The dependence on non-renewable resources, particularly fossil fuels, has awaken a growing interest in research of sustainable alternative energy sources, such as bioethanol. The production of bioethanol from lignocellulosic materials comprises three main stages, starting with a pretreatment, followed by an enzymatic hydrolysis step where fermentable sugars are obtained for the final fermentation process. Enzymatic hydrolysis represents an essential step in the bioethanol production, however there are some limitations in it that hinders the process to be economically feasible. Different strategies have been studied to overcome these limitations, including the enzyme recycling and the utilization of high solids concentrations. Several investigations have been carried out in different bioreactor configurations with the aim to obtain higher yields of glucose in the enzymatic hydrolysis stage; however, the commonest are Stirred Tank Bioreactors (STBR) and Membrane Bioreactors (MBR). In general, the key criteria for a bioreactor design include adequate mass transfer, low shear stress, and efficient mixing that allows the appropriated interaction between the substrate and the enzyme. Therefore, this review will address the main aspects to be considered for a bioreactor design, as well as, the operational conditions, some characteristics and mode of operating strategies of the two main bioreactors used in the enzymatic hydrolysis stage. Moreover, two types of pneumatically agitated bioreactors, namely bubble column and gas-lift bioreactors, are discussed as promising alternatives to develop enzymatic saccharification due to their low energy consumption compared with STBR.

Abstract:This study presents the development and application of a systematic model-based framework for bioprocess optimization. The framework relies on the identification of sources of uncertainties via global sensitivity analysis, followed by the quantification of their impact on performance evaluation metrics via uncertainty analysis. Finally, stochastic programming is applied to drive the process development efforts forward subject to these uncertainties. The framework is evaluated on four different process configurations for cellulosic ethanol production including Simultaneous Further stochastic optimization demonstrated the options for further reduction of the production costs with different processing configurations, reaching a reduction of up to 28% in the production cost in the SHCF configuration compared to the base case operation. Further, the framework evaluated here for uncertainties in the technical domain, can also be used to evaluate the impact of market uncertainties (feedstock prices, selling price of ethanol, etc) and political uncertainties (such as subsidies) on the economic feasibility of lignocellulosic ethanol production.

High momentum jets and hadrons can be used as probes for the quark gluon plasma (QGP) formed in nuclear collisions at high energies. We investigate the influence of fluctuations in the fireball on jet quenching observables by comparing propagation of light quarks and gluons through averaged, smooth QGP fireballs with event-by-event jet quenching using realistic inhomogeneous fireballs. We find that the transverse momentum and impact parameter dependence of the nuclear modification factor R AA can be fit well in an event-by-event quenching scenario within experimental errors. However the transport coefficientq extracted from fits to the measured nuclear modification factor R AA in averaged fireballs underestimates the value from event-by-event calculations by up to 50%. On the other hand, after adjustingq to fit R AA in the eventby-event analysis we find residual deviations in the azimuthal asymmetry v 2 and in two-particle correlations, that provide a possible faint signature for a spatial tomography of the fireball. We discuss a correlation function that is a measure for spatial inhomogeneities in a collision and can be constrained from data.

The objective of this study is to perform a comprehensive enzyme kinetics analysis in view of validating and consolidating a semimechanistic kinetic model consisting of homogeneous and heterogeneous reactions for enzymatic hydrolysis of lignocellulosic biomass proposed by the U.S. National Renewable Energy Laboratory (Kadam et al., Biotechnol Prog 20(3):698-705, 2004) and its variations proposed in this work. A number of dedicated experiments were carried out under a range of initial conditions (Avicel® versus pretreated barley straw as substrate, different enzyme loadings and different product inhibitors such as glucose, cellobiose and xylose) to test the hydrolysis and product inhibition mechanisms of the model. A nonlinear least squares method was used to identify the model and estimate kinetic parameters based on the experimental data. The suitable mathematical model for industrial application was selected among the proposed models based on statistical information (weighted sum of square errors). The analysis showed that transglycosylation plays a key role at high glucose levels. It also showed that the values of parameters depend on the selected experimental data used for parameter estimation. Therefore, the parameter values are not universal and should be used with caution. The model proposed by Kadam et al. (Biotechnol Prog 20(3):698-705, 2004) failed to predict the hydrolysis phenomena at high glucose levels, but when combined with transglycosylation reaction(s), the prediction of cellulose hydrolysis behaviour over a broad range of substrate concentrations (50-150 g/L) and enzyme loadings (15.8-31.6 and 1-5.9 mg protein/g cellulose for Celluclast and Novozyme 188, respectively) was possible. This is the first study introducing transglycosylation into the semimechanistic model. As long as these type of models are used within the boundary of their validity (substrate type, enzyme source and substrate concentration), they can support process design and technology improvement efforts at pilot and full-scale studies.

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Biofuels provide an attractive alternative for satisfying energy demands in a more sustainable way than fossil fuels. To establish a biorefinery, an optimal plan must be implemented for the entire associated supply chain, covering such aspects as selection of feedstocks, location, and capacity of biorefineries, selection of processing technologies, production amounts and transportation flows. In this context, there are several parameters, including the availability of biomass, product demand, and product prices, which are difficult to predict because they might change drastically over the different seasons of the year as well as across years. To address this challenge, this work presents a mathematical programming model for the optimal planning of a distributed system of biorefineries that considers explicitly the uncertainty associated with the supply chain operation as well as the associated risk. The potential of the proposed approach is demonstrated through its application to the production of biofuels in Mexico, considering multiple raw materials and products.