Hybrid systems combine the continuous behavior evolution specified by differential equations with discontinuous changes specified by discrete event logic. Usually these systems in the processing industry can be identified as having to depend on discrete decisions regarding their operation. In process control there therefore is a challenge to automate these decisions. A model predictive control (MPC) strategy was proposed and verified for the control of hybrid systems. More specifically, the dynamic matrix control (DMC) framework commonly used in industry for the control of continuous variables was modified to deal with mixed integer variables, which are necessary for the modelling and control of hybrid systems. The algorithm was designed and commissioned in a closed control loop comprising a SCADA system and an optimiser (GAMS). GAMS (General Algebraic Modelling System) is an optimisation package that is able to solve for integer/continuous variables given a model of the system and an appropriate objective function. Online and offline closed loop tests were undertaken on a benchmark interacting tank system and a heating/cooling circuit. The algorithm was also applied to an industrial problem requiring the optimal sequencing of coal locks in real time. To complete the research concerning controller design for hybrid behavior, an investigation was undertaken regarding systems that have different modes of operation due to physicochemical (inherent) discontinuities e.g. a tank with discontinuous cross sectional area, fitted with an overflow. The findings from the online tests and offline simulations reveal that the proposed algorithm, with some system specific modification, was able to control each of the four hybrid systems under investigation. Based on which hybrid system was being controlled, by modifying the DMC algorithm to include integer variables, the mixed integer predictive controller (MIPC) was employed to initiate selections, switchings and determine sequences. Control of the interacting tank system was focused on an optimum selection in terms of operating positions for process inputs. The algorithm was shown to retain the usual features of DMC (i.e. tuning and dealing with multivariable interaction). For a system with multiple modes of operation i.e. the heating/cooling circuit, the algorithm was able to switch the mode of operation in order to meet operating objectives. The MPC strategy was used to good effect when getting the algorithm to sequence the operation of several coal locks. In this instance, the controller maintained system variables within certain operating constraints. Furthermore, soft constraints were proposed and used to promote operation close to operating constraints without the danger of computational failure due to constraint violations. For systems with inherent discontinuities, a MPC strategy was proposed that predicted trajectories which crossed discontinuities. Convolution models were found to be inappropriate in this instance and state space equations describing the dynamics of the system were used instead. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2002.

Dynamic Matrix Control (DMC) has proven to be a powerful tool for optimal regulation of chemical processes under constrained conditions. The internal model of this predictive controller is based on step response measurements at an average operating point. As the process moves away from this point, however, control becomes sub-optimal due to process non-linearity. If DMC is made adaptive, it can be expected to perform well even in the presence of uncertainties, non-linearities and time-vary ing process parameters. This project examines modelling and control issues for a complex multivariable industrial operator training plant, and develops and applies a method for adapting the controller on-line to account for non-linearity. A two-input/two-output sub-system of the Training Plant was considered. A special technique had to be developed to deal with the integrating nature of this system - that is, its production of ramp outputs for step inputs. The project included the commissioning of the process equipment and the addition of instrumentation and interfacing to a SCADA system which has been developed in the School of Chemical Engineering. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2001.

Process control is essential in chemical engineering and has diverse applications in automation, manufacturing, scheduling, etc. In this cross-disciplinary work, we shift the domain focus from the control of machines to the control of multiple intelligent agents. Our goal is to improve the optimization problem-solving process, such as optimal regulation of emerging technologies, in a multi-agent system. Achieving that improvement would have potential value both within and outside the chemical engineering community. This work also illustrates the possibility of applying process systems engineering techniques, especially process control, beyond chemical plants. It is very common to observe crowds of individuals solving similar problems with similar information in a largely independent manner. We argue here that the crowds can become more efficient and robust problem-solvers, by partially following the average opinion. This observation runs counter to the widely accepted claim that the wisdom of crowds deteriorates with social influence. The key difference is that individuals are self-interested and hence will reject feedbacks that do not improve their performance. We propose a multi-agent control-theoretic methodology, soft regulation, to model the collective dynamics and compute the degree of social influence, i.e., the level to which one accepts the population feedback, that optimizes the problem-solving performance. Soft regulation is a modeling language for multi-agent sociotechnical systems. The state-space formulation captures the individual learning process (i.e., open loop dynamics) as well as the influence of the population feedback in a straightforward manner. It can model a diverse set of existing multi-agent dynamics. Through numerical analysis and linear algebra, we attempt to understand the role of feedback in multi-agent collective dynamics, thus achieving multi-agent control in sociotechnical systems. Our analysis through mathematical proofs, simulations, and a human subject experiment suggests that intelligent individuals, solving the same problem (or similar problems), could do much better by adaptively adjusting their decisions towards the population average. We even discover that the crowd of human subjects could self-organize into a near-optimal setting. This discovery suggests a new coordination mechanism for enhancing individual decision-making. Potential applications include mobile health, urban planning, and policymaking.

