presented and discussed. These are conventional methods, experiential (problem based) learning, and collective learning. The experiential learning model is discussed and what may inhibit learning from experiences to take place in a plant. I have defined the concept “learning arena” and regarded each shift in a control room as a main learning arena since this is the place where theory meets practice. It is further discussed that practice will differ between shifts within same control room due to different mental models of the process. In various learning arenas, different communities-ofpractice must be joined in order to make more shared mental models with the intention to align different practices.</p><p>In Chapter five the research methods used to explore the research questions, and thus to bring forth theories about gaining better process control, are reviewed. I have been inspired by action research methods in order to answer my research questions and to contribute in a necessary change process where development and use of learning arenas have been central. I have been more or less active in these arenas and played back experiences and theories in order to further develop the arenas. Besides participations in learning arenas, methods have been interviews, observations, and written documentations. </p><p>Chapter six is the case description of two different kinds of learning systems at Tofte: Operator Training (OT) - Operator based development and execution of education/ training and Operations Workshop (OW) - Problem based learning aiming at better production practice. I have provided background for the two cases as an answer to the educational challenges Tofte had in 1996 and not least to differences in operational practice between shifts. I describe background, characteristics, and development from what I term different learning arenas where the learning about process control will take place. I have also discussed in what ways these two learning systems can be regarded as learning arenas and briefly the kind of learning that can take place in each arena. In two Operations workshops I provide more details in order to show some strengths of the method.</p><p>In Chapter seven I provide answers to the three research questions outlined in the introduction and further refined in Chapter 4.5. I analyse how different learning types such as individual, experiential, and collective are covered within different learning arenas and how OT and OW meet the requirements for good learning systems in continuous process plants. Further I analyse how tasks regarding education and training are better distributed in a shift and daytime organisation with the two learning systems, and further how learning is integrated with working and thus process operators’ knowledge and skills are better utilised. When I analyse the implication of learning of two different socio-technical structures, I also regard how managers are better enabled to become facilitators for learning. The two arenas have been well established at Tofte, but to a varying degree in the different departments. When regarding Operator training it is still too early to conclude on its impact on results in the pulp mill. However, two Operations workshops have made positive contributions and demonstrated the potential of the method. The strengths of the methods are the collective learning that place in cogenerated learning arenas. In Operator training this strengthens the master-apprentice method, and in Operations workshops it gives a shared understanding and direction for further tasks in process control. Finally, based on the analysis of the first two questions I discuss how learning for workers can be further improved.</p><p>In Chapter eighth I conclude on my theoretical contribution and arguments for further research in the actual fields. Finally, based on my findings I will recommend organisational choices on future actions. STS provides frames and directions for learning to take place within groups along the production line. It is however not the scope of the STS paradigm to provide theories of what constitutes knowledge in operations of a plant. And the STS theories are not developed in order to cover more specifically models for how learning within and across semi-autonomous units and organisational levels may take place. Thus the main contribution of this thesis is learning theories based on two different kinds of learning models as means to develop process understanding.</p>
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Ibrahim, Kamarul Asri. "Active statistical process control." Thesis, University of Newcastle Upon Tyne, 1989. http://hdl.handle.net/10443/407.

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Most Statistical Process Control (SPC) research has focused on the development of charting techniques for process monitoring. Unfortunately, little attention has been paid to the importance of bringing the process in control automatically via these charting techniques. This thesis shows that by drawing upon concepts from Automatic Process Control (APC), it is possible to devise schemes whereby the process is monitored and automatically controlled via SPC procedures. It is shown that Partial Correlation Analysis (PCorrA) or Principal Component Analysis (PCA) can be used to determine the variables that have to be monitored and manipulated as well as the corresponding control laws. We call this proposed procedure Active SPC and the capabilities of various strategies that arise are demonstrated by application to a simulated reaction process. Reactor product concentration was controlled using different manipulated input configurations e.g. manipulating all input variables, manipulating only two input variables, and manipulating only a single input variable. The last two manipulating schemes consider the cases when all input variables can be measured on-line but not all can be manipulated on-line. Different types of control charts are also tested with the new Active SPC method e.g. Shewhart chart with action limits; Shewhart chart with action and warning limits for individual observations, and lastly the Exponentially Weighted Moving Average control chart. The effects of calculating control limits on-line to accommodate possible changes in process characteristics were also studied. The results indicate that the use of the Exponentially Weighted Moving Average control chart, with limits calculated using Partial Correlations, showed the best promise for further development. It is also shown that this particular combination could provide better performance than the common Proportional Integral (PI) controller when manipulations incur costs.
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Books on the topic "Process control"

