Real time optimization of solar powered direct contact membrane distillation based on multivariable extremum seeking

Abstract

Abstract-This paper presents a real time optimization scheme for a solar powered direct contact membrane distillation (DCMD) water desalination system. The sun and weather conditions vary and are inconsistent throughout the day. Therefore, the solar powered DCMD feed inlet temperature is never constant, which influences the distilled water flux. The problem of DCMD process optimization has not been studied enough. In this work, the response of the process under various feed inlet temperatures is investigated, which demonstrates the need for an optimal controller. To address this issue, we propose a multivariable Newton-based extremum seeking controller which optimizes the inlet feed and permeate mass flow rates as the feed inlet temperature varies. Results are presented and discussed for a realistic temperature profile. I. INTRODUCTIONMembrane distillation (MD) is a thermal separation technique driven by a vapor pressure difference across a hydrophobic membrane. In this process, a hot feed stream is passed along one side of a porous hydrophobic membrane. Water vapor is then transferred through the membrane to the other cooler (permeate) side. The vapor then condenses at the membrane-permeate interface and clean water is produced.MD has emerged as a sustainable water desalination technique, which offers several advantages over conventional desalination methods. In contrast to reverse osmosis, MD operates at a lower water pressure, hence is less susceptible to scaling and membrane fouling. Moreover, MD water desalination can be integrated with renewable and waste energy sources, since the feed water is not heated up to boiling temperature [1]. All of these features reduce the capital and operational cost of the MD process. Over the years, new MD configurations and membrane material were the main area of research [2]. However, very limited work has been done in terms of process control and optimization [3], [4].As it is well known, the objective is always to operate any process at optimal settings, which reduces the operational costs and guaranties the performance and stability of the system. This is true for the solar-powered MD water desalination, where the objective is to maximize the water production and reduce the energy consumption at the same time through the manipulation of the feed and permeate inlet mass flow rates.In terms of process optimization, the work in [4] developed a neural network model, which was used to calculate optimal

Hierarchical Control and Optimization Strategies Applied to Solar Membrane Distillation Facilities

“…More recently, advanced control strategies have begun to be proposed which are tasked with improving metrics related to the MD process, as to maximize the distillate production or the thermal efficiency. These techniques are mainly based on the Model Predictive Control (MPC) methodology and some examples were presented in [71][72][73].…”

Modelado y control automático en destilación por membranas solar: fundamentos y propuestas para su desarrollo tecnológico

“…En Karam and Laleg-Kirati (2015) se presentó un sistema de control en tiempo real basado en la metodología MPC y en la técnica de búsqueda del extremo de Newton. El objetivo de la estructura de control consiste en maximizar la producción de destilado actuando sobre el caudal de alimentación y teniendo en cuenta las perturbaciones de temperatura de alimentación y temperatura a la entrada al canal de evaporación del módulo.…”

“…Gil et al (2018b) Experimental Controladores PID De bajo nivel: mantener una temperatura Controladores anticipativos estable a la entrada del intercambiador de Controladores en cascada calor por la parte del campo solar. Karam and Laleg-Kirati (2015) Simulación…”

A Multivariable Controller for the Start-up Procedure of a Solar Membrane Distillation Facility

“…In this sense, a more complete control approach is the one proposed in (Karam and Laleg-Kirati, 2015), in which an optimal control algorithm was developed to regulate the feed flow rate according to the operating conditions, trying to maximize the distillate production. In (Porrazzo et al, 2013), a neural network model of the SMD facility was developed.…”