Modelling of pretreatment and saccharification with different feedstocks and kinetic modeling of sorghum saccharification.

Experiments have been performed for pretreatment of sorghum, wheat straw and bamboo through high temperature alkali pretreatment with different alkaline loading and temperatures, and the data on extent of delignification in terms of the final compositions of cellulose, hemicellulose and lignin have been generated. Further, enzymatic saccharification has been carried out in all the cases to find the extent of conversion possible after 72h. The effect of different operating parameters on the extent of delignification and cellulose conversion are evaluated. This data is employed to develop a generalized multi-feedstock and individual feedstock based models which can be used to determine the extent of delignification and cellulose conversion for any and specific biomass respectively with alkaline pretreatment and similar enzyme conditions as considered in the present study. Also, a kinetic model is developed and validated for sorghum for cellulosic conversion.

Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results.

Solvent resistant chitosan/poly(ether-block-amide) composite membranes for pervaporation of n-methyl-2-pyrrolidone/water mixtures.

A novel composite barrier comprising of hydrophilic and solvent resistant chitosan (CS) membrane on porous solvent resistant poly(ether-block-amide) (PEBA-2533) substrate was synthesized for pervaporation (PV) based dehydration of the polar aprotic n-methyl-2-pyrolidone (NMP) green solvent. The composite barrier was crosslinked with tetraethyl orthosilicate (TEOS) to control swelling and enhance selectivity. Operating parameters such as feed water concentration, permeate pressure and membrane thickness were varied to assess membrane flux and selectivity. A two-dimensional finite element method (FEM) model was developed to predict the concentration profile within the membrane through computational fluid dynamics (CFD). On the basis of complete mixing experiments, a numerical simulation was performed to predict membrane area requirement and exit streams' compositions for commercial pervaporation units operated in plug flow mode. Both unmodified chitosan and tetraethyl orthosilicate crosslinked composite membranes successfully separated feed mixture containing 4.6 wt% water by exhibiting water fluxes of 0.024 and 0.019 kg/m(2)h, whereas the corresponding selectivities were found to be as high as 182 and 225, respectively.