Suman Dutta

Artificial neural network modelling for removal of chromium (VI) from wastewater using physisorption onto powdered activated carbon

AbstractA three-layered feed-forward artificial neural network (ANN) model has been designed to predict the adsorption efficiency and adsorption capacity for the adsorptive removal of chromium (VI) from synthetic wastewater. The adsorbent dose, wastewater pH, initial pollutant concentration and contact time were used to develop the network. The data used to train and test the model were obtained from several batch experiments. Various algorithms and transfer functions for hidden layer were tested to find the most reliable network. Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithm gave the most satisfactory results for adsorption efficiency. Resilient and BFGS quasi-Newton backpropagation were the most suitable algorithm for adsorption capacity. The best combination of training algorithm and transfer function for adsorption efficiency was found to be trainrp and poslin, while poslin produced simulated results within 10% deviation for adsorption capacity. Eight to eleven neurons...

Watermelon juice concentration using ultrafiltration: Analysis of sugar and ascorbic acid

Raw watermelon juice was concentrated in a laboratory-scale flat-plate ultrafiltration system incorporating polyethersulfone membranes with a molecular weight cutoff of 5 kDa. The experiments have been carried out over a wide range of transmembrane pressures (100–300 kPa). The effect of ultrafiltration with molecular weight cutoff of 5 kDa polyethersulfone membrane on concentrate properties is reported. The total soluble solid content of the permeate was found to be very good, while the ascorbic acid content was on the lower side as compared to feed. The analyses of flux decay according to fouling models reported in the literature revealed that the formation of a cake layer covering the entire surface of the membrane is the main cause of the membrane fouling.

Recent Advances in Production of Biofuel and Commodity Chemicals from Algal Biomass

Algal biomass is considered among promising sources of biorenewable feedstock which is capable to meet growing energy and chemical demands. Agile growth rate and high oil yield per acre of land make it an interesting resource to produce biofuel in a sustainable manner. Indeed, various technologies such as pyrolysis, gasification, liquefaction, catalytic cracking, enzymatic, and biochemical methods have been reported for production of biofuel from algal biomass. Interestingly, majority of the processes can be classified into three categories namely thermal route, catalytic route, and biological route, at a broader scale, thus forms the basis of this chapter. On the contrary, algal biomass has several other promising aspects, such as to produce high value commodity chemicals and monomers, besides conventional application as a source of fuel. Overall, algal biomass conversion to biofuel and commodity chemicals holds a great future. However, certain challenges (such as techno-economic feasibility and biogenic impurities) associated with algal biomass conversion technologies need to be overcome before it scales up to a commercial process. Thus, in this chapter we have discussed recent advances in biofuel production from algal biomass via different routes with emphasis on some of the emerging routes which holds more economic value as compared to traditional processes.