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Authority, Engineering Training, ed. Process instrumentation: Process control. Watford: EnTra, 1994.

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Niu, Steve S., and Deyun Xiao. Process Control. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97067-3.

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King, Myke. Process Control. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470976562.

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Corriou, Jean-Pierre. Process Control. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3848-8.

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King, Myke. Process Control. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119157779.

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Corriou, Jean-Pierre. Process Control. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61143-3.

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Kauko, Leiviskä, ed. Process control. Helsinki: Fapet Oy, 1998.

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Oakland, John S. Statistical process control. 6th ed. Burlington, MA: Butterworth-Heinemann, 2008.

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Erickson, Kelvin T. Plantwide process control. New York: Wiley, 1999.

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Rengaswamy, Raghunathan, Babji Srinivasan, and Nirav Pravinbhai Bhatt. Process Control Fundamentals. First edition. | Boca Raton : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780367433437.

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Book chapters on the topic "Process control"

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Corriou, Jean-Pierre. "Digital Control." In Process Control, 507–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_13.

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Corriou, Jean-Pierre. "Optimal Control." In Process Control, 539–609. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_14.

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Corriou, Jean-Pierre. "Digital Control." In Process Control, 463–92. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3848-8_13.

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Corriou, Jean-Pierre. "Optimal Control." In Process Control, 493–554. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3848-8_14.

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Corriou, Jean-Pierre. "Dynamic Modelling of Chemical Processes." In Process Control, 3–75. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_1.

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Corriou, Jean-Pierre. "Identification Principles." In Process Control, 401–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_10.

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Corriou, Jean-Pierre. "Models and Methods for Parametric Identification." In Process Control, 419–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_11.

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Corriou, Jean-Pierre. "Parametric Estimation Algorithms." In Process Control, 455–503. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_12.

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Corriou, Jean-Pierre. "Generalized Predictive Control." In Process Control, 611–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_15.

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Corriou, Jean-Pierre. "Model Predictive Control." In Process Control, 631–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_16.

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Conference papers on the topic "Process control"

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Menke, Joseph. "In-Process Corrosion Control." In CORROSION 2002, 1–12. NACE International, 2002. https://doi.org/10.5006/c2002-02136.

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Abstract:
Abstract In-process corrosion occurs on stock from the mill, on parts that are freshly machined or fabricated, on pretreated material in the painting shop and on finished items in storage or transit. In each stage, attention must be paid to adequately provide corrosion control for the material. Corrosion control is the effort involved in eliminating deterioration after the part has been “finished.” In the fabrication process, after every operation, the part can be considered a finished item depending on who is performing that operation. Environments that can be encountered by in-process parts will be discussed throughout the paper. Corrosion control methods/packaging concepts used at various stages will also be discussed. Experience has shown that the timeframe for in-process parts can vary from as little as 24 hours to as much as 3 years. This makes selecting the corrosion control method/packaging very difficult. Materials include metallic and non-metallic items to be used in construction of hardware.
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Wang, Miao, Cyrus Tabery, Victor Blanco, and Bob Lin. "Via to metal line end process margin calibration by voltage contrast-based EPE process margin." In Metrology, Inspection, and Process Control XXXIX, edited by Matthew J. Sendelbach and Nivea G. Schuch, 138. SPIE, 2025. https://doi.org/10.1117/12.3051755.

