Operation of Medium-size Reverse Osmosis Plants with Optimal Energy Consumption

Abstract This paper deals with the optimal operational strategy of a reverse osmosis (RO) plant for remote sites. The electricity supply to these plants comes usually from renewable energies (wind and solar), when they are not temporarily available, they are complemented by a diesel generator and batteries. The water demand of small settlements in arid regions suffers strong variations along a day. As the higher demand of water usually occurs when solar energy is more available, the operational expenses can be reduced by considering the RO plant as an active load. A good control strategy, will implement a variable operation point, taking into account the predictions of water demand, the expected variation of the available energy sources and the scheduling of cleaning operations in the RO plant, in order to optimize the energy use. In this paper a hybrid predictive control is proposed to implement this task. Simulations of a specific plant show that an adequate operation reduces the diesel energy consumption, while satisfying the variable water demand.

[1]  Efstratios N. Pistikopoulos,et al.  C.A. Floudas, Nonlinear and Mixed-Integer Optimization. Fundamentals and Applications , 1998, J. Glob. Optim..

[2]  Ahmed Hannachi,et al.  Reconsidering water scaling tendency assessment , 2006 .

[3]  H. Roques,et al.  Prediction of the limit of the metastable zone in the CaCO3-CO2-H2O system , 2004 .

[4]  C. Floudas.  Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications , 1995 .

[5]  Thomas Melin,et al.  State-of-the-art of reverse osmosis desalination , 2007 .

[6]  César de Prada,et al.  Library for dynamic simulation of reverse osmosis plants , 2008 .

[7]  Fernando Tadeo,et al.  Operation of desalination plants using renewable energies and hybrid control , 2011 .

[8]  Essameddin Badreddin,et al.  Dynamic modelling of MSF plants for automatic control and simulation purposes: a survey , 2004 .

[9]  R. Baker.  Membrane Technology and Applications , 1999 .

[10]  Stefano Alvisi,et al.  A short-term, pattern-based model for water-demand forecasting , 2006 .

[11]  Mark C. Porter,et al.  Handbook of Industrial Membrane Technology , 2007 .

[12]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[13]  E. Badreddin,et al.  Dynamic Modeling of a Simple Reverse Osmosis Desalination Plant for Advanced Control Purposes , 2007, 2007 American Control Conference.

[14]  Fernando Tadeo,et al.  Integrated design using dynamic simulation of reverse osmosis plants , 2009, 2009 IEEE International Conference on Industrial Engineering and Engineering Management.

[15]  Fernando Tadeo,et al.  New dynamic library of reverse osmosis plants with fault simulation , 2011 .

[16]  César de Prada,et al.  Hybrid NMPC of supermarket display cases , 2009 .

[17]  P. I. Barton,et al.  Parametric sensitivity functions for hybrid discrete/continuous systems , 1999 .

[18]  Abderrahim Abbas,et al.  Modeling an industrial reverse osmosis unit , 1999 .

[19]  Eduardo F. Camacho,et al.  Optimization in reverse osmosis plants through risk mitigation , 2009, 2009 European Control Conference (ECC).

[20]  John V. Ringwood,et al.  A predictive controller for a heaving buoy producing potable water , 2009, 2009 European Control Conference (ECC).

[21]  Hisham Ettouney,et al.  Process control in water desalination industry: an overview☆ , 1999 .

[22]  Fernando Tadeo,et al.  Membrane Modeling for Simulation and Control of Reverse Osmosis in Desalination Plants , 2008 .

[23]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..