A review of biowaste remediation and valorization for environmental sustainability : Artificial intelligence approach
Copyright © 2023 Elsevier Ltd. All rights reserved..
Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:324 |
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| Enthalten in: |
Environmental pollution (Barking, Essex : 1987) - 324(2023) vom: 01. Mai, Seite 121363 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Aniza, Ria [VerfasserIn] |
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| Links: |
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| Themen: |
Algal biowaste |
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| Anmerkungen: |
Date Completed 28.03.2023 Date Revised 28.03.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.envpol.2023.121363 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2023 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Algal biowaste | |
| 650 | 4 | |a Artificial intelligence (AI) | |
| 650 | 4 | |a Bioenergy | |
| 650 | 4 | |a Lignocellulosic biowaste | |
| 650 | 4 | |a Remediation | |
| 650 | 4 | |a Valorization | |
| 700 | 1 | |a Chen, Wei-Hsin |e verfasserin |4 aut | |
| 700 | 1 | |a Pétrissans, Anélie |e verfasserin |4 aut | |
| 700 | 1 | |a Hoang, Anh Tuan |e verfasserin |4 aut | |
| 700 | 1 | |a Ashokkumar, Veeramuthu |e verfasserin |4 aut | |
| 700 | 1 | |a Pétrissans, Mathieu |e verfasserin |4 aut | |
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