Multi-objective optimization of batch electrodialysis for minimizing energy consumption by using non-dominated sorting genetic algorithm (NSGA-II)

Abstract

From the solid waste of palm oil, i.e. empty fruit bunch (EFB), sugar can be produced through the hydrolysis process by using hydrochloric acid (HCl). The acid must be separated from the sugar to produce pure sugar and to reduce the processing costs by recovering and recycling the hydrochloric acid. Electrodialysis (ED) which is a membrane separation characterized by an electrical field orthogonal to the membrane, is a feasible method for acid recovery. In the ED process, there are conflicting objective functions, i.e. maximum concentrated and minimum energy consumption which cannot be solved by single objective optimization technique. Simultaneous optimization of these objectives yield in a multi-objective optimization (MOO) problem, which is characterized by a set of multiple solutions, known as pareto solutions. In this work, a non-dominated sorting genetic algorithm (NSGA-II) approach was used to generate the pareto solutions for two objectives: maximize concentrated acid and minimize energy consumption, for the batch ED. Each point of Pareto solutions consists of different optimal current density and flowrate profiles, which lead to distinct amount of energy consumption and acid concentration. These solutions give flexibility in evaluating the trade-offs and selecting the most suitable operating policy