利用PSO-ANN技术进行海水淡化:综述

IF 3 Q2 ENGINEERING, CHEMICAL
Rajesh Mahadeva , Mahendra Kumar , Vishu Gupta , Gaurav Manik , Vaibhav Gupta , Janaka Alawatugoda , Harshit Manik , Shashikant P. Patole , Vinay Gupta
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引用次数: 0

摘要

水是人类、动物和植物赖以生存的自然资源。然而,只有2.5%的淡水资源可用,而剩余的97.5%是盐水,不适合人类使用。根据世界卫生组织的数据,到2050年,水资源短缺将进一步恶化。因此,许多研究人员、科学家和工程师正在该领域工作,以利用先进的处理技术改善水资源。除了多种水资源外,海水淡化在将盐水转化为淡水方面至关重要。根据国际海水淡化协会(IDA,Reuse Handbook 2022–23)最近的更新,全球约有22757家海水淡化厂在运营,每天提供10795万立方米淡水(m3/天)。此外,在这个数字时代,人工智能(AI)技术,如灰狼优化(GWO)、正余弦算法(SCA)、人工神经网络(ANN)、多元优化器(MVO)、模糊逻辑系统(FLS)、飞蛾火焰优化器(MFO)、粒子群优化(PSO)、人工蜂鸟算法(AHA)和遗传算法(GA),正在发挥着至关重要的作用,并能够对实时海水淡化厂进行深入分析,以节省时间、能源、人力和金钱。本研究的重点是对当前用于海水淡化厂的PSO-ANN技术的批判性回顾和各个方面。在这方面,Clarivate Analytics提供的科学网(WoS)的最新数据集指出,大约>;54856份海水淡化记录(1965–2023),以及大约>;全球共有180份PSO-ANN技术记录(2008-20123年)。这些记录包括研究文章、评论、会议记录、信件、书籍、章节和编辑材料。最后,这篇综述文章具体分析了PSO-ANN技术在海水淡化过程中的各种观点,促进了工厂工程师和研究人员以最小的努力和时间提高工厂性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Water desalination using PSO-ANN techniques: A critical review

Water is a natural and essential resource for humans, animals, and plants to persist. However, only ⁓2.5 % of the freshwater resources are available, while the remaining ⁓97.5 % is saline water, which is unsuitable for humanity. According to the WHO, water scarcity will worsen by 2050. As a result, numerous researchers, scientists, and engineers are working in this field to improve water resources with advanced treatment technologies. Aside from the multiple water resources, desalination is critical in converting saline water to fresh water. In line with a recent update from the International Desalination Association (IDA, Reuse Handbook 2022–23), approximately ⁓22,757 desalination plants are operating worldwide, providing ⁓107.95 million cubic meters of freshwater per day (m3/day). Furthermore, in this digital age, artificial intelligence (AI) techniques, such as gray wolf optimization (GWO), sine cosine algorithm (SCA), artificial neural networks (ANN), multi-verse optimizer (MVO), fuzzy logic systems (FLS), moth flame optimizer (MFO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA) and genetic algorithms (GA), are playing a vital role and capable of deep analysis of real-time desalination plant for saving time, energy, human efforts, and money. This study focuses on the critical review and various aspects of current-age PSO-ANN techniques for desalination plants. In this regard, recent datasets of the Web of Science (WoS), provided by Clarivate Analytics, state that about >54,856 records (1965–2023) of desalination and around > 180 records (2008–2023) of PSO-ANN techniques are available globally. These records involve research articles, reviews, proceedings, letters, books, chapters, and editorial materials. Finally, this review article is specific and analyzes the various perspectives of PSO-ANN techniques in the water desalination process, promoting plant engineers and researchers to improve plant performance with minimum effort and time.

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