Modeling the performance of upflow anaerobic filters treating paper-mill wastewater using gene-expression programming

摘要

This study investigates the predictive ability of gene-expression programming (GEP) in the estimation of methane yield (Y_m) and effluent substrate (S_e) produced by two anaerobic filters. The modeling study was carried out using the data obtained from two upflow anaerobic filters - one mesophilic (35 °C) and one thermophilic (55 °C) - operated for the treatment of paper-mill wastewater under varying organic loadings. The GEP model was composed of three inputs, hydraulic retention time (T_(hr)), organic loading rate (R_(ol)), and influent substrate (St), and one output, either S_e or Y_m. The Stover-Kincannon model was also used for data analysis and to evaluate the prediction ability. Three statistical criteria, root mean square error (RMSE), determination coefficient (R~2), and Akaike's information criteria (AIC), were the means used for comparison. The results showed that the GEP approach predicted the performance of both anaerobic filters much better than the Stover-Kincannon model.