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Adaptive and Predictive Control of Liquid‐Liquid Extractors Using Neural‐Based Instantaneous Linearization Technique
Author(s) -
Mjalli F. S.
Publication year - 2006
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.200500271
Subject(s) - control theory (sociology) , model predictive control , raffinate , linearization , controller (irrigation) , artificial neural network , nonlinear system , process (computing) , adaptive control , trajectory , stability (learning theory) , set (abstract data type) , computer science , engineering , control engineering , extraction (chemistry) , control (management) , artificial intelligence , machine learning , chemistry , physics , chromatography , quantum mechanics , astronomy , agronomy , biology , operating system , programming language
Nonlinearity of the extraction process is addressed via the application of instantaneous linearization to control the extract and raffinate concentrations. Two feed‐forward neural networks with delayed inputs and outputs were trained and validated to capture the dynamics of the extraction process. These nonlinear models were then adopted in an instantaneous linearization algorithm into two control algorithms. The self‐tuning adaptive control strategy was compared to an approximate model predictive control in terms of set point tracking capability, efficiency and stability. For the case of large, abrupt set point changes, the performance of the self‐tuning algorithm was poor, especially for the raffinate control. The approximate model predictive control strategy was superior to the self‐tuning control in terms of its ability to force the output to following the set point trajectory efficiently with smooth controller moves.