Thesis (Ph.D.)--McMaster University, 2002. / Adviser: P.A. Taylor and John F. MacGregor. Includes bibliographical references. Also available via World Wide Web.

Thesis (Ph.D.)--McMaster University, 2002. / Adviser: P.A. Taylor and John F. MacGregor. Includes bibliographical references. Also available via World Wide Web.

Orientadores: Flávio Vasconcelos da Silva, Ana Maria Frattini Fileti / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-16T18:45:23Z (GMT). No. of bitstreams: 1 Costa_ThiagoVazda_M.pdf: 15859888 bytes, checksum: 391c96e4f2ff3598497e8840686f8166 (MD5) Previous issue date: 2010 / Resumo: Uma malha de controle com baixo desempenho implica em um aumento dos custos de produção, causando descarte de produto fora da especificação e desgaste desnecessário dos elementos finais de controle. A depender do processo controlado, uma malha deficiente pode também acarretar em paradas não previstas na planta e até mesmo em danos ao meio ambiente. Diante do exposto, o controle preditivo baseado em modelos (MPC) é um dos poucos algoritmos comprovadamente capazes de estabilizar processos na presença de não-linearidades e restrições. Para atender aos seus objetivos de controle, o algoritmo clássico MPC utiliza um procedimento de otimização baseado no modelo linear da planta. Contudo, o afastamento da região de projeto do modelo linear resulta na perda de sua efetividade e consequente do controlador que o utiliza. Deste modo, objetivou-se a partir de uma descrição não-linear do sistema a melhoria do desempenho do controlador. Os objetivos específicos dessa dissertação foram o estudo e análise de um controlador GPC (generalized predictive controller) operando em paralelo com uma rede de modelos lineares locais, identificada por meio do algoritmo LOLIMOT (local linear model trees), capaz de adequar o modelo de predição do controlador para a faixa de operação atual do processo. Para a avaliação e análise da qualidade do controlador proposto foi montado um aparato experimental para controle de pH. A estratégia de controle foi implementada em um sistema em código aberto para monitoramento e controle do processo. Portanto, considerando a característica estática não-linear do processo foram realizados estudos comparativos entre o GPC tradicional (baseado em um único modelo linear) e a abordagem proposta. Os resultados mostraram que a rede foi capaz de representar satisfatoriamente a saída do sistema, resultando em uma estrutura simples, com modelos locais na forma de estruturas ARX. Também foi demonstrado que o GPC utilizando a rede de modelos lineares locais desempenhou de forma satisfatória e até mesmo superior ao GPC tradicional.Observou-se que a saída calculada pelo controlador proposto foi consideravelmente menos agressiva que o controlador tradicional, levando a uma considerável diminuição do esforço de controle empregado ao sistema. Os resultados obtidos demonstraram que houve uma economia de até 45% no esforço de controle. Observou-se ainda que o sistema desenvolvido é conveniente para aplicações reais, já que a estratégia de controle preditivo concebida em Scilab obteve sucesso na solução do problema de controle dentro do intervalo de amostragem inclusive quando incorporado o problema QP (programação quadrática) com restrições. Tendo em vista que a estrutura destes sistemas permite que sejam utilizados nas mesmas aplicações destinadas a modelos lineares, comprovou-se também a viabilidade e aplicabilidade do uso das redes de modelos lineares locais diretamente em algoritmos de controle avançado já disponíveis para indústria, como nos controladores GPC / Abstract: Low performance control loops imply in higher production costs, leading to off-specification production loss and unnecessary wear of the final control elements. Depending on the controlled process, the deficient loop can also lead to non-expected plant stops and even on environment damage. In this sense, model predictive control (MPC) is one of the few algorithms proved capable to stabilize processes in the presence of nonlinearities and constraints. To meet its control objectives, the classic MPC algorithm is based on an optimization problem which relies in the system's linear model. Although, the removal of the linear model from its designed condition deteriorates the model's and controller's effectiveness. Hence, the general objective of the presented work relies in the non-linear description of the system for improving the control performance. The main objectives were the study and analysis of a generalized predictive controller (GPC) operating in parallel with a linear local model network, identified by the LOLIMOT algorithm, able to adequate the controller's prediction model for the process operation range. For the quality assessment of the proposed controller, tests were evaluated in an experimental apparatus for pH control. The control strategy was implemented in an open source system for monitoring and control. Therefore, considering the static nonlinear characteristics of the process, comparative studies were applied between the traditional GPC (based on an single global model) and the proposed approach. The results showed that the dynamic network was able to effectively represent the system output, resulting in a simple structure, given the fact that the local models are indeed local ARX models. It was also shown that the GPC using the linear local model network performed satisfactorily and even better than the single model GPC. It was observed that the output calculated by the proposed controller has been considerably less aggressive than the traditional controller, leading to a considerable reduction in the system's control effort. The results showed that there was a saving up to 45% in the control effort. It was also observed that the developed monitoring and control system is suitable for real applications, since the predictive control strategy, implemented in Scilab, succeeded in solving the control problem within the sampling time even with the embedded constrained QP (quadratic programming) problem. Considering that the structure of these systems allows them to be used in similar applications to linear models, it was also proved the viability and applicability of using the linear local models network directly into advanced control algorithms already available to industry as in the GPC controllers / Mestrado / Sistemas de Processos Quimicos e Informatica / Mestre em Engenharia Química

