A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach*

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 dis-carded 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.

Automated summary

Biowaste remediation and valorization focus on using biowaste-to-bioenergy conversion systems to prevent waste generation and promote environmental sustainability. Biomass waste, such as agriculture waste and algal residue, is widely studied as a potential feedstock for biowaste valorization due to its abundance. However, variability in feedstock, conversion costs, and supply chain stability hinder the widespread usage of bioenergy products. To overcome these challenges, artificial intelligence (AI) has been applied in biowaste remediation and valorization research to improve prediction models and decision-making processes.