Hydrothermal liquefaction of Nostoc ellipsosporum biomass grown in municipal wastewater under optimized conditions for bio-oil production.

Microalgae offer numerous potential applications, however the industrial scale-up of algal technology still remains a challenge due to high production cost. Optimization of growth conditions and integration with waste streams can improve the economic viability of microalgal production systems. This study investigated on the optimal growth conditions of microalgae Nostoc ellipsosporum cultivated in municipal wastewater with the objective of achieving maximum biomass production, nutrient removal efficiency and bio-oil yield. The effect of light intensity, photoperiod, wavelength, aeration and growth media composition were studied. Different formulations of municipal wastewater blended with Fog's nutrient were used as growth medium. Optimization of growth conditions and acclimatization to wastewater enhanced the biomass yield of Nostoc ellipsosporum from 1.42 to 2.9 g L-1, achieving 87.59% of nitrogen removal and 88.31% of phosphate removal from wastewater. Furthermore, hydrothermal liquefaction of biomass produced bio-oil yield of 24.62% at 300 °C.

Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network.

Anaerobic treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor (ATFBR) with a mesoporous granular activated carbon (GAC) as a support material. The experimental protocol was defined to examine the effect of the maximum organic loading rate (OLR), hydraulic retention time (HRT), the efficiency of the reactor and to report on its steady-state performance. The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/(m3 x d). The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m3/(m3 x d) of the reactor. With the increase of OLR from 83.7 kg COD/(m3 x d), the COD removal efficiency decreased. Also an artificial neural network (ANN) model using multilayer perceptron (MLP) has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data, the experimental values obtained have been used. The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data. The mean square error (MSE) was found to be only 0.0146.