用神经网络优化工业废水处理中的出水

Matviichuk M
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摘要

地球人口的增长导致获取淡水的问题日益严重。地球上的主要水源是咸水和海水。针对水危机,水净化成为一个极其重要的过程,其成果是通过海水淡化和各种水处理方法来实现的。在此背景下,研究利用神经网络改善污水处理厂运行的可能性是必要的。本研究的目的是对工业废水处理设施的工作效率进行优化和分析。采用软计算方法对模型进行优化。在本研究中,使用分析和比较的方法确定了神经网络应用的确切结果。处理工业中产生的所有废水和废物的处理涉及许多过程,包括气浮、化学混凝、沉淀和使用完全混合的活性污泥的生物处理。已经考虑了各种学习函数,包括前向传播人工神经网络(ann),如多层感知器(MLP),级联前向传播ann和支持向量回归(SVR)模型。学习过程包括使用Levenberg-Marquardt优化算法和顺序最小。文章还提供了图形图像,说明不同类型的污染物,与处理厂相关的成本,以及处理过程后观察到的废水颜色变化。得到的结果表明,预测数据与实验数据高度相似,强调了反向传播人工神经网络模型在准确预测方面的有效性。此外,将机器学习整合到洗涤剂的生产中,可以非常有效地促进水资源的高效和可持续利用。总的来说,这篇论文为利用机器学习解决淡水短缺问题提供了有价值的见解
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of effluents using a neural network in the treatment of industrial wastewater
The growth of the planet's population leads to an increase in the problem of access to fresh water. The main sources of water on Earth are brackish and sea water. In connection with the water crisis, water purification becomes an extremely important process, and its achievement is carried out through desalination and various methods of water treatment. In this context, research into the possibility of using neural networks to improve the operation of sewage treatment plants is necessary. The purpose of the research was to optimize and analyze the efficiency of the work of treatment facilities in the treatment of industrial wastewater. Soft computing methods were used to optimize the proposed models. In this study, the exact results of the application of the neural network were determined using analytical and comparative approaches. Treatment of all wastewater and waste generated in the treatment industry involves a number of processes including air flotation, chemical coagulation, settling and biological treatment using fully mixed activated sludge. Various learning functions have been considered, including forward-propagation artificial neural networks (ANNs) such as multilayer perceptron (MLP), cascaded forward-propagation ANNs, and support vector regression (SVR) models. The learning process includes the use of Levenberg-Marquardt optimization algorithms and sequential minimum. The article also provides graphical images illustrating the different types of pollutants, the costs associated with treatment plants, and the color changes in wastewater observed after the treatment process. The obtained results show a high degree of similarity between the predicted and experimental data, which emphasizes the effectiveness of the backpropagation ANN model for accurate predictions. In addition, the integration of machine learning into the production of detergents can be extremely effective in promoting the efficient and sustainable use of water resources. Overall, the paper provides valuable insights into the use of machine learning to address freshwater scarcity
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