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Pretreatment and hydrolysis of lignocellulosic biomass are affected by several uncertainties, which must be systematically considered for a robust process design. In this work, stochastic simulations for expected uncertainties in feedstock composition, kinetic parameter values, and operational parameter values for these two steps were performed. The results indicated that these uncertainties significantly impacted the concentration profiles, which could also affect the optimal batch time. Global sensitivity analysis was then used to identify the critical uncertain parameters. In the feedstock components, cellulose and xylan fractions for acid pretreatment and cellulose fraction for enzymatic hydrolysis were important. Temperature was the most sensitive operating parameter for both acid pretreatment and hydrolysis. The activation energies for different reactions were ranked in terms of their impact on process output. The selected parameters were used to develop stochastic process models using Ito process and mean reverting process for feed composition and kinetic parameter uncertainty. (C) 2017 Elsevier Ltd. All rights reserved.

Cellulose hydrolysis is a key step in lignocellulosic ethanol production. At present, commercial production of lignocellulosic ethanol is limited due to the long hydrolysis times and requirement of large quantity of expensive enzymes. Therefore, reduction of the enzyme consumption as well as hydrolysis time is crucial and model based optimisation methods can be used for the same. A semi-mechanistic model with cellobiose, glucose, and xylose inhibition with Arrhenius based relationship between temperature and kinetic parameters and thermal deactivation of enzymes was used for the present study. Optimal control problem with temperature as control variable was formulated after considering two different objective functions. For the objective of glucose concentration maximisation at final batch time, the benefit of implementing optimal control increased with reducing batch times. For the batch time of 24 hours, the final glucose concentration increased by 3.2%. For the objective of batch time minimisation, the reduction of batch time was 5.8% and it was observed for a target glucose concentration of 45 g/kg of cellulose. The use of optimal control can reduce the enzyme requirement up to 77.8% of endoglucanase and exoglucanase for glucose maximisation and 22.2% for batch time minimisation. The above results show the usefulness of optimal temperature control in increasing the glucose concentration, and reducing the batch time without increasing the enzyme used.

Uncertainties in feedstock composition and kinetic parameters impact the performance of enzymatic hydrolysis of lignocellulosic biomass. Operating strategies need to be accordingly modified to achieve higher yield of glucose. Therefore, stochastic optimization and optimal control problems were formulated to maximize glucose concentration and minimize variance. The optimal temperature for maximization of glucose concentration reduced in the presence of uncertainties. For uncertainties in kinetic parameters, the optimal temperature reduced from 323.34 K to 317.07 K for a batch time of 12 h, and the glucose concentration increased by 7.7% when the stochastic optimal temperature was used instead of deterministic temperature. Similarly, for the objective of minimization of variance in glucose concentration at the end of the batch, stochastic optimization reduced the variance by 89% as compared to deterministic optimization. Stochastic optimal control resulted in up to 60% improvement in mean glucose concentration in comparison to the deterministic optimal control.