Proposed protocol for rate-limiting step determination during anaerobic digestion of complex substrates

Biohythane production via anaerobic digestion process: fundamentals, scale-up challenges, and techno-economic and environmental aspects

Biohythane, a balanced mixture comprising bioH2 (biohydrogen) and bioCH4 (biomethane) produced through anaerobic digestion, is gaining recognition as a promising energy source for the future. This article provides a comprehensive overview of biohythane production, covering production mechanisms, microbial diversity, and process parameters. It also explores different feedstock options, bioreactor designs, and scalability challenges, along with techno-economic and environmental assessments. Additionally, the article discusses the integration of biohythane into waste management systems and examines future prospects for enhancing production efficiency and applicability. This review serves as a valuable resource for researchers, engineers, and policymakers interested in advancing biohythane production as a sustainable and renewable energy solution.

Mechanistic insights into synergistic facilitation of copper/zinc ions and dewatered swine manure-derived biochar on anaerobic digestion of swine wastewater

Biochar-assisted anaerobic digestion (AD) has been proposed an advanced system for swine wastewater (SW) management. However, the effects of metallic nutrients in SW, such as copper/zinc ions (Cu2+/Zn2+), on the biochar-assisted AD of SW are not well understood. This study investigated the influences of individual Cu2+/Zn2+ or dewatered swine manure-derived biochar, as well as their combined additions, on the AD of SW. The results showed that exposure to 50 mg/L Cu2+/Zn2+ temporary inhibited methane production, but the addition of 20 g/L biochar alleviated this inhibition by shortening the methanogenic lag time and increasing methane yield. Following a period of acclimation, both Cu2+/Zn2+ and biochar promoted methane production, although metagenomic analysis revealed distinct mechanisms underlying their promotion. Cu2+/Zn2+ enhanced ATP processing, including electron exchange between NADH/NAD+ and succinate/fumarate transformation, by 26.0-35.8%. Additionally, the gene encoding Coenzyme M methylation was upregulated by 36.2% along with enrichments of Methanocullus and Methanosarcina, contributing to accelerated hydrolysis and methanogenesis rates by 54.7% and 44.8%, respectively. On the other hand, biochar mainly stimulated bacterial F-type ATPase activities by 28.4%, likely facilitating direct interspecies electron transfer between Geobacter and Methanosarcina for syntrophic methanogenesis. The combined addition of Cu2+/Zn2+ and biochar resulted in "win-win" benefits, significantly increasing the maximum methane production rate from 40.3 mL CH4/d to 53.7 mL CH4/d. Moreover, the introduction of biochar into AD of SW facilitated the transformation of more Cu2+/Zn2+ from a reducible Fe-Mn oxides form to a residual form, which potentially reduced the metallic toxicity of the digestate for soil amendment. The findings of this study provide novel insights into understanding the synergistic impacts of heavy metals and biochar in regulating SW during AD, as well as the management of associated digestate.

Advances, trends and challenges in the use of biochar as an improvement strategy in the anaerobic digestion of organic waste: a systematic analysis

A recently strategy applied to anaerobic digestion (AD) is the use of biochar (BC) obtained from the pyrolysis of different organic waste. The PRISMA protocol-based review of the most recent literature data from 2011-2022 was used in this study. The review focuses on research papers from Scopus® and Web of Knowledge®. The review protocol used permits to identify 169 articles. The review indicated a need for further research in the following challenges on the application of BC in AD: i) to increase the use of BC in developing countries, which produce large and diverse amounts of waste that are the source of production of this additive; ii) to determine the effect of BC on the AD of organic waste under psychrophilic conditions; iii) to apply tools of machine learning or robust models that allow the process optimization; iv) to perform studies that include life cycle and technical-economic analysis that allow identifying the potential of applying BC in AD in large-scale systems; v) to study the effects of BC on the agronomic characteristics of the digestate once it is applied to the soil and vi) finally, it is necessary to deepen in the effect of BC on the dynamics of nitrogen and microbial consortia that affect AD, considering the type of BC used. In the future, it is necessary to search for new solutions in terms of the transport phenomena that occurs in AD with the use of BC using robust and precise mathematical models at full-scale conditions.

Comparative life cycle assessment of anaerobic digestion, lagoon evaporation, and direct land application of olive mill wastewater

Olive mill wastewater is a prominent waste stream in the Mediterranean countries, with its uncontrolled disposal in water recipients causing significant environmental issues. Anaerobic digestion has been extensively studied for the treatment of various agricultural waste streams. The scope of the present study was the environmental evaluation of the anaerobic digestion of three-phase olive mill wastewater for energy production in an anaerobic bioreactor. Regarding the environmental assessment of the process, the results indicate a lead in the proposed process compared with the baseline scenarios. Moreover, several environmental issues in terrestrial acidification and water eutrophication midpoint categories were exhibited by the digestate utilization. The implementation of the anaerobic digestion method averts an overall environmental damage of 5 mPt per 1000 kg of waste treated. For this reason, the implementation of the proposed method could be a sustainable alternative for wastewater treatment in olive oil production regions, aiming to circular economy.

Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms

In the anaerobic digestion (AD) process there are some difficulties in maintaining process stability due to the complexity of the system. The variability of the raw material coming to the facility, temperature fluctuations and pH changes as a result of microbial processes cause process instability and require continuous monitoring and control. Increasing continuous monitoring, and internet of things applications within the scope of Industry 4.0 in AD facilities can provide process stability control and early intervention. In this study, five different machine learning (ML) algorithms (RF, ANN, KNN, SVR, and XGBoost) were used to describe and predict the correlation between operational parameters and biogas production quantities collected from a real-scale anaerobic digestion plant. The KNN algorithm had the lowest accuracy in predicting total biogas production over time, while the RF model had the highest prediction accuracy of all prediction models. The RF method produced the best prediction, with an R² of 0.9242, and it was followed by XGBoost, ANN, SVR, and KNN (with R² values of 0.8960, 0.8703, 0.8655, 0.8326, respectively). Real-time process control will be provided and process stability will be maintained by preventing low-efficiency biogas production with the integration of ML applications into AD facilities.