Arsenic (As) in groundwater has become a worldwide concern due to its high toxicity as it is classified as a potent carcinogen, when exposed to drinking water. None of the reported As remediation technologies has so far been reasonably successful in overcoming the constraints encountered during on-field application. This paper evaluated the potential of modifying the well-acclaimed ElectroChemical Arsenic Remediation (ECAR) technique from ‘Batch Scale’ to a ‘Continuous Flow’ operation. The development of this innovative process modification involved a thorough investigation to understand the effects of different process parameters like, iron concentration, pH, dissolved oxygen (DO) and presence of co-occurring solute (PO43−). An indigenously fabricated 50 L continuous ECAR unit exhibited a successful remediation from 604 ppb of As(III) concentration to a value (% removal efficiency greater than 99%) less than the maximum contamination level prescribed by World Health Organization. Powder X-ray diffraction (PXRD) and scanning electron microscope (SEM) studies were conducted for iron-arsenic dry sludge to show a highly crystalline nature of the sludge, produced in the process. The experimental results were explored for the formulation of a feed-forward back-propagation artificial neural network (ANN) model to predict the removal efficiency of As from the contaminated water. The experimental outcome of the study justifies the feasibility and significance of the ‘continuous mode’ ECAR system in providing a tangible and sustainable solution towards global As contamination problem.

中文翻译：

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提供切实可行的无砷饮用水：基于实验研究和人工神经网络模拟，提供了连续规模的电化学砷修复工艺

地下水中的砷 (As) 因其高毒性而成为全球关注的问题，因为它在暴露于饮用水时被归类为强致癌物。迄今为止，所报道的 As 修复技术都没有在克服现场应用过程中遇到的限制方面取得相当成功。本文评估了将广受好评的电化学砷修复 (ECAR) 技术从“批次规模”改进为“连续流动”操作的潜力。这种创新工艺改进的开发涉及彻底调查以了解不同工艺参数的影响，例如铁浓度、pH、溶解氧 (DO) 和共存溶质 (PO43−) 的存在。一个本土制造的 50 L 连续 ECAR 装置展示了从 604 ppb 的 As(III) 浓度到低于世界卫生组织规定的最大污染水平的值（% 去除效率大于 99%）的成功修复。对铁砷干污泥进行了粉末 X 射线衍射 (PXRD) 和扫描电子显微镜 (SEM) 研究，以显示该过程中产生的污泥具有高度结晶的性质。对实验结果进行了探索，以制定前馈反向传播人工神经网络 (ANN) 模型，以预测污染水中 As 的去除效率。