Optimized multivariable predictive control of an industrial distillation column considering hard and soft constraints

Volk, U.; Kniese, D.-W.; Hahn, R. S.; Haber, Robert; Schmitz, Ulrich

doi:10.1016/j.conengprac.2004.09.007

Cited by 31 publications

(16 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…O GPC vem se tornando um dos controladores mais populares tanto na indústria quanto na academia devido ao sucesso em aplicações industriais (Volk et al, 2004) e (Richalet, 1993), principalmente em sua abordagem multivariável e com tratamento de restrições.…”

Section: Análise Em Malha Fechada Do Controlador Generalized Predictiunclassified

Análise Em Malha Fechada Do Controlador Generalized Predictive Control (Gpc)

Cavalcanti¹

2009

HOLOS

View full text Add to dashboard Cite

RESUMO O presente trabalho tem o objetivo de apresentar uma análise em malha fechada do controlador Generalized Predictive Control (GPC). Esta análise visa observar, com detalhes, as características deste tipo de controlador. Os detalhes apresentados são de extrema importância na análise de estabilidade robusta. Alguns resultados de simulação são apresentados. PALAVRAS-CHAVE: Controle preditivo, sistemas em malha fechada. CLOSED-LOOP ANALYSIS OF GENERALIZED PREDICTIVE CONTROL (GPC) ABSTRACT This paper presents a closed loop analysys of Generalized Predictive Control GPC. This analysis observes, in details, the features of this kind of predictive controller The details showed are very important in robust stability analysis. Simulation results are shown. KEY-WORDS: Predictive control, closed-loop systems.

show abstract

Section: Análise Em Malha Fechada Do Controlador Generalized Predictiunclassified

Análise Em Malha Fechada Do Controlador Generalized Predictive Control (Gpc)

Cavalcanti¹

2009

HOLOS

View full text Add to dashboard Cite

show abstract

“…Volk et al [15] tested the multivariable predictive control of an crude distillation column. This controller keeps the set-points of the distillate and bottom concentration constant.…”

Section: Nonlinear Autoregressive-moving Average Narma-l2 (Feedback Lmentioning

confidence: 99%

Artificial Neural Networks Controller for Crude Oil Distillation Column of Baiji Refinery

Ahmed¹,

Khalaf²

2015

J Chem Eng Process Technol

View full text Add to dashboard Cite

A neural networks controller is developed and used to regulate the temperatures in a crude oil distillation unit. Two types of neural networks are used; neural networks predictive and nonlinear autoregressive moving average (NARMA-L2) controllers. The neural networks controller that is implemented in the neural network toolbox software uses a neural network model of a nonlinear plant to predict future plant performance. Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. A comparison has been made between two methods to test the effectiveness and performance of the responses. The results show that a good improvement is achieved when the NARMA-L2 controller is used with maximum mean square error of 103.1 while the MSE of neural predictive is 182.7 respectively. Also shown priority of neural networks NARMA-L2 controller which gives less offset value and the temperature response reach the steady state value in less time with lower over-shoot compared with neural networks predictive controller.

show abstract

“…6, which separates chemical petro in a refinery of Shell Company in Cologne (FRG) in Germany, a multi-variable predictive controller has been developed [28]. Its feed is chemical petrol from the desulfurization, its top product is light petro with boiling point between 30 and 65°C, and its bottom product is heavy petro with boiling point between 65 and 180°C.…”

Section: Case Studymentioning

confidence: 99%

“…All CVs/MVs and their corresponding parameters are shown in Tables 3 and 4. According to [28], the first three CVs are quality variables. The concentration of the heavy petro in the top product (distillate) has to be kept below 2.5%.…”

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Alternative solutions to multi-variate control performance assessment problems

Huang

Ding

Thornhill

2006

Journal of Process Control

View full text Add to dashboard Cite

Performance assessment of multi-variate control with minimum variance control as the benchmark requires an interactor matrix to filter the closed-loop output. This is to transfer the coordinate of the original variables into a new one in order to identify the control invariant disturbance dynamics from the first few terms of the closed-loop output Markov parameters. There has been a great deal of interest to simplify this approach, in particular, to find methods that do not need the interactor matrix. With this motivation, this paper explores alternative solutions to multi-variate control performance assessment problems. In particular, we will consider two practical scenarios: (1) known time delays between each pair of inputs and outputs, (2) no a priori knowledge about the process model or time delays at all. Solutions to these two scenarios are proposed. Two data-driven algorithms based on subspace approach are derived for the calculation of performance measures. Several examples illustrate the feasibility of the proposed approaches.

show abstract

Optimized multivariable predictive control of an industrial distillation column considering hard and soft constraints

Cited by 31 publications

References 2 publications

Análise Em Malha Fechada Do Controlador Generalized Predictive Control (Gpc)

Análise Em Malha Fechada Do Controlador Generalized Predictive Control (Gpc)

Artificial Neural Networks Controller for Crude Oil Distillation Column of Baiji Refinery

Alternative solutions to multi-variate control performance assessment problems

Contact Info

Product

Resources

About