Application of Machine Learning for the Development of the Next Generation of Membrane Material for Water Treatment Purposes.

根据多份报告，全球变暖和气候变化的后果，包括严重干旱、盐水侵入地下水和冰川萎缩，以及经济发展、快速城市化和人口增长导致的水需求增加，预计到2030年全球缺水率将达到40%。因此，除了改善水资源的保护、分配和管理外，确保新的淡水来源随时可用也是满足日益增长的需求的关键。由于其丰富的资源，海水淡化和废水处理及回用已被提议作为解决水资源短缺问题的一种方案。使用膜技术进行水处理是解决水资源短缺的一种有吸引力的解决方案，因为它们消耗很少的能源，运行效率高，占地面积小，几乎不需要维护。这些解决方案中的一些已经在工业规模上应用，包括反渗透（RO）、纳滤（NF）、超滤（UF）、微滤（MF）、膜蒸馏（MD）、电渗析、正渗透（FO）。尽管最近在这一领域取得了所有进展，但严重的问题仍未得到解决。其中包括RO膜的高能量需求和污染、浓差极化和FO的反向溶质通量（RSF）。可以通过优化工艺参数和条件来缓解上述问题中的一些问题的影响;然而，其中一些问题（例如内部浓差极化）是膜本身固有的，并且受到膜材料的限制。因此，为了进一步改善这种膜的性能，有必要考虑用于膜合成的不同材料以克服当前的功能限制。尽管将不同聚合物组合应用于膜合成的想法似乎是可行的，但其可能是相当长、繁琐和昂贵的过程，并且由于时间和资源有限，总是存在忽略合格候选物的可能性。已经越来越多地应用于优化工艺参数以及预测、模拟和/或建模输入和输出。在机器学习中，计算算法用于基于数据开发这些模型。它可以使用学习机制来分析输入变量和输出变量之间的关系，而不是使用数学模型方程，并使用该方法以高精度预测复杂的非线性系统。虽然大多数研究都集中在通过改变操作条件来优化工艺，迄今为止，还没有全面的研究调查不同膜材料和聚合物的应用对系统性能的影响。本研究的目的是调查膜制造和材料的可能发展，以克服膜水处理工艺性能的限制，膜的固有特性。在这方面，将实施所使用的不同膜的综合数据库来训练算法，随后将进行模拟以预测用作膜时各种可能的材料组合的性能。该过程可以显著减少与在实验室中测试所有候选材料并将具有期望性能的材料列入短名单相关的时间和资源量。在下一步中，将在真实的条件下制造和测试在CP、污垢、截留率、水回收率等方面具有显著性能的潜在候选物，以验证模拟结果。

According to several reports, consequences of global warming and climate change including severe droughts, saltwater intrusion into groundwater, and shrinkage of glaciers, in addition to the increased water demand due to economic developments, fast urbanization, and population increase, have combined to result in an estimated 40% global water shortage by the year 2030. Hence, in addition to improvements in the conservation, distribution, and management of water resources, ensuring that new sources of fresh water can be readily available is essential in order to meet the increasing demand. As a result of its abundance, seawater desalination and wastewater treatment and reuse have been proposed as a solution to the problem of water deficit.Water treatment using membrane technologies is an attractive solution to water shortages since they consume little energy, operate efficiently, have a small footprint, and require little maintenance. Some of these solutions have been applied on an industrial scale including Reverse osmosis (RO), Nanofiltration (NF), Ultrafiltration (UF), Microfiltration (MF), Membrane distillation (MD), Electrodialysis, Forward osmosis (FO). Despite all the recent progress in this field, serious issues remain unaddressed yet. High energy requirement and fouling in RO membranes, concentration polarization, and reverse solute flux (RSF) for FO are among these. It is possible to alleviate the effect of some of the mentioned issues by optimizing the process parameters and conditions; however, some of them such as internal concentration polarization are intrinsic to the membrane itself and the limitations imposed by the membrane material. Consequently, in order to further improve the performance of such membranes it is necessary to consider different materials for membrane synthesis to overcome the current functional limitations. Although the idea of applying different combinations of polymers for membrane synthesis seems feasible it could be a rather long, tedious, and expensive process and there would always be the possibility of overlooking eligible candidates due to limited time and resources.Recently, A number of machine learning (ML) techniques, such as response surface methodologies (RSMs) and artificial neural networks (ANNs), have been increasingly applied to optimize process parameters as well as to predict, simulate, and/or model inputs and outputs. In machine learning, computational algorithms are used to develop these models based on data. It is possible to analyze the relationships between input variables and output variables using a learning mechanism, rather than using mathematical model equations, and to predict complex non-linear systems with high precision using this method. While most studies have focused on the optimization of processes by modifying operating conditions, to this date there are no comprehensive studies investigating the effect of the application of different membrane materials and polymers on the performance of the system.This study would aim to investigate the possible developments in membrane fabrication and material in order to overcome the limitations imposed on the membrane water treatment process performance due to the intrinsic characteristics of membranes. In this regard, a comprehensive database of different membranes used will be implemented to train the algorithm, and later on, simulations will be carried out to predict the performance of various possible combinations of material when used as a membrane. This process can significantly reduce the amount of time and resources related to testing all the candidate material in the laboratory and shortlisting the ones with desired performance. In the next step, potential candidates with noticeable performance regarding CP, fouling, rejection rate, water recovery rate, etc. will be fabricated and tested in real conditions to validate the simulation results.