Optimization of a renewable hydrogen production system from food waste: A combination of anaerobic digestion and biogas reforming

Food wastes for bioproduct production and potential strategies for high feedstock variability

Unavoidable food wastes could be an important feedstock for industrial biotechnology, while their valorization could provide added value for the food processor. However, despite their abundance and low costs, the heterogeneous/mixed nature of these food wastes produced by food processors and consumers leads to a high degree of variability in carbon and nitrogen content, as well as specific substrates, in food waste hydrolysate. This has limited their use for bioproduct synthesis. These wastes are often instead used in anaerobic digestion and mixed microbial culture, creating a significant knowledge gap in their use for higher value biochemical production via pure and single microbial culture. To directly investigate this knowledge gap, various waste streams produced by a single food processor were enzymatically hydrolyzed and characterized, and the degree of variability with regard to substrates, carbon, and nitrogen was quantified. The impact of hydrolysate variability on the viability and performance of polyhydroxyalkanoates biopolymers production using bacteria (Cupriavidus necator) and archaea (Haloferax mediterranei) as well as sophorolipids biosurfactants production with the yeast (Starmerella bombicola) was then elucidated at laboratory-scale. After which, strategies implemented during this experimental proof-of-concept study, and beyond, for improved industrial-scale valorization which addresses the high variability of food waste hydrolysate were discussed in-depth, including media standardization and high non-selective microbial organisms growth-associated product synthesis. The insights provided would be beneficial for future endeavors aiming to utilize food wastes as feedstocks for industrial biotechnology.

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 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.

Heavy metal removal using an advanced removal method to obtain recyclable paper incineration ash

Various agents, including ethylenediaminetetraacetic acid, oxalic acid, citric acid, and HCl, were applied to remove heavy metals from raw paper incineration ash and render the ash recyclable. Among these prepared agent solutions, ethylenediaminetetraacetic acid showed the highest efficiency for Pb removal, while oxalic acid showed the highest efficiencies for Cu, Cd, and As removal. Additionally, three modes of an advanced removal method, which involved the use of both ethylenediaminetetraacetic acid and oxalic acid, were considered for use at the end of the rendering process. Among these three modes of the advanced removal method, that which involved the simultaneous use of ethylenediaminetetraacetic acid and oxalic acid, i.e., a mixture of both solutions, showed the best heavy metal removal efficiencies. In detail, 11.9% of Cd, 10% of Hg, 28.42% of As, 31.29% of Cu, and 49.19% of Pb were removed when this method was used. Furthermore, the application of these three modes of the advanced removal method resulted in a decrease in the amounts of heavy metals eluted and brought about an increase in the CaO content of the treated incineration ash, while decreasing its Cl content. These combined results enhanced the solidification effect of the treated incineration ash. Thus, it was confirmed that the advanced removal method is a promising strategy by which recyclable paper incineration ash can be obtained.