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Machine Learning Models for Predicting Water Quality of Treated Fruit and Vegetable Wastewater
Water ( IF 3.4 ) Pub Date : 2021-09-10 , DOI: 10.3390/w13182485
Gurvinder Mundi , Richard G. Zytner , Keith Warriner , Hossein Bonakdari , Bahram Gharabaghi

Wash-waters and wastewaters from the fruit and vegetable processing industry are characterized in terms of solids and organic content that requires treatment to meet regulatory standards for purpose-of-use. In the following, the efficacy of 13 different water remediation methods (coagulation, filtration, bioreactors, and ultraviolet-based methods) to treat fourteen types of wastewater derived from fruit and vegetable processing (fruit, root vegetables, leafy greens) were examined. Each treatment was assessed in terms of reducing suspended solids, total phosphorus, nitrogen, biochemical and chemical oxygen demand. From the data generated, it was possible to develop predictive modeling for each of the water treatments tested. Models to predict post-treatment water quality were studied and developed using multiple linear regression (coefficient of determination (R2) of 30 to 83%), which were improved by the generalized structure of group method of data handling models (R2 of 73–99%). The selection of multiple linear regression and the generalized structure of group method of data handling models was due to the ability of the models to produce robust equations for ease of use and practicality. The large variability and complex nature of wastewater quality parameters were challenging to represent in linear models; however, they were better suited for group method of data handling technique as shown in the study. The model provides an important tool to end users in selecting the appropriate treatment based on the original wastewater characteristics and required standards for the treated water.

中文翻译:

用于预测处理过的水果和蔬菜废水的水质的机器学习模型

来自水果和蔬菜加工业的洗涤水和废水的特征在于固体和有机含量,需要对其进行处理以满足使用目的的监管标准。在下文中,研究了 13 种不同的水修复方法(混凝、过滤、生物反应器和基于紫外线的方法)处理来自水果和蔬菜加工(水果、根茎类蔬菜、绿叶蔬菜)的 14 种废水的功效。根据减少悬浮固体、总磷、氮、生化和化学需氧量来评估每个处理。根据生成的数据,可以为每个测试的水处理开发预测模型。2 ) of 30 to 83%),这通过数据处理模型的分组方法的广义结构得到改进(R 2 of 73-99%)。选择多元线性回归和数据处理模型的分组方法的广义结构是由于模型能够产生稳健的方程以便于使用和实用性。废水质量参数的巨大可变性和复杂性很难用线性模型来表示;然而,它们更适合研究中显示的数据处理技术的分组方法。该模型为最终用户提供了一个重要的工具,可以根据原始废水特性和处理水的要求标准选择合适的处理方法。
更新日期:2021-09-10
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