Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment
The aim of wastewater treatment plants (WWTPs) is to clean wastewater before it is discharged into the environment. Real-time monitoring and control will become more essential as the regulations for effluent discharges are likely to become stricter in the future. Model-based soft sensors provide a p...
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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7848 |
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| author | Henri Pörhö Jani Tomperi Aki Sorsa Esko Juuso Jari Ruuska Mika Ruusunen |
| author_facet | Henri Pörhö Jani Tomperi Aki Sorsa Esko Juuso Jari Ruuska Mika Ruusunen |
| author_sort | Henri Pörhö |
| collection | DOAJ |
| description | The aim of wastewater treatment plants (WWTPs) is to clean wastewater before it is discharged into the environment. Real-time monitoring and control will become more essential as the regulations for effluent discharges are likely to become stricter in the future. Model-based soft sensors provide a promising solution for estimating important process variables such as chemical oxygen demand (COD) and help in predicting the performance of WWTPs. This paper explores the possibility of using interpretable model structures for monitoring the influent and predicting the effluent of paper mill WWTPs by systematically finding the best model parameters using an exhaustive algorithm. Experimentation was conducted with regression models such as multiple linear regression (MLR) and partial least squares regression (PLSR), as well as LASSO regression with a nonlinear scaling function to account for nonlinearities. Some autoregressive time series models were also built. The results showed decent modelling accuracy when tested with test data acquired from a wastewater treatment process. The most notable test results included the autoregressive model with exogenous inputs for influent COD (correlation 0.89, mean absolute percentage error 8.1%) and a PLSR model for effluent COD prediction (correlation 0.77, mean absolute percentage error 7.6%) with 20 h prediction horizon. The results show that these models are accurate enough for real-time monitoring and prediction in an industrial WWTP. |
| first_indexed | 2024-03-11T01:45:48Z |
| format | Article |
| id | doaj.art-185c97ec90d4491f9ab1cd22fa77e671 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:45:48Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-185c97ec90d4491f9ab1cd22fa77e6712023-11-18T16:12:18ZengMDPI AGApplied Sciences2076-34172023-07-011313784810.3390/app13137848Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater TreatmentHenri Pörhö0Jani Tomperi1Aki Sorsa2Esko Juuso3Jari Ruuska4Mika Ruusunen5Control Engineering Research Group, Environmental and Chemical Engineering Research Unit, University of Oulu, P.O. Box 4300, 90014 Oulu, FinlandControl Engineering Research Group, Environmental and Chemical Engineering Research Unit, University of Oulu, P.O. Box 4300, 90014 Oulu, FinlandControl Engineering Research Group, Environmental and Chemical Engineering Research Unit, University of Oulu, P.O. Box 4300, 90014 Oulu, FinlandControl Engineering Research Group, Environmental and Chemical Engineering Research Unit, University of Oulu, P.O. Box 4300, 90014 Oulu, FinlandControl Engineering Research Group, Environmental and Chemical Engineering Research Unit, University of Oulu, P.O. Box 4300, 90014 Oulu, FinlandControl Engineering Research Group, Environmental and Chemical Engineering Research Unit, University of Oulu, P.O. Box 4300, 90014 Oulu, FinlandThe aim of wastewater treatment plants (WWTPs) is to clean wastewater before it is discharged into the environment. Real-time monitoring and control will become more essential as the regulations for effluent discharges are likely to become stricter in the future. Model-based soft sensors provide a promising solution for estimating important process variables such as chemical oxygen demand (COD) and help in predicting the performance of WWTPs. This paper explores the possibility of using interpretable model structures for monitoring the influent and predicting the effluent of paper mill WWTPs by systematically finding the best model parameters using an exhaustive algorithm. Experimentation was conducted with regression models such as multiple linear regression (MLR) and partial least squares regression (PLSR), as well as LASSO regression with a nonlinear scaling function to account for nonlinearities. Some autoregressive time series models were also built. The results showed decent modelling accuracy when tested with test data acquired from a wastewater treatment process. The most notable test results included the autoregressive model with exogenous inputs for influent COD (correlation 0.89, mean absolute percentage error 8.1%) and a PLSR model for effluent COD prediction (correlation 0.77, mean absolute percentage error 7.6%) with 20 h prediction horizon. The results show that these models are accurate enough for real-time monitoring and prediction in an industrial WWTP.https://www.mdpi.com/2076-3417/13/13/7848soft sensorwastewater treatmentmodellingresource efficiencyexhaustive search |
| spellingShingle | Henri Pörhö Jani Tomperi Aki Sorsa Esko Juuso Jari Ruuska Mika Ruusunen Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment Applied Sciences soft sensor wastewater treatment modelling resource efficiency exhaustive search |
| title | Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment |
| title_full | Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment |
| title_fullStr | Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment |
| title_full_unstemmed | Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment |
| title_short | Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment |
| title_sort | data based modelling of chemical oxygen demand for industrial wastewater treatment |
| topic | soft sensor wastewater treatment modelling resource efficiency exhaustive search |
| url | https://www.mdpi.com/2076-3417/13/13/7848 |
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