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In this study, the capabilities of response surface methodology (RSM) and artificial neural networks (ANN) for modeling and optimization of ethanol production from glucoseusing Saccharomyces cerevisiae in batch fermentation process were investigated. Effect of three independent variables in a defined range of pH (4.2-5.8), temperature (20-40ºC) and glucose concentration (20-60 g/l) on the cell growth and ethanol production was evaluated. Results showed that prediction accuracy of ANN was apparently similar to RSM. At optimum condition of temperature (32°C), pH (5.2) and glucose concentration (50 g/l) suggested by the statistical methods, the maximum cell dry weight and ethanol concentration obtained from RSM were 12.06 and 16.2 g/l whereas experimental values were 12.09 and 16.53 g/l, respectively. The present study showed that using ANN as fitness function, the maximum cell dry weight and ethanol concentration were 12.05 and 16.16 g/l, respectively. Also, the coefficients of determination for biomass and ethanol concentration obtained from RSM were 0.9965 and 0.9853 and from ANN were 0.9975 and 0.9936, respectively. The process parameters optimization was successfully conducted using RSM and ANN; however prediction by ANN was slightly more precise than RSM. Based on experimental data maximum yield of ethanol production of 0.5 g ethanol/g substrate (97 % of theoretical yield) was obtained

BACKGROUND In this work, a pervaporation membrane bioreactor (PVMBR) with the novel ethanol permselective membrane was used to achieve ethanol production with high yield. For this purpose, ethanol produced by Saccharomyces cerevisiae from glucose and pervaporation was performed via dense hydrophobic polydimethylsiloxane (PDMS) having carbon nanotube‐graphene oxide (CNT‐GO) mixed matrix membrane. Different percentages of CNT‐GO (0, 0.25, 0.5, 0.75, and 1) were loaded in polymer matrix. Synthesized membranes were analyzed using FESEM, AFM, FTIR and XRD. RESULTS The bonding of groups in CNT‐GO, that was synthesized in this work, and membranes was characterized and confirmed using FTIR test. Considering the existence of 0.75 wt% of CNT‐GO in PDMS showed the highest ethanol selectivity of 22.5 in synthetic ethanol‐water solution, this incorporation was selected to use in PVMBR. The high ethanol selectivity was proved using XRD patterns. Also, FESEM indicated the optimum dispersion of nanoparticles at 0.75% of loading and AFM proofed the optimum surface roughness at this percentage. CONCLUSIONS The results showed that maximum ethanol production in PVMBR was achieved after 44 h with productivity of 0.5g.l−1.h−1 in feed side and 9.89 g.l−1.h−1 in permeate side. In fact, PVMBR enhanced the ethanol concentration up to 26 times. © 2020 Society of Chemical Industry

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We investigated the adsorption properties of HCN on dianiline (as a model for polyaniline, denoted here as 2PANI) using density functional theory (DFT) by considering the geometry as well as the electronic property. Contact information of 2PANI with HCN at different configurations was studied and the adsorption energy was calculated in each case. UV-vis analysis, density of states (DOS) and natural bond orbital (NBO) analysis were used to study the interaction of HCN with 2PANI at different configurations. Our calculations showed that HCN could be adsorbed on 2PANI with adsorption energy of ∼ −4.3 kcal mol