School of Environment, College of Engineering, University of Tehran, Tehran, Iran.
Center of Excellence in Geomatics Eng. in Disaster Management, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Chemosphere. 2021 May;270:129465. doi: 10.1016/j.chemosphere.2020.129465. Epub 2021 Jan 2.
In this research, twelve linear and nonlinear regression models were performed and evaluated to formulate the best one for the estimation of chemical oxygen demand level in the effluent of the clarifier unit of a petrochemical wastewater treatment plant. The input variables measured twice a day in the influent of the biological unit over a period of 13 months using standard methods. The piece-wise linear regression with breakpoint method, with a mean squared error value equal to 0.041, mean absolute error of 0.144, and correlation coefficient equal to 0.835 was found to estimate the output chemical oxygen demand parameter more sustainable rather than other linear and nonlinear methods. However, some of the other applied models such as radial basis function neural network and gene expressing programming models achieved good performance considering their correlation coefficient, robustness in presence of outliers, mean squared error and mean absolute error test. Mathematical and intelligent modeling proved useful as an accurate alternative to estimate the amount of chemical oxygen demand rather than spending time and cost for its laboratory tests.
在这项研究中，进行了十二种线性和非线性回归模型，并对其进行了评估，以制定出最适合于估算石化废水处理厂沉淀池出水化学需氧量水平的模型。输入变量是在生物单元进水口使用标准方法每天测量两次，测量时间为 13 个月。通过分段线性回归与断点法，得到的均方误差值为 0.041，平均绝对误差为 0.144，相关系数为 0.835，这表明该方法比其他线性和非线性方法更能持续地估计输出化学需氧量参数。然而，其他一些应用模型，如径向基函数神经网络和基因表达编程模型，考虑到它们的相关系数、在存在异常值时的稳健性、均方误差和平均绝对误差测试，也取得了良好的性能。数学和智能建模被证明是一种有用的替代方法，可以准确地估算化学需氧量的量，而无需花费时间和成本进行实验室测试。
