A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste
Abstract The utilization of biochar derived from biomass residue to enhance anaerobic digestion (AD) for bioenergy recovery offers a sustainable approach to advance sustainable energy and mitigate climate change. However, conducting comprehensive research on the optimal conditions for AD experiments...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-01-01
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| Series: | Carbon Neutrality |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43979-023-00078-0 |
| _version_ | 1797355466212245504 |
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| author | Pengshuai Zhang Tengyu Zhang Jingxin Zhang Huaiyou Liu Cristhian Chicaiza-Ortiz Jonathan T. E. Lee Yiliang He Yanjun Dai Yen Wah Tong |
| author_facet | Pengshuai Zhang Tengyu Zhang Jingxin Zhang Huaiyou Liu Cristhian Chicaiza-Ortiz Jonathan T. E. Lee Yiliang He Yanjun Dai Yen Wah Tong |
| author_sort | Pengshuai Zhang |
| collection | DOAJ |
| description | Abstract The utilization of biochar derived from biomass residue to enhance anaerobic digestion (AD) for bioenergy recovery offers a sustainable approach to advance sustainable energy and mitigate climate change. However, conducting comprehensive research on the optimal conditions for AD experiments with biochar addition poses a challenge due to diverse experimental objectives. Machine learning (ML) has demonstrated its effectiveness in addressing this issue. Therefore, it is essential to provide an overview of current ML-optimized energy recovery processes for biochar-enhanced AD in order to facilitate a more systematic utilization of ML tools. This review comprehensively examines the material and energy flow of biochar preparation and its impact on AD is comprehension reviewed to optimize biochar-enhanced bioenergy recovery from a production process perspective. Specifically, it summarizes the application of the ML techniques, based on artificial intelligence, for predicting biochar yield and properties of biomass residues, as well as their utilization in AD. Overall, this review offers a comprehensive analysis to address the current challenges in biochar utilization and sustainable energy recovery. In future research, it is crucial to tackle the challenges that hinder the implementation of biochar in pilot-scale reactors. It is recommended to further investigate the correlation between the physicochemical properties of biochar and the bioenergy recovery process. Additionally, enhancing the role of ML throughout the entire biochar-enhanced bioenergy recovery process holds promise for achieving economically and environmentally optimized bioenergy recovery efficiency. Graphical Abstract |
| first_indexed | 2024-03-08T14:12:33Z |
| format | Article |
| id | doaj.art-cccdb3f5ffc04921b6d6bf241435f31c |
| institution | Directory Open Access Journal |
| issn | 2788-8614 2731-3948 |
| language | English |
| last_indexed | 2024-03-08T14:12:33Z |
| publishDate | 2024-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Carbon Neutrality |
| spelling | doaj.art-cccdb3f5ffc04921b6d6bf241435f31c2024-01-14T12:38:50ZengSpringerCarbon Neutrality2788-86142731-39482024-01-013112110.1007/s43979-023-00078-0A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic wastePengshuai Zhang0Tengyu Zhang1Jingxin Zhang2Huaiyou Liu3Cristhian Chicaiza-Ortiz4Jonathan T. E. Lee5Yiliang He6Yanjun Dai7Yen Wah Tong8China-UK Low Carbon College, Shanghai Jiao Tong UniversityChina-UK Low Carbon College, Shanghai Jiao Tong UniversityChina-UK Low Carbon College, Shanghai Jiao Tong UniversityChina-UK Low Carbon College, Shanghai Jiao Tong UniversityChina-UK Low Carbon College, Shanghai Jiao Tong UniversityDepartment of Chemical & Biomolecular Engineering, National University of SingaporeChina-UK Low Carbon College, Shanghai Jiao Tong UniversitySchool of Mechanical Engineering, Shanghai Jiaotong UniversityEnvironmental Research Institute, National University of SingaporeAbstract The utilization of biochar derived from biomass residue to enhance anaerobic digestion (AD) for bioenergy recovery offers a sustainable approach to advance sustainable energy and mitigate climate change. However, conducting comprehensive research on the optimal conditions for AD experiments with biochar addition poses a challenge due to diverse experimental objectives. Machine learning (ML) has demonstrated its effectiveness in addressing this issue. Therefore, it is essential to provide an overview of current ML-optimized energy recovery processes for biochar-enhanced AD in order to facilitate a more systematic utilization of ML tools. This review comprehensively examines the material and energy flow of biochar preparation and its impact on AD is comprehension reviewed to optimize biochar-enhanced bioenergy recovery from a production process perspective. Specifically, it summarizes the application of the ML techniques, based on artificial intelligence, for predicting biochar yield and properties of biomass residues, as well as their utilization in AD. Overall, this review offers a comprehensive analysis to address the current challenges in biochar utilization and sustainable energy recovery. In future research, it is crucial to tackle the challenges that hinder the implementation of biochar in pilot-scale reactors. It is recommended to further investigate the correlation between the physicochemical properties of biochar and the bioenergy recovery process. Additionally, enhancing the role of ML throughout the entire biochar-enhanced bioenergy recovery process holds promise for achieving economically and environmentally optimized bioenergy recovery efficiency. Graphical Abstracthttps://doi.org/10.1007/s43979-023-00078-0Anaerobic digestionBiomass-based biocharMachine learningBioenergy recovery |
| spellingShingle | Pengshuai Zhang Tengyu Zhang Jingxin Zhang Huaiyou Liu Cristhian Chicaiza-Ortiz Jonathan T. E. Lee Yiliang He Yanjun Dai Yen Wah Tong A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste Carbon Neutrality Anaerobic digestion Biomass-based biochar Machine learning Bioenergy recovery |
| title | A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste |
| title_full | A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste |
| title_fullStr | A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste |
| title_full_unstemmed | A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste |
| title_short | A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste |
| title_sort | machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste |
| topic | Anaerobic digestion Biomass-based biochar Machine learning Bioenergy recovery |
| url | https://doi.org/10.1007/s43979-023-00078-0 |
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