Alrefaey, KarimKarimAlrefaeySchultz, JanaJanaSchultzScherzinger, MarvinMarvinScherzingerNosier, Mahmoud A.Mahmoud A.NosierElbanhawy, Amr Y.Amr Y.Elbanhawy2024-07-042024-07-042024-09-01Bioresource Technology Reports 27: 101882 (2024)https://hdl.handle.net/11420/48186This study presents a comprehensive biochemical predictive approach for assessing biogas production kinetics across ten lignocellulosic substrates in batch operation. The methodology employs a range of kinetic and regression models, all grounded in the substrates' chemical composition. Among the kinetic models, the cone model demonstrated superior performance, achieving an average error of 1.67 % in describing biogas production from all substrates. The quadratic Monod type model followed closely, with an error of 1.96 %. Among the regression models, on the other hand, the logistic function model exhibited enhanced predictive capabilities, yielding an average error of 6.02 %, while the Chen and Hashimoto one showed a higher error of 60.54 %. The findings underscore the potential of precise biogas production forecasting and tracking the daily rates of gas generation, rather than solely relying on cumulative gas yields at the end of the process.en2589-014X2024ElsevierAnaerobic digestionBiogas productionLignocellulosic biomassRegression modelsNatural Sciences and Mathematics::500: ScienceJournal Article10.1016/j.biteb.2024.101882Journal Article