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Syafiie S.,University of Valladolid | Tadeo F.,University of Valladolid | Villafin M.,Process Engineering Group IIM CSIC | Alonso A.A.,Process Engineering Group IIM CSIC
ISA Transactions | Year: 2011

A control technique based on Reinforcement Learning is proposed for the thermal sterilization of canned foods. The proposed controller has the objective of ensuring a given degree of sterilization during Heating (by providing a minimum temperature inside the cans during a given time) and then a smooth Cooling, avoiding sudden pressure variations. For this, three automatic control valves are manipulated by the controller: a valve that regulates the admission of steam during Heating, and a valve that regulate the admission of air, together with a bleeder valve, during Cooling. As dynamical models of this kind of processes are too complex and involve many uncertainties, controllers based on learning are proposed. Thus, based on the control objectives and the constraints on input and output variables, the proposed controllers learn the most adequate control actions by looking up a certain matrix that contains the state-action mapping, starting from a preselected state-action space. This state-action matrix is constantly updated based on the performance obtained with the applied control actions. Experimental results at laboratory scale show the advantages of the proposed technique for this kind of processes. © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Lopez-Quiroga E.,Process Engineering Group IIM CSIC | Antelo L.T.,Process Engineering Group IIM CSIC | Alonso A.A.,Process Engineering Group IIM CSIC
Computer Aided Chemical Engineering | Year: 2010

The production of some chemicals or pharmaceuticals has been improved over the years by the development of a new concept of reactors, the reactor-heat exchangers, that overcome the classical constrains affecting many reaction units related to dissipation of heat and dilution/separation of products. The aim of this work is to propose a methodology for robust predictive control which aims at capturing the slow and most relevant, dynamics of the system. This model reduction constitutes the preliminary step to apply a real time optimization (RTO) framework for the robust control of reaction systems. The Open Plate Reactor (OPR) developed by the Swedish company Alfa Laval is used as a benchmark to validate the proposed methodology. © 2010 Elsevier B.V.

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