Statistical regression modeling for energy consumption in wastewater treatment

论文摘要

Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand（COD） reduction was the response variable of interest. Via the proposed approach,the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen（TN）-rich wastewater,and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving.

论文目录

• Introduction
• 1. Energy consumption modeling based on Bayesian semi-parametric quantile regression
•   1.1. Energy consumption and potential influencing factors
•   1.2. Bayesian semi-parametric quantile regression model for energy consumption
•   1.3. Data description
• 2. Results and discussion
•   2.1. Regression analysis at lower energy consumption level
•   2.2. Regression analysis at median energy consumption level
•   2.3. Regression analysis at higher energy consumption level
• 3. Conclusions

文章来源

类型: 期刊论文

作者: Yang Yu,Zhihong Zou,Shanshan Wang

来源: Journal of Environmental Sciences 2019年01期

年度: 2019

分类: 工程科技Ⅰ辑

专业: 环境科学与资源利用

单位: School of Economics and Management, Beihang University,Beijing Key Laboratory of Emergence Support Simulation Technologies for City Operations

基金: supported by the National Natural Science Foundation of China (Nos.51478025,11701023,71420107025)

分类号: X703

页码: 201-208

总页数: 8

文件大小: 189K

下载量: 59