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Butler, Dean, and Hongwei Zhang. "Intelligent software sensors and process prediction for glass container forming processes based on multivariate statistical process control techniques." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334643.

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Liu, Xueqin, Kang Li, Marion McAfee, and Jing Deng. "Polymer extrusion process monitoring using nonlinear dynamic model-based PCA." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334593.

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Araujo, Antonio, Simone Gallani, Michela Mulas, and Sigurd Skogestad. "Sensitivity of optimal operation of an activated sludge process model." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334639.

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Deng, Xiaogang, Xuemin Tian, Sheng Chen, and Chris J. Harris. "Statistics local fisher discriminant analysis for industrial process fault classification." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737588.

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"Process control." In 2004 Semiconductor Manufacturing Technology Workshop Proceedings. IEEE, 2004. http://dx.doi.org/10.1109/smtw.2004.1393732.

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"Process control." In 2004 Semiconductor Manufacturing Technology Workshop Proceedings. IEEE, 2004. http://dx.doi.org/10.1109/smtw.2004.1393738.

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Postlethwaite, Bruce. "The development of PISim: Software for process control teaching and learning." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737576.

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Abdullah, Muhammad, and John Anthony Rossiter. "Alternative Method for Predictive Functional Control to Handle an Integrating Process." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516787.

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Reports on the topic "Process control"

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Adams, Jesse. Process Control Data. Office of Scientific and Technical Information (OSTI), May 2022. http://dx.doi.org/10.2172/1866776.

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Morari, M. Modeling for process control. Office of Scientific and Technical Information (OSTI), January 1991. http://dx.doi.org/10.2172/5951697.

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Weck, Philippe, Nichole Fluke, Laura Price, Jeralyn Prouty, Ralph Rogers, David Sassani, and Walter Walkow. OWL Change Control Process. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1763925.

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Khuri-Yakub, B. T., and Krishna C. Saraswat. Sensors for In-Situ Process Monitoring and Process Control. Fort Belvoir, VA: Defense Technical Information Center, September 1996. http://dx.doi.org/10.21236/ada329734.

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Connors, John J., Kevin Hill, David Hanekamp, William F. Haley, Robert J. Gallagher, Craig Gowin, Arthur R. Farrar, et al. Sensor fusion for intelligent process control. Office of Scientific and Technical Information (OSTI), August 2004. http://dx.doi.org/10.2172/919114.

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Morari, M. Modeling for process control. Progress report. Office of Scientific and Technical Information (OSTI), December 1991. http://dx.doi.org/10.2172/10116349.

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Egorova, M. I., I. S. Michaleva, and E. S. Nikolaeva. Sugar production process flow control schemes. Federal Agricultural Kursk Research Center, December 2022. http://dx.doi.org/10.12731/ofernio.2022.25083.

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Pulsipher, B. A., and W. L. Kuhn. Statistical process control applied to the liquid-fed ceramic melter process. Office of Scientific and Technical Information (OSTI), September 1987. http://dx.doi.org/10.2172/5988542.

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Pleitt, Kristina, Julie Kozaili, Carina Huber, Charlie Heise, Shawkat Hussain, Mehdi Ghodbane, Ashley Reeder, et al. A RISK-BASED BLUEPRINT FOR PROCESS CONTROL OF CONTINUOUS BIOPROCESSING: PHASE 1: GENERIC RISK ASSESSMENT AA RISK-BASED BLUEPRINT FOR PROCESS CONTROL OF CONTINUOUS BIOPROCESSING: PHASE 1: GENERIC RISK ASSESSMENT AND ESTABLISHING PROCESS CONTROL PARAMETERSND ESTABLISHING PROCESS CONTROL PARAMETERS. BioPhorum, January 2021. http://dx.doi.org/10.46220/2021tr002.

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Leist, K. L. ,. Fluor Daniel Hanford. WRAP process area development control work plan. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/325868.

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