Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study.
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://www.teses.usp.br/teses/disponiveis/3/3137/tde-05032021-093254/ |
Resumo: | There is a rising need for more practical, efficient and sustainable techniques for improving industrial system operation in the face of a highly competitive market. The integration of real time optimization (RTO) and model predictive control (MPC) is a classical approach applied in the industry for improving processes. In this work, we analyze the application of the two most common RTO and MPC integration strategies - one-layer and two-layer - to a gas-lifted system. We analyzed the performance of the economic cost function and efficiency in handling disturbance for each strategy, as well as consider practical industrial application. in the two-layer strategy, an upper economic optimization layer uses a rigorous nonlinear steady state model to compute the optimal process decision variables and send to the controller as an optimizing target which then computes the optimal control actions to achieve these targets. For this strategy, the hybrid RTO (HRTO) technique is implemented in the upper layer and an established controller - infinite horizon MPC with zone control- in the lower dynamic control layer. The hybrid RTO stems from the modification of the traditional static RTO found in the industry, to deal with the steady state wait time challenge. This is achieved by updating the optimizer with dynamic information rather than the static used in the traditional RTO. The zone control strategy allows the controller to focus on reaching a desired input target supplied by the optimization layer if the outputs are kept within their specified zones and constraints are respected. In the one-layer strategy, the gradient of the economic cost function is included in the controller cost function to be considered when computing the manipulated variable used to achieve optimal process operation. It was proposed with the main aim of practical industrial application. The two strategies were applied to a gas-lifted system and their results are compared and discussed considering economic objective. The results show that the IHMPC can reach the desired input targets despite abrupt disturbances of the uncertain parameter while keeping the outputs within the desired zone. Therefore, the HRTO can efficiently work with the IHMPC implemented in achieving optimal operation under uncertainties interfering as disturbance. It also shows that the one layer strategy gives similar results to the two-layer strategy, implying that it can also achieve similar economic objective. However, the two-layer strategy using HRTO technique is more efficient in handling the disturbances. |
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Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study.Integração de otimização em tempo real com controle preditivo baseado em modelo aplicado a um sistema de gas-lift: um estudo comparativo.Controle por zonasControle preditivoEstimação em tempo real.Extended Kalman filter (EKF)Filtros de KalmanModel predictive control (MPC)Online estimationReal-time Optimization (RTO)Tempo real (Otimização)Zone controlThere is a rising need for more practical, efficient and sustainable techniques for improving industrial system operation in the face of a highly competitive market. The integration of real time optimization (RTO) and model predictive control (MPC) is a classical approach applied in the industry for improving processes. In this work, we analyze the application of the two most common RTO and MPC integration strategies - one-layer and two-layer - to a gas-lifted system. We analyzed the performance of the economic cost function and efficiency in handling disturbance for each strategy, as well as consider practical industrial application. in the two-layer strategy, an upper economic optimization layer uses a rigorous nonlinear steady state model to compute the optimal process decision variables and send to the controller as an optimizing target which then computes the optimal control actions to achieve these targets. For this strategy, the hybrid RTO (HRTO) technique is implemented in the upper layer and an established controller - infinite horizon MPC with zone control- in the lower dynamic control layer. The hybrid RTO stems from the modification of the traditional static RTO found in the industry, to deal with the steady state wait time challenge. This is achieved by updating the optimizer with dynamic information rather than the static used in the traditional RTO. The zone control strategy allows the controller to focus on reaching a desired input target supplied by the optimization layer if the outputs are kept within their specified zones and constraints are respected. In the one-layer strategy, the gradient of the economic cost function is included in the controller cost function to be considered when computing the manipulated variable used to achieve optimal process operation. It was proposed with the main aim of practical industrial application. The two strategies were applied to a gas-lifted system and their results are compared and discussed considering economic objective. The results show that the IHMPC can reach the desired input targets despite abrupt disturbances of the uncertain parameter while keeping the outputs within the desired zone. Therefore, the HRTO can efficiently work with the IHMPC implemented in achieving optimal operation under uncertainties interfering as disturbance. It also shows that the one layer strategy gives similar results to the two-layer strategy, implying that it can also achieve similar economic objective. However, the two-layer strategy using HRTO technique is more efficient in handling the disturbances.Otimização em tempo real (no inglês, RTO) e controle preditivo baseado em modelo (no inglês, MPC) é uma abordagem clássica na indústria para melhorar processos industriais. Nesta dissertação, duas técnicas mais comuns de integração de RTO e MPC - uma e duas camadas - são aplicadas em um sistema de gas-lift. Compara-se a performance da função objetivo econômico bem como a rejeição à distúrbios das duas configurações, considerando aplicação industrial prática. Na configuração de duas camadas, utiliza-se um modelo não-linear rigoroso de estado estacionário em uma camada de otimização econômica, que tem por função calcular variáveis de decisão ótimas para o processo. Estas variáveis são então enviadas para a camada inferior, responsável pelo controle dinâmico do sistema e por atingir os valores ótimos computados na camada superior. Nesta