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NONLINEAR PREDICTIVE OPTIMAL CONTROL OF A BATCH REFRIGERATION PROCESS
Author(s) -
TRELEA IOANCRISTIAN,
ALVAREZ GRACIELA,
TRYSTRAM GILLES
Publication year - 1998
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/j.1745-4530.1998.tb00436.x
Subject(s) - nonlinear system , refrigeration , control theory (sociology) , robustness (evolution) , mathematical optimization , process (computing) , model predictive control , computer science , optimal control , process control , constant (computer programming) , mathematics , control (management) , engineering , mechanical engineering , programming language , biochemistry , chemistry , physics , quantum mechanics , artificial intelligence , gene , operating system
A predictive optimal control algorithm for a fruit refrigeration process is described. The future inlet air temperature profile is optimized on line, taking into account the most recent process measurements, and thus introducing feedback. The optimality criterion is directly based on economic costs. Technological and product quality constraints are included. The future control profile optimization requires long term predictions of process variables evolution, achieved using a physical model of the plant. The model is adjusted on line in order to account for unmeasured disturbances and poorly known product properties. Both the optimality criterion and the plant model are nonlinear. The approach is experimentally validated on a pilot scale refrigeration plant. Economic benefits from using an optimal, rather than a traditional constant temperature profile are discussed, revealing the importance of the process configuration. Robustness of the control algorithm with respect to modeling errors, measured and unmeasured disturbances, and temporary plant failures is demonstrated.