Sultan Moulay Slimane University of Beni Mellal, Research Group in Environmental Sciences and Applied Materials (SEMA), FP Khouribga, Morocco.
Sultan Moulay Slimane University of Beni Mellal, Research Group in Environmental Sciences and Applied Materials (SEMA), FP Khouribga, Morocco.
Sci Total Environ. 2022 Feb 10;807(Pt 1):150554. doi: 10.1016/j.scitotenv.2021.150554. Epub 2021 Sep 28.
During the last few years, important advances have been made in big data exploration, complex pattern recognition and prediction of complex variables. Machine learning (ML) algorithms can efficiently analyze voluminous data, identify complex patterns and extract conclusions. In chemical engineering, the application of machine learning approaches has become highly attractive due to the growing complexity of this field. Machine learning allows computers to solve problems by learning from large data sets and provides researchers with an excellent opportunity to enhance the quality of predictions for the output variables of a chemical process. Its performance has been increasingly exploited to overcome a wide range of challenges in chemistry and chemical engineering, including improving computational chemistry, planning materials synthesis and modeling pollutant removal processes. In this review, we introduce this discipline in terms of its accessible to chemistry and highlight studies that illustrate in-depth the exploitation of machine learning. The main aim of the review paper is to answer these questions by analyzing physicochemical processes that exploit machine learning in organic and inorganic pollutants removal. In general, the purpose of this review is both to provide a summary of research related to the removal of various contaminants performed by ML models and to present future research needs in ML for contaminant removal.
在过去的几年中，在大数据探索、复杂模式识别和复杂变量预测方面取得了重要进展。机器学习 (ML) 算法可以有效地分析大量数据，识别复杂模式并得出结论。在化学工程中，由于该领域的复杂性不断增加，机器学习方法的应用变得极具吸引力。机器学习允许计算机通过从大数据集中学习来解决问题，并为研究人员提供了一个极好的机会来提高化学过程输出变量预测的质量。它的性能越来越多地被用于克服化学和化学工程中的一系列挑战，包括改进计算化学、规划材料合成和模拟污染物去除过程。在这篇综述中，我们根据其对化学的可及性介绍了这门学科，并强调了说明深入利用机器学习的研究。综述论文的主要目的是通过分析利用机器学习去除有机和无机污染物的物理化学过程来回答这些问题。总的来说，本综述的目的既是对 ML 模型去除各种污染物的相关研究进行总结，也是对 ML 去除污染物的未来研究需求进行介绍。
