Predictive control with multiobjective optimization: Application to a sludge drying operation

•A predictive command strategy with a multiobjective optimization problem is proposed.•The procedure is used to found an optimal command policy for the air drying conditions.•A multiphysics model is validated for the drying step with a reversal operation.•An energetic analysis of the improvement is...

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Bibliographic Details
Published in:Computers & chemical engineering 2015-07, Vol.78, p.70-78
Main Authors: Fuentes, A., Ploteau, J.P., Glouannec, P.
Format: Article
Language:English
Subjects:
Computer simulation
Drying
Mathematical analysis
Mathematical models
Moisture content
Multiobjective optimization
Multiphysics modeling
Offline predictive control
Operating input parameters
Optimization
Predictive control
Sludge
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Summary:•A predictive command strategy with a multiobjective optimization problem is proposed.•The procedure is used to found an optimal command policy for the air drying conditions.•A multiphysics model is validated for the drying step with a reversal operation.•An energetic analysis of the improvement is proposed. The main objective of this study is to develop an offline tuning of the operating input parameters for a sludge drying operation, by using multiobjective optimization techniques combined with a predictive control method. The manipulated variables concerned are the temperature and the relative humidity of the drying air (Tair, RHair). The optimal time for the reversal operation of the product is also investigated. The optimization procedure is coupled to a one-dimensional numerical model that allows the simulation of moisture content and temperature field evolutions in the product during the drying step. A genetic algorithm is used to identify the two manipulated variables, at each step time, by minimizing simultaneously three objective functions over a finite horizon. These objective functions are linked to penalties concerning the heating and dehumidifying of the outside air used for the drying stage and to a global moisture content gap relative to a drying target. First, the heat and mass transfer model is validated for the drying step of a plate sample of sludge, with a reversal operation. Afterwards, the optimization procedure is carried out, and the results are discussed in terms of an energetic analysis.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2015.04.017