Sebastián Villegas-Moncada, Mario Luna-delRisco, Catalina Arroyave-Quiceno, Mauricio González-Palacio, Carlos Peláez-Jaramillo
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引用次数: 0
Abstract
Over the past two decades, modeling the hydrolysis stage has been recognized as critical for understanding its behavior and determining optimal operating conditions for anaerobic digestion (AD). Traditional approaches, such as first-order and Michaelis–Menten kinetic models, account for substrate concentration and enzymatic activity, respectively, but neglect mass-transfer effects. In this work, we propose a semi-empirical model that integrates enzymatic catalysis with molecular diffusion phenomena in the microbial boundary layer. We derive a hydrolysis rate expression by combining Michaelis–Menten kinetics with Fick’s law of diffusion and validate it against experimental data from a thermophilic batch reactor treating cattle manure (55 \(^{\circ }\)C, 62 \(g\,\text {VS}\,\text {L}^{-1}\)). Compared to the first-order model (R\(^2\) = 0.940), our model achieves a superior fit (R\(^2\) = 0.973), demonstrating that diffusion resistance can significantly influence hydrolysis kinetics. By formulating the kinetic model in terms of explicit biochemical and mass-transfer parameters (\(r_{h,\text {max}}\), \(K_M\), \(k_d\), \(\alpha \)), it becomes possible to identify optimal operational strategies for enhancing hydrolysis efficiency. The results indicate that coupling enzymatic catalysis with diffusion provides a more accurate theoretical description than the first-order model and enables improved prediction of biopolymer solubilization in AD.
期刊介绍:
BioEnergy Research fills a void in the rapidly growing area of feedstock biology research related to biomass, biofuels, and bioenergy. The journal publishes a wide range of articles, including peer-reviewed scientific research, reviews, perspectives and commentary, industry news, and government policy updates. Its coverage brings together a uniquely broad combination of disciplines with a common focus on feedstock biology and science, related to biomass, biofeedstock, and bioenergy production.