Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes

作     者：Deepak, Malini Rustum, Rabee 

作者机构：Heriot Watt Univ Sch Energy Geosci Infrastruct & Soc Dubai CampusDubai Knowledge PkPOB 38103 Dubai U Arab Emirates 

出 版 物：《PROCESSES》 (Process.)

年 卷 期：2023年第11卷第1期

页      面：77页

核心收录：

主　　题：wastewater treatment activated sludge process optimization artificial intelligence nature-inspired algorithms bio-inspired algorithms swarm intelligence computational intelligence evolutionary algorithms 

摘      要：The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages.