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A major constraint in the microalgal technology is the economics involved in cultivation and harvesting. This work is focussed on the optimization of nutrients for cultivation and harvestin...

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.

Among the various sources of biomass, the lignocellulosic sources such as agricultural residue, dedicated energy grasses, forestry residue and short rotation woody biomass are expected to satisfy a significant fraction of the total future demand. However, while technologically feasible, the production of biofuels from lignocellulosic biomass is not techno-economically feasible. The presence of various uncertainties creates a number of challenges in overcoming these hurdles. The biomass to biofuel system is impacted by variability, both natural as well as anthropogenic, at almost all stages. This includes variability in feedstock production on a seasonal basis, variability in feedstock composition, variability in process efficiencies and variability in prices of raw material and products. This makes the optimization of the complete system extremely challenging. This article highlights the presence of these uncertainties and their impact on the operations of the biomass to biofuel systems. A detailed classification of uncertainties in biomass to biofuel systems, methods of incorporating uncertainties in process optimization and supply chain design are reviewed and explained. The paper will also review the work done to address these uncertainties using systematic methods.

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.

A major constraint in the microalgal technology is the economics involved in cultivation and harvesting. This work is focussed on the optimization of nutrients for cultivation and harvesting using 'Scenedesmus sp'

A major constraint in the microalgal technology is the economics involved in cultivation and harvesting. This work is focussed on the optimization of nutrients for cultivation and harvesting using ‘Scenedesmus sp’. Response surface methodology (RSM) using ‘Face centered central composite design’ (FCCD) available in Design expert 10.0.4 was used to develop the regression model for optimization of nutrients and flocculation conditions. The optimum nutrient conditions were 500 ppm of urea, 250 ppm of potassium dihydrogen phosphate and 1000 ppm of potassium hydrogen carbonate under artificial light conditions and 500 ppm of urea and 2000 ppm of potassium hydrogen carbonate under sunlight conditions. The optimum conditions were predicted using the model and compared with experimental data. The model has an R2 value of 0.9769 and 0.9798 for artificial light and sunlight conditions, respectively. In the case of harvesting studies, 98% flocculation efficiency was obtained for a combination of pH 10.4, temperature 45°C, 200 mg/l of leaf powder of Cassia auriculata. The model has an R2 value of 0.9989. The present studies indicated that cultivation of Scenedesmus sp. with the optimized nutrients and harvesting conditions facilitate a platform for energy efficient mass cultivation.