An energy consumption model of wastewater treatment process based on adaptive regressive kernel function

HAN Honggui; ZHANG Lu; QIAO Junfei

doi:10.11949/j.issn.0438-1157.20151977

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An energy consumption model of wastewater treatment process based on adaptive regressive kernel function

更新时间：2025-12-10

- An energy consumption model of wastewater treatment process based on adaptive regressive kernel function
- CIESC Journal Issue 3, Pages: 947-953(2016)
- 作者机构：
  
  北京工业大学电子信息与控制工程学院,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151977
  CLC： Q028.8
- Online First：12 January 2016，
  
  Published：12 January 2016
- 稿件说明：
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HAN Honggui, ZHANG Lu, QIAO Junfei. An energy consumption model of wastewater treatment process based on adaptive regressive kernel function[J]. CIESC Journal, 2016, (3): 947-953.
DOI：

HAN Honggui, ZHANG Lu, QIAO Junfei. An energy consumption model of wastewater treatment process based on adaptive regressive kernel function[J]. CIESC Journal, 2016, (3): 947-953. DOI： 10.11949/j.issn.0438-1157.20151977.

摘要

针对污水处理过程能耗模型难以建立的问题

提出了一种基于自适应回归核函数的建模方法。通过分析污水处理过程的运行特点

构建能耗与运行过程变量之间的关系

得到一种基于过程变量的能耗模型表达;同时利用梯度下降算法对能耗模型参数进行自适应调整

提高模型精度。最后

将设计的能耗模型应用于污水处理过程基准仿真平台BSM1和实际污水处理厂

实验结果表明该模型能够根据污水处理过程变量实时获得污水处理过程的能耗

具有较好的自适应特性和较高的精度。

Abstract

Due to the difficulty in establishing the energy consumption model for the wastewater treatment process

an adaptive regressive kernel function is developed in this paper. Based on the characteristic analysis of the wastewater treatment process

the relationship between the energy consumption and the process variables is obtained. And an energy consumption model is established with the process variables. Meanwhile

the model parameters are adjusted adaptively by using the gradient descent algorithm to improve its accuracy. Finally

this proposed energy consumption model is applied to benchmark simulation platform and a real wastewater treatment process. The results show that this energy consumption model is able to display the operate cost of wastewater treatment process online according to the process variables

with good adaptivity and accuracy.

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