Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption
Benchmarking electricity consumption of wastewater treatment plants (WWTPs) is fundamental for sustainable wastewater management, as these facilities have a concomitant electricity-intensive nature along with their pollutant removal and resource recovery functions. Due to the challenge of characteri...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Water Research X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S258991472500009X |
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| author | Minjian Li Chongqiao Tang Junhan Gu Nianchu Li Ahemaide Zhou Kunlin Wu Zhibo Zhang Hui Huang Hongqiang Ren |
| author_facet | Minjian Li Chongqiao Tang Junhan Gu Nianchu Li Ahemaide Zhou Kunlin Wu Zhibo Zhang Hui Huang Hongqiang Ren |
| author_sort | Minjian Li |
| collection | DOAJ |
| description | Benchmarking electricity consumption of wastewater treatment plants (WWTPs) is fundamental for sustainable wastewater management, as these facilities have a concomitant electricity-intensive nature along with their pollutant removal and resource recovery functions. Due to the challenge of characterizing influent water quality using traditional methods, satisfactory benchmarks have long been elusive. To overcome the complexity of wastewater compositions, an unsupervised machine learning algorithm, spectral clustering, is introduced to analyze 2,576 WWTPs across China, effectively characterizing influent quality as a single variable and contributing to robust benchmarks with 75 % of the fittings achieving coefficients of determination (R2) >0.85. The benchmarks are established with four critical parameters influencing electricity consumption: scale, influent quality, discharge standard and treatment process. Regional variations of the four parameters and their effects on regional WWTP electricity consumption are elaborated. Results indicate that the overall influent concentration characterized by spectral clustering is the major influencing factor of regional WWTP annual average electricity consumption per unit of volume (UEC). The findings not only enhance understanding of WWTP electricity consumption patterns and provide a scalable model for wider application, but also demonstrate a novel methodology for addressing multi-variable problems. |
| format | Article |
| id | doaj-art-900de71127f04d9a916e96d41838921f |
| institution | Kabale University |
| issn | 2589-9147 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Water Research X |
| spelling | doaj-art-900de71127f04d9a916e96d41838921f2025-02-06T05:12:39ZengElsevierWater Research X2589-91472025-01-0126100309Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumptionMinjian Li0Chongqiao Tang1Junhan Gu2Nianchu Li3Ahemaide Zhou4Kunlin Wu5Zhibo Zhang6Hui Huang7Hongqiang Ren8State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR ChinaCorresponding author at: School of the Environment, Nanjing University, N.O.163, Xianlin Avenue, Qixia District, Nanjing 210023, Jiangsu, PR China.; State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR ChinaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR ChinaBenchmarking electricity consumption of wastewater treatment plants (WWTPs) is fundamental for sustainable wastewater management, as these facilities have a concomitant electricity-intensive nature along with their pollutant removal and resource recovery functions. Due to the challenge of characterizing influent water quality using traditional methods, satisfactory benchmarks have long been elusive. To overcome the complexity of wastewater compositions, an unsupervised machine learning algorithm, spectral clustering, is introduced to analyze 2,576 WWTPs across China, effectively characterizing influent quality as a single variable and contributing to robust benchmarks with 75 % of the fittings achieving coefficients of determination (R2) >0.85. The benchmarks are established with four critical parameters influencing electricity consumption: scale, influent quality, discharge standard and treatment process. Regional variations of the four parameters and their effects on regional WWTP electricity consumption are elaborated. Results indicate that the overall influent concentration characterized by spectral clustering is the major influencing factor of regional WWTP annual average electricity consumption per unit of volume (UEC). The findings not only enhance understanding of WWTP electricity consumption patterns and provide a scalable model for wider application, but also demonstrate a novel methodology for addressing multi-variable problems.http://www.sciencedirect.com/science/article/pii/S258991472500009XWastewater treatment plantElectricity consumptionBenchmarkingMachine learningInfluent indicatorRegional analysis |
| spellingShingle | Minjian Li Chongqiao Tang Junhan Gu Nianchu Li Ahemaide Zhou Kunlin Wu Zhibo Zhang Hui Huang Hongqiang Ren Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption Water Research X Wastewater treatment plant Electricity consumption Benchmarking Machine learning Influent indicator Regional analysis |
| title | Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption |
| title_full | Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption |
| title_fullStr | Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption |
| title_full_unstemmed | Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption |
| title_short | Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption |
| title_sort | machine learning driven benchmarking of china s wastewater treatment plant electricity consumption |
| topic | Wastewater treatment plant Electricity consumption Benchmarking Machine learning Influent indicator Regional analysis |
| url | http://www.sciencedirect.com/science/article/pii/S258991472500009X |
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