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Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies. [ABSTRACT FROM AUTHOR]

Spatial separation into acidogenic and methanogenic stages is considered a viable option to ensure process stability, energy efficiency and the better control of key anaerobic digestion (AD) parameters. The elucidation of the optimal modes of two-stage AD for the maximization of the recovery of biofuels (H2 and CH4) is still an urgent task, the main optimization criteria being the highest energy yield (EY) and energy production rate (EPR). In this work, a response surface methodology was used for an optimization of energy production from the two-stage mesophilic–thermophilic AD of cheese whey (CW). Three dilution rates of CW, providing values of 10.9, 14.53 and 21.8 g for the chemical oxygen demand (COD)/L in the influent and three hydraulic retention times (HRTs) (1, 2 and 3 days) in methanogenic biofilters at a constant HRT in an acidogenic biofilter of 0.42 days, were tested to optimize the EY and EPR. The desirability approach produced combined optimum conditions as follows: the dilution rate of the CW provided 17.58 g COD/L (corresponding to OLR of 6.5 g COD/(L·day)) in the influent and a HRT in the methanogenic biofilter of 2.28 days, both of which provided a maximum EPR of 80.263 kJ/(L·day) and EY of 9.56 kJ/g COD, with an overall desirability value of 0.883. [ABSTRACT FROM AUTHOR]

According to the International Energy Agency (IEA), only a tiny fraction of the full potential of energy from biomass is currently exploited in the world. Biogas is a good source of energy and heat, and a clean fuel. Converting it to biomethane creates a product that combines all the benefits of natural gas with zero greenhouse gas emissions. This is important given that the methane contained in biogas is a more potent greenhouse gas than carbon dioxide (CO2). The total amount of CO2 emission avoided due to the installation of biogas plants is around 3380 ton/year, as 1 m3 of biogas corresponds to 0.70 kg of CO2 saved. In Russia, despite the huge potential, the development of bioenergy is rather on the periphery, due to the abundance of cheap hydrocarbons and the lack of government support. Based on the data from an agro-industrial plant located in Central Russia, the authors of the article demonstrate that biogas technologies could be successfully used in Russia, provided that the Russian Government adopted Western-type measures of financial incentives. [ABSTRACT FROM AUTHOR]