Economic nonlinear model predictive control using hybrid mechanistic data-driven models for optimal operation in real-time electricity markets: In-silico application to air separation processes

2019 ◽  
Vol 84 ◽  
pp. 171-181 ◽  
Author(s):  
Pascal Schäfer ◽  
Adrian Caspari ◽  
Adel Mhamdi ◽  
Alexander Mitsos
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Electricity Markets ◽  
In Silico ◽  
Optimal Operation ◽  
Air Separation ◽  
Nonlinear Model Predictive Control ◽  
Data Driven ◽  
Separation Processes

Nonlinear Model Predictive Control of High Purity Distillation Columns for Cryogenic Air Separation

2010 ◽  
Vol 18 (4) ◽  
pp. 811-821 ◽  
Author(s):  
Zhongzhou Chen ◽  
Michael A. Henson ◽  
Paul Belanger ◽  
Lawrence Megan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
High Purity ◽  
Air Separation ◽  
Nonlinear Model Predictive Control ◽  
Distillation Columns

scholarly journals Optimal operation of a Solar Membrane Distillation pilot plant via Nonlinear Model Predictive Control

2018 ◽  
Vol 109 ◽  
pp. 151-165 ◽  
Author(s):  
Juan D. Gil ◽  
Lidia Roca ◽  
Alba Ruiz-Aguirre ◽  
Guillermo Zaragoza ◽  
Manuel Berenguel
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Pilot Plant ◽  
Membrane Distillation ◽  
Optimal Operation ◽  
Nonlinear Model Predictive Control

scholarly journals Data-Driven Optimization Framework for Nonlinear Model Predictive Control

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Shiliang Zhang ◽  
Hui Cao ◽  
Yanbin Zhang ◽  
Lixin Jia ◽  
Zonglin Ye ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Learning Algorithm ◽  
Nonlinear Model Predictive Control ◽  
Data Driven ◽  
Optimization Framework ◽  
Linguistic Model ◽  
Input Variables ◽  
Control Accuracy

The structure of the optimization procedure may affect the control quality of nonlinear model predictive control (MPC). In this paper, a data-driven optimization framework for nonlinear MPC is proposed, where the linguistic model is employed as the prediction model. The linguistic model consists of a series of fuzzy rules, whose antecedents are the membership functions of the input variables and the consequents are the predicted output represented by linear combinations of the input variables. The linear properties of the consequents lead to a quadratic optimization framework without online linearisation, which has analytical solution in the calculation of control sequence. Both the parameters in the antecedents and the consequents are calculated by a hybrid-learning algorithm based on plant data, and the data-driven determination of the parameters leads to an optimization framework with optimized controller parameters, which could provide higher control accuracy. Experiments are conducted in the process control of biochemical continuous sterilization, and the performance of the proposed method is compared with those of the methods of MPC based on linear model, the nonlinear MPC with neural network approximator, and MPC nonlinear with successive linearisations. The experimental results verify that the proposed framework could achieve higher control accuracy.

Optimal operation of adsorption chillers: First implementation and experimental evaluation of a nonlinear model-predictive-control strategy

2019 ◽  
Vol 149 ◽  
pp. 1503-1521 ◽  
Author(s):  
Uwe Bau ◽  
Nils Baumgärtner ◽  
Jan Seiler ◽  
Franz Lanzerath ◽  
Christian Kirches ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Control Strategy ◽  
Experimental Evaluation ◽  
Optimal Operation ◽  
Nonlinear Model Predictive Control
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