Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants
Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants
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DOI：
10.3390/w13243507
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发表时间：
2021-12
期刊：
影响因子：
3.4
通讯作者：
Animesh Bhattacharya;Saswata Sahu;Venkatesh Telu;Srimanti Duttagupta;S. Sarkar;J. Bhattacharya;
中科院分区：
文献类型：
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作者：
Animesh Bhattacharya;Saswata Sahu;Venkatesh Telu;Srimanti Duttagupta;S. Sarkar;J. Bhattacharya;
经过几十年对砷治理的广泛研究，已经开发出了大量的技术，尽管该领域除砷工厂的技术和财务前景需要进行严格的评估。在本研究中，重点是在印度西孟加拉，一些明显的砷影响的地区，我们评估了不同的砷去除技术的实施和操作使用的4000个时空数据集收集的136个砷去除厂从事公共供水的深入实地调查。我们对该数据集的统计分析表明，2014-2021年期间，工厂的平均累积产能增长了120%。大多数工厂都是基于活性氧化铝与FeCl 3技术，服务于研究区域约49%的人口。采用FeCl 3技术的活性氧化铝的平均水生产成本为7.56美元/m3（1美元/70卢比），而颗粒氢氧化铁技术的最低成本为0.39美元/m3。采用基于机器学习的框架来分析水质和处理厂参数对工厂的去除效率，资本和运营成本的影响。人工神经网络模型表现出足够的统计显著性，资本成本模型的F值和R2分别为5830.94和0.72，运营成本模型的F值和R2分别为136，954和0.98。过程变量的相对重要性通过随机森林模型确定。该模型表明，流速，媒体和化学品是主要的成本，而污染物负荷在进水和消毒剂是重要的去除效率。所建立的框架可能有助于作为一个决策工具，供水商评估预期的性能和财政参与拟议或正在进行的砷去除厂有关各种设计和质量参数。
A plethora of technologies has been developed over decades of extensive research on arsenic remediation, although the technical and financial perspective of arsenic removal plants in the field requires critical evaluation. In the present study, focusing on some of the pronounced arsenic-affected areas in West Bengal, India, we assessed the implementation and operation of different arsenic removal technologies using a dataset of 4000 spatio-temporal data collected from an in-depth field survey of 136 arsenic removal plants engaged in the public water supply. Our statistical analysis of this dataset indicates a 120% rise in the average cumulative capacity of the plants during 2014–2021. The majorities of the plants are based on the activated alumina with FeCl3 technology and serve about 49% of the population in the study area. The average cost of water production for the activated alumina with FeCl3 technology was found to be ₹7.56/m3 (USD $1 ≈ INR ₹70), while the lowest was ₹0.39/m3 for granular ferric hydroxide technology. A machine learning-based framework was employed to analyze the impact of water quality and treatment plant parameters on the removal efficiency, capital, and operational cost of the plants. The artificial neural network model exhibited adequate statistical significance, with a high F-value and R2 of 5830.94 and 0.72 for the capital cost model, 136,954, and 0.98 for the operational cost model, respectively. The relative importance of the process variables was identified through random forest models. The models indicated that flow rate, media, and chemicals are the predominant costs, while contaminant loading in influent water and a coagulating agent was important for removal efficiency. The established framework may be instrumental as a decision-making tool for water providers to assess the expected performance and financial involvement for proposed or ongoing arsenic removal plants concerning various design and quality parameters.