Parametric data from anaerobic digestion processes are normally collected once every couple of days and not daily. As a result, only a small amount of data could be collected and this is not sufficient for the neural network analysis. In this research, interpolations were used during the modelling process to increase the sample data used for Elman neural network (Elman NN) modelling. Laboratory digestion of silage cornstalk was conducted for 54 days, and a portion of the biogas data was used for training the Elman neural network (Elman NN) model, while the remaining biogas data were used to verify the prediction capability of the model. Compared to the Elman NN model without interpolations, using an interpolation coefficient of 0.2 increased the number of experimental data from 54 to 266 and the correlation coefficient of prediction data and sampling data from 0.7966 (for no-interpolation) to 0.9962 (for cubic spline interpolation) and 0.9942 (for piecewise linear interpolation). In addition, the mean square error decreased from 0.1190 (for no interpolation) to 0.001 (for cubic spline interpolation) and 0.001 (for piecewise linear interpolation), while the average relative error decreased from 63.04% (for no interpolation) to 3.93% (for cubic spline interpolation) and 4.01% (for piecewise linear interpolation). The Elman NN simulation results thus showed that the interpolation algorithm can greatly improve the prediction accuracy of biogas production from an anaerobic digestion process.

Dairy industry contributes to the pollution of the environment, both in quality and quantity. It generates about 0.2 to 10 L of effluent per liter of milk processed. Up-flow anaerobic sludge blanket is most suitable for biodegradable wastes, hence the present study evaluated the performance of UASBR through a laboratory model (25 liters of volume) for treating the dairy effluent with maintained psychrophilic temperature (15-20ºC) at phase I and mesophilic temperature (30-40ºC) at phase II. This model was studied for its treatment efficiency in terms of COD reduction. In the phase I, the average varying influent COD applied over the model are 1684, 2693, 3160, 3637, and 4059 mg/L with flow rates for each average influent COD as 4.80, 9.60, 14.40, 19.20 and 24.00 L/d. It was found that, successful COD removal of 91.42% was for the operating conditions of OLR at 0.039 kg COD/kg VSS day, VLR at 0.70 kg COD/m3 day and HRT at 5.21 days. In the phase II the average varying influent COD of 2316, 2827, 3329, 3908 and 4522 mg/L were applied with same flow rates. The experimental work on UASBR model was found successful with 94.70% COD removal under the operating conditions of OLR at 0.037 kg COD/kg VSS day, VLR at 0.630 kg COD/m3day and HRT at 5.21 days. The reactor achieved BOD, TSS, TDS, N and P removal efficiency, observed in phase I and II, as 91%, 82%, 89%, 42% and 46% and 96.4%, 86%, 91%, 41% and 50% respectively.

Leachate recirculation is a leachate management technique and an option for faster stabilization of MSW. The objective of this research is to highlight the effects of leachate recirculation on waste stabilization in simulated bioreactor. The study was conducted in a laboratory in a cylindrical shaped bioreactor loaded with MSW waste maintained under controlled anaerobic condition. The leachate quality was regularly measured and operating parameters like pH, VFA, alkalinity, etc. were found in the optimum range of anaerobic degradation. The leachate recirculated bioreactor is an effective option for MSW management, as COD removal observed was 96% during the study period. These observations indicate that the leachate recirculation technique is a viable approach to treat landfill leachate and stabilize the MSW.