Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

Chun-Ming Xu, Jia-Shuai Zhang, Ling-Qiang Kong, Xue-Bo Jin (), Jian-Lei Kong, Yu-Ting Bai, Ting-Li Su, Hui-Jun Ma and Prasun Chakrabarti
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Chun-Ming Xu: School of Light Industry, Beijing Technology and Business University, Beijing 100048, China
Jia-Shuai Zhang: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Ling-Qiang Kong: School of Light Industry, Beijing Technology and Business University, Beijing 100048, China
Xue-Bo Jin: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Jian-Lei Kong: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Yu-Ting Bai: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Ting-Li Su: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Hui-Jun Ma: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Prasun Chakrabarti: Department of Computer Science and Engineering, ITM SLS Baroda University, Vadodara 391510, India

Mathematics, 2022, vol. 10, issue 22, 1-15

Abstract: Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as chemical oxygen demand (COD) and total phosphorus (TP) makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappearing or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models.

Keywords: wastewater treatment; combined normalization; codec; pollutant indicators; predict (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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