Orientador: Roger Josef Zemp / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-23T09:46:30Z (GMT). No. of bitstreams: 1 Santos_RaphaelRibeiroCruz_M.pdf: 3339099 bytes, checksum: 221059e3eab5645623f894afee5431de (MD5) Previous issue date: 2013 / Resumo: Neste trabalho é apresentada uma metodologia de desenvolvimento de um sistema supervisório utilizando Microsoft Excel®. O sistema foi desenvolvido para operar uma planta de polimerização em solução de estireno. A aplicação de códigos computacionais na em VBA (Visual Basic for Applications) na planilha torna possível a criação de um cliente OPC para comunicação entre o computador e hardware. A planilha possui, portanto condições de receber, enviar, armazenar e tratar informações vindas do processo. A polimerização de estireno é realizada utilizando tolueno como solvente e BPO (Peroxido de Benzoíla) como iniciador. Aquisição da temperatura do reator, temperatura de entrada e saída da camisa e massa especifica do meio reacional foram realizada. A partir dos dados experimentais extraídos da reação de polimerização, foi gerado um modelo empírico utilizando Redes Neurais Artificiais (RNA). As RNAs são implementadas no Excel de forma simples, usando a própria planilha, por operar matrizes, tornando-se oportunas para o desenvolvimento de controladores preditivos. O Microsoft Excel® mostrou-se uma interessante ferramenta para aplicação em automação de protótipos experimentais / Abstract: This research work presents a methodology for the development of a supervisory system using Microsoft Excel®. The system was designed to operate a styrene polymerization plant. The development of computational codes in VBA (Visual Basic for Applications) allows for the creation of an OPC client for communication between computer and hardware. The spreadsheet has porting is able to receive, send, store and process information from the process. The polymerization of styrene is carried out using toluene as solvent and BPO (benzoyl peroxide) as an initiator. Acquisition of reactor temperature, inlet temperature and outlet shirt and specific mass of the reaction medium were performed. From the experimental data extracted from the polymerization reaction, an empirical model was generated using Artificial Neural Networks (RNA). RNAs are easily implemented in Excel simply, using operations arrays, making it appropriate for the development of predictive controllers. The use of Microsoft Excel® proved to be an interesting tool for application in automation experimental prototypes / Mestrado / Engenharia Química / Mestre em Engenharia Química

