Off-Line Determination of the Optimal Command Policies for a Sludge Drying Application

The main objective of this work is to develop, using a predictive control method, an off-line determination of the operating parameters for a sludge drying stage. At each time step, two operating parameters are identified by simultaneously minimizing three objective functions over a finite horizon. A laboratory dryer is briefly presented and used, in order to evaluate the suitability of the direct model employed to simulate sludge drying. Surface temperature, drying kinetics, and evaporated mass flux obtained from experimental measurements are compared to numerical simulations. Afterward, the optimization procedure is carried out and the results are discussed.

[1]  Shane Clements,et al.  Experimental verification of a heat pump assisted continuous dryer simulation model , 1993 .

[2]  Z. Erbay,et al.  Optimization of spray drying process in cheese powder production , 2015 .

[3]  Angélique Léonard,et al.  Review on fundamental aspect of application of drying process to wastewater sludge , 2013 .

[4]  Davide Fissore,et al.  A Model-Based Framework to Optimize Pharmaceuticals Freeze Drying , 2012 .

[5]  Paulo César Stringheta,et al.  Parameter optimization for spray-drying microencapsulation of jaboticaba (Myrciaria jaboticaba) peel extracts using simultaneous analysis of responses , 2013 .

[6]  H. Ramon,et al.  MPC as control strategy for pasta drying processes , 2009, Comput. Chem. Eng..

[7]  J. Vaxelaire,et al.  Moisture distribution in activated sludges: a review. , 2004, Water research.

[8]  Pascal Dufour,et al.  On nonlinear distributed parameter model predictive control strategy: on-line calculation time reduction and application to an experimental drying process , 2003, Comput. Chem. Eng..

[9]  Julio R. Banga,et al.  On the Optimal Control of Contact-Cooking Processes , 2001 .

[10]  P. Glouannec,et al.  Experimental and Numerical Study of Flat Plate Sludge Drying at Low Temperature by Convection and Direct Conduction , 2014 .

[11]  A. Chauhan,et al.  Optimization of the spray-drying process for developing guava powder using response surface methodology , 2014 .

[12]  Mehdi Rajabi-Hamane,et al.  Optimization of Drying–Tempering Periods in a Paddy Rice Dryer , 2012 .

[13]  Arun S. Mujumdar,et al.  SLUDGE DEWATERING AND DRYING , 2002 .

[14]  Mustafa Turker,et al.  Nonlinear predictive control of a drying process using genetic algorithms. , 2006, ISA transactions.

[15]  Davide Fissore,et al.  In-Line and Off-Line Optimization of Freeze-Drying Cycles for Pharmaceutical Products , 2013 .

[16]  Davide Fissore,et al.  Freeze-drying cycle optimization using model predictive control techniques , 2011 .

[17]  P. Kinsman,et al.  Hazard Assessment for Fires in Agrochemical Warehouses: The Role of Combustion Products , 2001 .

[18]  T. Friesen,et al.  Optimization of the Convective Air Drying of Penicillium bilaii for Improved Efficiency , 2004 .

[19]  Shahin Rafiee,et al.  Optimization of an air drying process for Artemisia absinthium leaves using response surface and artificial neural network models , 2012 .

[20]  J. Banga,et al.  Dynamic optimization of double-sided cooking of meat patties , 2003 .

[21]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[22]  Franz Durst,et al.  Dynamic optimization of multiple-zone air impingement drying processes , 2006, Comput. Chem. Eng..

[23]  Mekki Ksouri,et al.  Multi-criteria optimization in nonlinear predictive control , 2008, Math. Comput. Simul..