Title : Experimental investigation and parametric optimization with RSM and ANN for floating drum anaerobic bio digester
Abstract:
The primary goal of this research is to use artificial neural networks (ANN) and response surface methodology (RSM) to improve the ideal conditions for biogas yield from the anaerobic digestion of agricultural waste (rice straw). Temperature, pH, substrate concentration, and agitation time are considered model variables in the creation of prediction models. The experimental findings demonstrate the significant interacting effects (p < 0.05) of the liner model variables temperature, substrate concentration, pH, and agitation time. The findings show that, when comparing the ANN model to the RSM model, the ideal process parameters had an impact on the increase in biogas yield. When compared to the RSM model, the ANN model shows that it is significantly more accurate and calculates the values of maximum biogas yield.
Keywords: Agricultural waste, Temperature, Rice straw, Agitation time, Response surface temperature, artificial neural networks