configuração, utiliza-se a técnica de RTO híbrida (do inglês, HRTO) na camada superior e uma estratégia de controle estabelecida na camada inferior - controle por zona de horizonte infinito. HRTO vem de modificações feitas na RTO estática com o propósito de lidar com a necessidade de aguardar o estado estacionário. Isso é feito por atualizações do otimizador com informações dinâmicas ao invés de estáticas, como é feito na RTO tradicional. A estratégia de controle por zona permite ao controlador focar em alcançar um alvo para as variáveis manipuladas uma vez que as variáveis controladas se encontrem dentro de suas zonas operacionais e as restrições do processo sejam satisfeitas. Para a configuração de uma camada, o gradiente da função objetivo econômico é incluso na função objetivo do controlador, para que o objetivo econômico seja considerado ao se computar valores para as variáveis manipuladas que acarretem operação ótima. As duas configurações foram aplicadas a um sistema de gas-lift e seus resultados são comparados com respeito ao objetivo econômico. Os resultados mostram que o MPC alcança os objetivos econômicos mesmo na presença de distúrbios no parâmetro estimado em tempo real concomitantemente mantendo as saídas dentro das zonas operacionais desejadas. Sendo assim, a HRTO opera juntamente com o MPC afim de levar a operação ótima, mesmo com incertezas perturbadas. Fica evidente que a estratégia de uma camada fornece resultados simulares à estratégia de duas camadas, implicando que também maximiza o objetivo econômico. Porém, a estratégia de duas camadas que utiliza a técnica de HRTO é mais eficiente em detectar e responder a distúrbios para este sistema.Biblioteca Digitais de Teses e Dissertações da USPOdloak, DarciShamaki, Patience Bello2020-11-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/3/3137/tde-05032021-093254/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2021-03-17T21:26:02Zoai:teses.usp.br:tde-05032021-093254Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212021-03-17T21:26:02Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study. Integração de otimização em tempo real com controle preditivo baseado em modelo aplicado a um sistema de gas-lift: um estudo comparativo. |
title |
Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study. |
spellingShingle |
Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study. Shamaki, Patience Bello Controle por zonas Controle preditivo Estimação em tempo real. Extended Kalman filter (EKF) Filtros de Kalman Model predictive control (MPC) Online estimation Real-time Optimization (RTO) Tempo real (Otimização) Zone control |
title_short |
Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study. |
title_full |
Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study. |
title_fullStr |
Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study. |
title_full_unstemmed |
Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study. |
title_sort |
Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study. |
author |
Shamaki, Patience Bello |
author_facet |
Shamaki, Patience Bello |
author_role |
author |
dc.contributor.none.fl_str_mv |
Odloak, Darci |
dc.contributor.author.fl_str_mv |
Shamaki, Patience Bello |
dc.subject.por.fl_str_mv |
Controle por zonas Controle preditivo Estimação em tempo real. Extended Kalman filter (EKF) Filtros de Kalman Model predictive control (MPC) Online estimation Real-time Optimization (RTO) Tempo real (Otimização) Zone control |
topic |
Controle por zonas Controle preditivo Estimação em tempo real. Extended Kalman filter (EKF) Filtros de Kalman Model predictive control (MPC) Online estimation Real-time Optimization (RTO) Tempo real (Otimização) Zone control |
description |
There is a rising need for more practical, efficient and sustainable techniques for improving industrial system operation in the face of a highly competitive market. The integration of real time optimization (RTO) and model predictive control (MPC) is a classical approach applied in the industry for improving processes. In this work, we analyze the application of the two most common RTO and MPC integration strategies - one-layer and two-layer - to a gas-lifted system. We analyzed the performance of the economic cost function and efficiency in handling disturbance for each strategy, as well as consider practical industrial application. in the two-layer strategy, an upper economic optimization layer uses a rigorous nonlinear steady state model to compute the optimal process decision variables and send to the controller as an optimizing target which then computes the optimal control actions to achieve these targets. For this strategy, the hybrid RTO (HRTO) technique is implemented in the upper layer and an established controller - infinite horizon MPC with zone control- in the lower dynamic control layer. The hybrid RTO stems from the modification of the traditional static RTO found in the industry, to deal with the steady state wait time challenge. This is achieved by updating the optimizer with dynamic information rather than the static used in the traditional RTO. The zone control strategy allows the controller to focus on reaching a desired input target supplied by the optimization layer if the outputs are kept within their specified zones and constraints are respected. In the one-layer strategy, the gradient of the economic cost function is included in the controller cost function to be considered when computing the manipulated variable used to achieve optimal process operation. It was proposed with the main aim of practical industrial application. The two strategies were applied to a gas-lifted system and their results are compared and discussed considering economic objective. The results show that the IHMPC can reach the desired input targets despite abrupt disturbances of the uncertain parameter while keeping the outputs within the desired zone. Therefore, the HRTO can efficiently work with the IHMPC implemented in achieving optimal operation under uncertainties interfering as disturbance. It also shows that the one layer strategy gives similar results to the two-layer strategy, implying that it can also achieve similar economic objective. However, the two-layer strategy using HRTO technique is more efficient in handling the disturbances. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-26 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/3/3137/tde-05032021-093254/ |
url |
https://www.teses.usp.br/teses/disponiveis/3/3137/tde-05032021-093254/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
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1815256687111569408 |