Orientador: Flávio Vasconcelos da Silva / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-23T22:24:17Z (GMT). No. of bitstreams: 1 Fonseca_RodolphoRodrigues_M.pdf: 9175458 bytes, checksum: f2dde4cc9ff6948eb7de39f51a322a4a (MD5) Previous issue date: 2013 / Resumo: Devido ao aumento da demanda nacional e mundial por combustíveis renováveis e novas tecnologias para melhoria de seus processos, é inegável a importância do desenvolvimento de novos controladores que possam garantir o funcionamento adequado destes sistemas. Neste contexto, este trabalho focou no desenvolvimento de um tipo de controlador não convencional baseado em inteligência artificial (Lógica Fuzzy) associado a uma estratégia Split-range para a manutenção da temperatura de reação de transesterificação do óleo de soja. Os ensaios foram conduzidos em um reator batelada totalmente instrumentado, monitorado e controlado via SCADA (Supervisory Control And Data Acquisition). Verificou-se que a melhor estratégia proposta para os sistemas de controle Fuzzy - Split-range na regulação da temperatura do reator foi a que empregou 147 regras sem a mistura de utilidades na jaqueta do reator, obtendo rápida estabilização da temperatura do reator, aproximadamente 15 minutos, e menor esforço de controle quando comparado às demais estratégias testadas. Como ferramentas de análise comparativa do sistema de controle foram utilizados os critérios de desempenho IAE, ISE e ITAE, além dos esforços de controle requeridos pelas válvulas durante os ensaios. Os resultados mostraram que a combinação Fuzzy - Split-range é viável no controle de temperatura, podendo ser estendida a demais processos industriais. / Abstract: In fact of national and international demand increasing for renewable fuels as biodiesel and also new technologies for process enhancement, it is worthy of attention the development of new controllers that guarantee adequate biodiesel production process control. In this context, this work applied the design of a non-conventional controller based on artificial intelligence (Fuzzy Logic) associated with Split-range strategy to regulate the temperature of soybean oil transesterification. The tests were conducted in a instrumented batch reactor, monitored and controlled by a SCADA (Supervisory Control And Data Acquisition) system. For the studied process control, the best combination set among the Fuzzy - Split-range strategies for the reactor's temperature control applied 147 set rules and no mixture of utilities in reactor's jacket. With fast temperature estabilization in almost 15 min, less control effort was required by the system among the strategies testeds. Performance criterions as IAE, ISE and ITAE were used to support comparative analysis, either control efforts by valves were used. The results show that Fuzzy - Split-range strategy is viable in biodiesel batch reactor temperature control, promising to application in others chemical processes. / Mestrado / Sistemas de Processos Quimicos e Informatica / Mestre em Engenharia Química

Orientador: Ana Maria Frattini Fileti / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-21T17:10:58Z (GMT). No. of bitstreams: 1 VidalSantos_SauloFernandodos_M.pdf: 3908407 bytes, checksum: 8f5bba56950cb6628faf5b13df5bea0e (MD5) Previous issue date: 2012 / Resumo: Na maioria das indústrias químicas se faz necessário a refrigeração de equipamentos, produtos, processos, ambientes, etc. A grande desvantagem dos sistemas de refrigeração empregados nesses processos está no gasto energético envolvido, que pode representar 70% do gasto total de uma planta. Visando minimizar esses gastos, exaustivos trabalhos de pesquisa vem sendo desenvolvidos neste campo. No Laboratório de Controle e Automação de Processos da Faculdade de Engenharia Química da Unicamp, há um protótipo de um chiller utilizado em pesquisas de técnicas de modelagem e controle visando reduzir o gasto energético empregado nos sistemas de refrigeração. Com o intuito de facilitar estes estudos, este trabalho desenvolveu um modelo a partir dos simuladores comerciais ASPEN PLUS'MARCA REGISTRADA' e ASPEN DYNAMICS'MARCA REGISTRADA' capaz de representar o protótipo experimental citado. Para validação do modelo, foram utilizados dados obtidos da própria planta experimental, em diferentes condições de processo, garantindo a funcionalidade do modelo para situações diversas de funcionamento do equipamento. Uma vez validado, o modelo desenvolvido foi utilizado no estudo dinâmico do processo de refrigeração. Também foram verificadas estratégias de controle clássico e sua aplicabilidade nos sistema em questão. Está presente no trabalho o detalhamento das variáveis e parâmetros que foram utilizados na configuração, no ASPEN PLUS'MARCA REGISTRADA', dos blocos representativos dos equipamentos. Todas as formas de controle PID sugeridas se adaptaram bem ao processo, mas apenas no ponto de operação para qual o controle foi sintonizado, ou seja, para cada ponto de operação, uma nova sintonia deve ser realizada / Abstract: Refrigeration systems are required in most chemical industries in order to maintain the temperature in process equipments, production line, air-conditioned environments or rooms, etc. The main drawback of the refrigeration systems is the great expense of energy, which can represent up to 70% of the energy expenditure of the global plant. Aiming to minimize these costs, several scientific works have been developed in this field. In the Automation and Process Control Laboratory at FEQ/UNICAMP, there is a chiller prototype used in technical research on modeling and control strategies to save energy in refrigeration systems. In order to facilitate these studies, in this work it was developed a model, based on commercial simulators such as ASPEN PLUS 'TRADE MARK' and ASPEN DYNAMICS'TRADE MARK', capable of representing the experimental prototype. To validate the model, data obtained from the experimental plant, under different process conditions was used, ensuring the functionality of the model for different operating conditions. After validated, the model was used to the dynamic cooling process study. Classical control strategies were applied and their applicability in the cooling system was analyzed. This work contains details on the configuration of variables and parameters used in the equipment simulation blocks from ASPEN PLUS'TRADE MARK'. All proposed strategies of PID control showed to be well adapted to the process, but only at the operating point for which control was tuned. To summarize, for each operating point a new controller tuning must be carried out / Mestrado / Sistemas de Processos Quimicos e Informatica / Mestre em Engenharia Química

