Modeling Methane Emission of Wastewater Anaerobic Pond at Palm Oil Mill Using Radial Basis Function Neural Network

Abstract

Plant-based industries such as palm oil mills will generate wastewater rich in organic matter. Palm oil mill effluent (POME) treatment in Indonesia is still dominant with conventional methods without the capture of methane. This system does not know the value of methane emitted into the atmosphere. Measurement and testing of biomethane from anaerobic ponds of palm oil mills are relatively difficult because gas material is rapidly changing. An alternative methodology that is accurate through modeling with a radial basis function neural network (RBFNN) with abiotic variable input. The aim of this research is to find out an anaerobic pond methane emission model of POME and simulation to find out the dynamics of methane emissions. Methane emission data is measured by a TGS2611 methane gas sensor CH4-meter system and using closed static chambers. A sampling of wastewater and methane gas was conducted in October-November 2018. The results showed that the methane gas emission model was obtained in the AP with RBFNN. The best RBFNN model had a 5-5-3 network architecture, spread 0.11 and error-goals 0.0005, R 0.940652 and MSE 0.003166. The reliability of RBFNN in determining models with non-linear field data variables was quite good, which was influenced by the number of data patterns, types and accuracy of the variables, network architecture, and the ANN model used. The simulation and prediction of methane emissions in the lowest-moderate-highest variable value scenario found that the COD-R and VS-R variables greatly affected the anaerobic pond WWTP emissions of multiple feeding systems. Even so, inlet wastewater temperature and rainfall variables had not significantly affected methane gas emissions, because the temperature was in a mesophilic range (30-40 oC) and the effect of rainfall would depend mainly on the high-low levels of organic matter (COD and VS).