Abstract
A pilot scale Upflow Anaerobic Contactor (UAC), based on upflow sludge blanket principle, was designed to treat vinasse waste obtained from beet molasses fermentation. An assessment of the anaerobic digestion of vinasse was carried out for the production of biogas as a source of energy. Average Organic loading rate (OLR) was around 7.5 gCOD/m3/day in steady state, increasing upto 8.1 gCOD/m3/day. The anaerobic digestion was conducted at mesophilic (30–37 °C) temperature and a stable operating condition was achieved after 81 days with average production of 65 % methane which corresponded to a maximum biogas production of 85 l/day. The optimal performance of UAC was obtained at 87 % COD removal, which corresponded to a hydraulic retention time of 16.67 days. The biogas production increased gradually with OLR, corresponding to a maximum 6.54 gCOD/m3/day (7.4 % increase from initial target). A coupled Artificial Neural Network-Differential Evolution (ANN-DE) methodology was formulated to predict chemical oxygen demand (COD), total suspended solids (TSS) and volatile fatty acids (VFA) of the effluent along with the biogas production. The method incorporated a DE approach for the efficient tuning of ANN meta-parameters such as number of nodes in hidden layer, input and output activation function and learning rate. The model prediction indicated that it can learn the nonlinear complex relationship between the parameters and able to predict the output of the contactor with reasonable accuracy. The utilization of the coupled ANN-DE model provided significant improvement to the study and helps to study the parametric effect of influential parameters on the reactor output.
Prediction of biogas production rate from dry anaerobic digestion of food waste: Process-based approach vs. recurrent neural network black-box model