Orientador: Flávio Vasconcelos da Silva / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-20T15:49:40Z (GMT). No. of bitstreams: 1 Navarro_AlexandreKhaeWu_M.pdf: 4067789 bytes, checksum: 20f4684078783b9a38711e856a726e6e (MD5) Previous issue date: 2012 / Resumo: O controle de distribuições de cristais é de grande importância para a indústria química de alta tecnologia, encontrando especial aplicação na produção de fármacos e alimentos. Nestas indústrias, outra característica é igualmente importante: polimorfismo. Legislações específicas comumente requerem a presença de um determinado tipo de polimorfo. Como o controle para obtenção destas características é de relevância industrial e tipicamente de difícil realização, neste trabalho, foi estudado o controle da cristalização do ácido L-glutâmico por resfriamento visando obter um único polimorfo e maximizar o tamanho dos cristais ao final da batelada. Este tema foi abordado utilizando controle ótimo através de três estratégias diferentes: controle ótimo em malha aberta, controle ótimo em malha aberta com rastreamento de temperatura e concentração e controle ótimo em malha fechada. As otimizações foram realizadas no software Scilab através de um método quasi-newton em esquema sequencial, de forma que os momentos da distribuição de cristais eram simulados e a curva de resfriamento ajustadas com base na simulação. Para lidar com o efeito de dissolução total de um dos polimorfos, característica que não é capturados pelos momentos populacionais, foi utilizado um loop de integração baseado na interpretação física dos valores dos momentos populacionais. Ao final do trabalho, verificou-se que a estratégia de controle ótimo em malha fechada obteve melhores resultados / Abstract: Crystal size distribution control is of great importance to high-end chemical industry, especially in applications to the production of pharmaceuticals and foods. In these industries, another crystal characteristic is equally important: polymorphism. Strict regulations often require that only an specific type of polymorph may be present in a determined product. As controlling these factors is both of industrial relevance and typically difficult, in this work, the control of batch L-glutamic acid crystallization by cooling aiming to obtain one specific polymorph while maximizing crystal size at the end of the operation. This theme was approached by using optimal control and three different strategies: open-loop optimal control, open-loop optimal control with temperature and concentration tracking, and closed-loop optimal control. The optimizations were performed in Scilab through a quasi-newton method in a sequential process so that the moments of the crystal size distribution were simulated and the cooling curves adjusted based on the simulation and the objective. To deal with the total dissolution of one of the polymorphs, a feature not captured by the populational momentts, a special integration loop was used based on the physical interpretation of the moment values. Finalluy, at the end of the study, it was found that the closed-loop optimal control approach provided better results / Mestrado / Sistemas de Processos Quimicos e Informatica / Mestre em Engenharia Química