Work

Series-connected data-based model to estimate effluent chemical oxygen demand in industrial wastewater treatment process

Year: 2024

Type: article

Abstract: Continuous monitoring of chemical oxygen demand (COD) is essential to ensure efficient and sustainable wastewater treatment and regulatory compliance. However, traditional hardware measurements are laborious, infrequent and costly. In this research, a cost-effective real-time alternative is presented. ARMAX, ARX, Neural Network and PLSR model structures were identified and tested for finding data-based model for real-time estimation of effluent COD in a full-scale industrial WWTP. To aim for estimating effluent COD without physically measuring it, a novel chain of two estimators was created by connecting in series the identified influent and effluent COD models. A comprehensive and systematic model identification was carried out to find the model inputs, delays, parameters and training windows using an exhaustive search algorithm. The results showed that using solely linear model structure it is possible to identify sufficiently accurate (R: 0.67, MAPE: 7.33%) and practical (interpretable and implementable) data-based estimation model which has predictive ability even up to 20 h horizon. As the series-connected model structure reaches the required margin of error it has potential for real-world industrial usage alongside or even replacing the hardware online sensor. Estimation model enhances resiliency and provides real-time insights into effluent quality in varying operating conditions and during unexpected disturbances. • A novel chain of two data-based COD estimators was presented. • An exhaustive search algorithm was used for comprehensive and systematic model identification. • Continuously measured data from a full-scale industrial WWTP was applied. • Series-connected model provides a cost-effective way for monitoring COD in real-time applications.

Source: Journal of Environmental Management

DOI: 10.1016/j.jenvman.2024.123680

Authors: Jani Tomperi, Aki Sorsa, Jari Ruuska, Mika Ruusunen

Institution: University of Oulu

Language: English

Topic: Water Quality Monitoring and Analysis

Subfield: Waste Management and Disposal

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Responsible consumption and production

Open Access status: hybrid

Estimated APC paid: $3,850