Optimization of Process Variables for C-massecuite Exhaustion in a Nigerian Sugar Refinery

Sucrose in the final molasses continues to be a source of major financial loss to sugar refineries worldwide. This study therefore aims at rectifying this anomaly. In this study, the final molasses exhaustibility was predicted using Adaptive Neuro Fuzzy Inference System (ANFIS) and Response Surface Methodology (RSM) based ondata generated from molasses sample collected from the recovery end of refining processes. The results show that both models are able to predict the final molasses exhaustibility with sufficient accuracy. The optimum sucrose recovery of 49.18% was achieved at the point when Brix0 is 96.00%, Purity of 65.00% and pH of 4.50. Also, both models agree on the combination of purity and pH as the two factors interaction that have optimal effect on the sucrose recovery. The correlation coefficient (R2) value obtained for ANFIS was 0.96 while that of RSM was 0.99. Thus, the RSM model has better prediction performance than ANFIS.