Suppr超能文献

整合人工智能建模与膜技术用于深度污水处理:研究进展与未来展望

Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives.

作者信息

Cairone Stefano, Hasan Shadi W, Choo Kwang-Ho, Li Chi-Wang, Zarra Tiziano, Belgiorno Vincenzo, Naddeo Vincenzo

机构信息

Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy.

Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical and Petroleum Engineering, Khalifa University of Science and Technology, PO, Box 127788, Abu Dhabi, United Arab Emirates.

出版信息

Sci Total Environ. 2024 Sep 20;944:173999. doi: 10.1016/j.scitotenv.2024.173999. Epub 2024 Jun 13.

Abstract

Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.

摘要

膜技术已成为先进废水处理的有效替代方案,可确保高效去除污染物并实现可持续的资源回收。尽管取得了重大进展,但目前的研究仍致力于进一步优化处理性能。在面临的挑战中,膜污染仍然是膜技术中的一个重要障碍,因此需要开发更有效的缓解策略。广泛用于预测处理性能的数学模型,由于废水的复杂多变性,通常准确性较低且存在不确定性。为了克服这些局限性,许多研究提出了人工智能(AI)建模,以准确预测膜技术的性能和污染机制。这种方法旨在提供先进的模拟和预测,从而加强过程控制、优化和强化。本文献综述探讨了通过人工智能模型对基于膜的废水处理过程进行建模的最新进展。分析突出了该研究领域在提高膜技术效率方面的巨大潜力。讨论了人工智能建模在定义最佳操作条件、制定有效的膜污染缓解策略、提高新型膜基技术的性能以及改进膜制造技术方面的作用。这些由人工智能建模驱动的强化过程优化和控制策略可确保提高出水质量、优化资源消耗并降低运营成本。研究了这种前沿方法对向可持续废水处理范式转变的潜在贡献。最后,本综述概述了未来展望,强调了需要关注的研究挑战,以克服目前阻碍人工智能建模在废水处理厂中应用的局限性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验