Physicochemical Characterization of Wheat Straw during a Continuous Pretreatment Process

Advances in process design, techno-economic assessment and environmental aspects for hydrothermal pretreatment in the fractionation of biomass under biorefinery concept

The development and sustainability of second-generation biorefineries are essential for the production of high added value compounds and biofuels and their application at the industrial level. Pretreatment is one of the most critical stages in biomass processing. In this specific case, hydrothermal pretreatments (liquid hot water [LHW] and steam explosion [SE]) are considered the most promising process for the fractionation, hydrolysis and structural modifications of biomass. This review focuses on architecture of the plant cell wall and composition, fundamentals of hydrothermal pretreatment, process design integration, the techno-economic parameters of the solubilization of lignocellulosic biomass (LCB) focused on the operational costs for large-scale process implementation and the global manufacturing cost. In addition, profitability indicators are evaluated between the value-added products generated during hydrothermal pretreatment, advocating a biorefinery implementation in a circular economy framework. In addition, this review includes an analysis of environmental aspects of sustainability involved in hydrothermal pretreatments.

Kinetic Parameter Determination for Depolymerization of Biomass by Inverse Modeling and Metaheuristics

A computational methodology based on inverse modeling and metaheuristics is presented for determining the best parameters of kinetic models aimed to predict the behavior of biomass depolymerization processes during size scaling up. The Univariate Marginal Distribution algorithm, particle swarm optimization, and Interior-Point algorithm were applied to obtain the values of the kinetic parameters (KM and Vmax) of four mathematical models based on the Michaelis–Menten equation: (i) Traditional Michaelis–Menten, (ii) non-competitive inhibition, (iii) competitive inhibition, and (iv) substrate inhibition. The kinetic data were obtained from our own experimentation in micro-scale. The parameters obtained from an optimized micro-scale experiment were compared with a bench scale experiment (0.5 L). Regarding the metaheuristic optimizers, it is concluded that the Interior-Point algorithm is effective in solving inverse modeling problems and has the best prediction power. According to the results, the Traditional model adequately describes the micro-scale experiments. It was found that the Traditional model with optimized parameters was able to predict the behavior of the depolymerization process during size scaling up. The methodology followed in this study can be adopted as a starting point for the solution of future inverse modeling problems.