利用人工神经网络预测植物修复-涡旋系统去除废水中硝酸盐、磷酸盐和氨氮的效果

D. Cuevas, C. Pescos, L. Carrillo, A. Sibal, N. Guevarra
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引用次数: 0

摘要

清洁的水是人类生存的基本要素。然而,全球人口的不断增长和工业发展的无情步伐导致了水污染的惊人增长。这使得许多水体不适合饮用或使用。为了保护我们的自然资源,必须按照环境专家制定的标准处理废水。在菲律宾,《2021-19 法令》的颁布将排放要求更新为更严格的标准。因此,三级处理(如植物修复床)可作为废水处理过程的额外步骤。本研究采用的综合方法包括植物修复装置(包括使用三种植物物种和专门的土壤基质)和涡流系统。这三种植物,即澳洲葭(Phragmites australis)、岩兰草(Vetiveria zizanioides)和苘麻(Canna indica),以其去除硝酸盐、磷酸盐和氨三种污染物的能力而闻名。植物修复涡旋系统能够去除污染物,并有效降低污染物浓度,使处理后的废水达到排放标准。研究采用了人工神经网络(ANN)预测模型来评估结果。通过使用这一技术,该研究不仅旨在了解涡旋系统的复杂工作原理,还旨在优化其性能,以有效减少废水中的硝酸盐、磷酸盐和氨等污染物。这项研究是为应对水污染带来的紧迫挑战而开发可持续和高效解决方案的关键一步,从而促进为人类消费和各种工业用途提供清洁的饮用水。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of nitrate, phosphate and ammonia removal in wastewater by phytoremediation-vortex system using artificial neural network
Clean water is an essential component for human survival. However, the growing global population and the relentless pace of industrial development have resulted in an alarming increase in water contamination. This renders many bodies of water as unfit for consumption or usage. In order to safeguard our natural resources, it is imperative that wastewater should be treated to follow a standard set by environmental specialists. In the Philippines, the enactment of the DAO 2021-19 has updated the requirements for release to more stringent standards. Because of this, a tertiary treatment, such as a phytoremediation bed, can be employed as an additional step in a wastewater treatment process. This study involves a comprehensive approach that consists of a phytoremediation setup, which includes the use of three plant species and specialized soil matrices, and a vortex system. The three plant species, Phragmites australis, Vetiveria zizanioides, and Canna indica are known for their capability for removal of the three pollutants, nitrates, phosphates and ammonia. The phytoremediation-vortex system was able to remove the pollutants and effectively reduce the pollutant concentration that the treated wastewater passes the standards for release. The predictive model, artificial neural network (ANN), was employed to assess the results. By using this technique, the study aimed to not only understand the intricate workings of the vortex system but also to optimise its performance for the effective reduction of pollutants, such as, nitrates, phosphates and ammonia, in wastewater. This research represents a critical step towards developing sustainable and efficient solutions for addressing the pressing challenges posed by water pollution, thereby fostering the availability of clean and potable water for human consumption and various industrial purposes.
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