Mathematical and optimization modelling in desalination: State-of-the-art and future direction

Abstract

The growing water demand across the world necessitates the need for new and improved processes as well as for a better understanding of existing processes. This level of understanding includes predicting system performance in scenarios that cannot always be evaluated experimentally. Mathematical modelling is a crucial component of designing new and improved engineering processes. Through mathematically modelling real life systems, we gain a deeper understanding of processes while being able to predict performance more effectively. Advances in computational capacity and the ease of assessing systems allow researchers to study the feasibility of various systems. Mathematical modelling studies enable optimization performance parameters while minimizing energy requirements and, as such, have been an active area of research in desalination. In this review, the most recent developments in mathematical and optimization modelling in desalination are discussed with respect to transport phenomena, energy consumption, fouling predictions, and the integration of multiple scaling evolution on heat transfer surfaces has been reviewed. Similarly, developments in optimization of novel reverse osmosis (RO) configurations have been analyzed from an energy consumption perspective. Transport models for membranebased desalination processes, including relatively less understood processes such as nanofiltration and forward osmosis are presented, with recent modifications to allow for different solutes and solutions. Mathematical modelling of hybrid systems integrated with RO has also been reviewed. A survey of the literature shows that mathematical and optimization modelling of desalination processes is an exciting area for researchers in which future scholarship includes coupling of renewable energy systems with desalination technologies, as well as more advanced descriptions of fouling evolution other than that of cake filtration in membrane-based processes.

A Review of CFD Modelling and Performance Metrics for Osmotic Membrane Processes

“…These include better prediction of the membrane performance for a range of membrane properties and operating conditions, and deeper understanding of the effects of flow dynamics and water transport mechanisms. Thus, mathematical modelling is an important element which can complement experimental studies in designing optimal operating conditions or configurations [ 44 , 45 ].…”

“…In recent years Ahmed et al [ 45 ] reviewed the advances in modelling and optimisation for desalination in the context of transport phenomena, energy usage, fouling and hybrid desalination technologies. However, their review only considered simple transport phenomena without taking into account the current advent of computational fluid dynamics (CFD) and multi-scale modelling techniques for membrane processes.…”

Commercial Thermal Technologies for Desalination of Water from Renewable Energies: A State of the Art Review

“…Table 1 shows the energy required to produce 1 m 3 of fresh water from distinct types of water sources. Generally, water desalination processes can be classified into phase change or thermal processes, and processes without phase change or by membranes [1,4,7,[16][17][18][19][20]. The phase or thermal change process involves evaporation of salt water by contact with a heating surface (evaporation surface) leaving the salts in it; then, the fresh water vapor condenses in cooling pipes producing high-pressure water with quality and without salts [21].…”

Optimization of Wind Energy Battery Storage Microgrid by Division Algorithm Considering Cumulative Exergy Demand for Power-Water Cogeneration

“…The 35% growth of the world population between 2015 and 2050 not only increases the demand for energy resources but also, leads to the rapid increase in the demand for freshwater [1]. Freshwater resources on Earth are limited, and to cope with the global freshwater demand, desalination is introduced as one of the most feasible solutions [2].…”