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Guo S, Ao X, Ma X, Cheng S, Men C, Harada H, Saroj DP, Mang HP, Li Z, Zheng L. Machine-learning-aided application of high-gravity technology to enhance ammonia recovery of fresh waste leachate. WATER RESEARCH 2023; 235:119891. [PMID: 36965295 DOI: 10.1016/j.watres.2023.119891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 02/27/2023] [Accepted: 03/17/2023] [Indexed: 06/18/2023]
Abstract
Stripping is widely applied for the removal of ammonia from fresh waste leachate. However, the development of air stripping technology is restricted by the requirements for large-scale equipment and long operation periods. This paper describes a high-gravity technology that improves ammonia stripping from actual fresh waste leachate and a machine learning approach that predicts the stripping performance under different operational parameters. The high-gravity field is implemented in a co-current-flow rotating packed bed in multi-stage cycle series mode. The eXtreme Gradient Boosting algorithm is applied to the experimental data to predict the liquid volumetric mass transfer coefficient (KLa) and removal efficiency (η) for various rotation speeds, numbers of stripping stages, gas flow rates, and liquid flow rates. Ammonia stripping under a high-gravity field achieves η = 82.73% and KLa = 5.551 × 10-4 s-1 at a pH value of 10 and ambient temperature. The results suggest that the eXtreme Gradient Boosting model provides good accuracy and predictive performance, with R2 values of 0.9923 and 0.9783 for KLa and η, respectively. The machine learning models developed in this study are combined with experimental results to provide more comprehensive information on rotating packed bed operations and more accurate predictions of KLa and η. The information mining behind the model is an important reference for the rational design of high-gravity-field-coupled ammonia stripping projects.
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Affiliation(s)
- Shaomin Guo
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, University of Science and Technology Beijing, Beijing 100083, PR China
| | - Xiuwei Ao
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, University of Science and Technology Beijing, Beijing 100083, PR China
| | - Xin Ma
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, University of Science and Technology Beijing, Beijing 100083, PR China
| | - Shikun Cheng
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, University of Science and Technology Beijing, Beijing 100083, PR China
| | - Cong Men
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, University of Science and Technology Beijing, Beijing 100083, PR China
| | - Hidenori Harada
- Graduate School of Asian and African Area Studies, Kyoto University, Kyoto 606-8501, Japan
| | - Devendra P Saroj
- Department of Civil and Environmental Engineering, Centre for Environmental Health Engineering (CEHE), Faculty of Engineering and Physical Sciences, University of Surrey, Surrey GU27XH, United Kingdom
| | - Heinz-Peter Mang
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, University of Science and Technology Beijing, Beijing 100083, PR China
| | - Zifu Li
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, University of Science and Technology Beijing, Beijing 100083, PR China.
| | - Lei Zheng
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, University of Science and Technology Beijing, Beijing 100083, PR China.
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Lai Y, Abdelbasset WK, Olegovich Bokov D, Salah Al-Shati A. Development of ANFIS technique for estimation of CO2 solubility in amino acids and study on impact of input parameters. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.104284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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