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The research on lignocellulose pretreatments is generally performed through experiments that require substantial resources, are often time-consuming and are not always environmentally friendly. Therefore, researchers are developing computational methods which can minimize experimental procedures and save money. In this research, three machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM), as well as their ensembles were evaluated to predict acid-insoluble detergent lignin (AIDL) content in lignocellulose biomass. Three different types of harvest residue (maize stover, soybean straw and sunflower stalk) were first pretreated in a laboratory oven with hot air under two different temperatures (121 and 175 °C) at different duration (30 and 90 min) with the aim of disintegration of the lignocellulosic structure, i.e., delignification. Based on the leave-one-out cross-validation, the XGB resulted in the highest accuracy for all individual harvest residues, achieving the coefficient of determination (R2) in the range of 0.756–0.980. The relative variable importances for all individual harvest residues strongly suggested the dominant impact of pretreatment temperature in comparison to its duration. These findings proved the effectiveness of machine learning prediction in the optimization of lignocellulose pretreatment, leading to a more efficient lignin destabilization approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermally pretreated harvest residues and manure were anaerobically co-digested. • Four machine learning methods were tested for biogas and methane yield prediction. • Recursive Feature Elimination was the optimal feature selection approach. • R2 for biogas and methane yield prediction was up to 0.820 and 0.823, respectively. • An ensemble machine learning produced stable accuracy regardless of input datasets. This study aimed to clarify the statistical accuracy assessment approaches used in recent biogas prediction studies using state-of-the-art ensemble machine learning approach according to 10-fold cross-validation in 100 repetitions. Three thermally pretreated harvest residue types (maize stover, sunflower stalk and soybean straw) and manure were anaerobically co-digested, measuring biogas and methane yield alongside eight thermal preprocessing and biomass covariates. These were the inputs to an ensemble machine learning approach for biogas and methane yield prediction, employing three feature selection approaches. The Support Vector Machine prediction with the Recursive Feature Elimination resulted in the highest prediction accuracy, achieving the coefficient of determination of 0.820 and 0.823 for biogas and methane yield prediction, respectively. This study demonstrated an extreme dependency of prediction accuracy to input dataset properties, which could only be mitigated with ensemble machine learning and strongly suggested that the split-sample approach, often used in previous studies, should be avoided. [ABSTRACT FROM AUTHOR]

• PEF is predominantly used in medicine and in food industry. • Up to now PEF is efficiently applied to sludge pretreatment for biogas production. • Application of PEF in lignocellulose pretreatment demonstrated its high potential. • Transfer of PEF from batch to continuous industrial-scale is in its R&D phase. This review focuses on current status of pulsed electric field (PEF) technology and its implementation in biogas production. First, basic principles of PEF and a schematic overview of typical PEF processing system were provided. Thereafter, lab- and pilot-scale PEF pretreatments of sludge with subsequent anaerobic digestion (AD) were provided. Furthermore, PEF technology, as an emerging technology for the lignocellulose (LC) pretreatment in biogas production which is still predominantly used at lab-scale, was outlined. Eventually, conclusion together with future perspectives and challenges were outlined. [ABSTRACT FROM AUTHOR]