Response surface methodology (RSM) is commonly used for optimising process parameters affecting enzymatic hydrolysis. However, artificial neural network–genetic algorithm hybrid model can also serve as an effective option, primarily for non-linear polynomial systems. The present study compares these approaches for enzymatic hydrolysis of water hyacinth biomass to maximise total reducing sugar (TRS) for bio-ethanol production. Maximum TRS (0.5672 g/g) was obtained using 9.92 (% w/w) substrate concentrations, 49.56 U/g cellulase concentrations, 280.33 U/g xylanase concentrations and 0.13 (% w/w) surfactant concentrations. The average % error for artificial neural networking (ANN) and RSM were 3.08 and 4.82 and the prediction percentage errors in optimum output are 0.95 and 1.41, respectively, which showed the supremacy of ANN in illustrating the non-linear behaviour of the system. Fermentation of the hydrolysate yielded a maximum ethanol concentration of 10.44 g/l using Pichia stipitis, followed by 8.24 and 6.76 g/l for Candida shehatae and Saccharomyces cerevisiae

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A generalized unsteady-state kinetic model, coupled with all modes of heat transfer, was developed to describe the combined coal devolatilization and the subsequent combustion of the residual char under oxy−fuel condition in both O 2 −CO 2 and O 2 −N 2 environments. Experiments were conducted to validate the model, which was also found to predict the experimental data published in the literature well. The effect of coal particle diameter, temperature of the reactor, and oxygen concentration on devolatilization time was investigated. Peaks in devolatilization and char combustion rates and particle center temperature were studied, and the effect of different parameters assessed. Higher reaction time was observed in an O 2 −CO 2 environment compared to that in an O 2 −N 2 environment due to lower particle temperatures resulting from endothermic gasification reaction and the difference in thermo-physical properties. Simulation studies were carried out to generate temperature, carbon, O 2 , CO, and CO 2 contours to understand the char combustion reaction mechanism. The reaction started at the external surface of the particle, following unreacted shrinking core model with two zones; the solid product layer and the unreacted shrinking core, separated by a thin reaction front. Gradually, the reaction front thickness increases, leading to the shrinking reactive core model, consisting of three zones: completely reacted ash layer, partially burnt reacting char called reaction zone, and unreacted zone or slightly reacted char core. At the outset, O 2 cannot penetrate into the particle and CO produced near the surface diffuses out to the boundary layer, forming a thin flame. Subsequently, O 2 diffuses through porous ash layer into the char, and CO burns within the pores, with hardly any CO detected in the boundary layer as the particle temperature increases.