A multiple regression model approach was developed to estimate buffering indices, as well as biogas and methane productions in an upflow anaerobic sludge blanket (UASB) reactor treating coffee wet wastewater. Five input variables measured (pH, alkalinity, outlet VFA concentration, and total and soluble COD removal) were selected to develop the best models to identify their importance on methanation. Optimal regression models were selected based on four statistical performance criteria, viz. Mallow’s Cp statistic (Cp), Akaike information criterion ( AIC), Hannan– Quinn criterion ( HQC), and Schwarz–Bayesian information criterion ( SBIC). The performance of the models selected were assessed through several descriptive statistics such as measure of goodness-of-fit test (coefficient of multiple determination, R2; adjusted coefficient of multiple determination, Adj-R2; standard error of estimation, SEE; and Durbin–Watson statistic, DWS), and statistics on the prediction errors (mean squared error, MSE; mean absolute error, MAE; mean absolute percentage error, MAPE; mean error, ME and mean percentage error, MPE). The estimated model reveals that buffering indices are strongly influenced by three variables (volatile fatty acids (VFA) concentration, soluble COD removal, and alkalinity); while, pH, VFA concentration and total COD removal were the most significant independent variables in biogas and methane production. The developed equation models obtained in this study, could be a powerful tool to predict the functionability and stability for the UASB system.

The paper’s objective was to present the results of predicting the stiffness modulus of a recycled mix containing a blended road binder with foamed bitumen and emulsified bitumen. The Sm (acc. to IT-CY) indirect tensile test was used at temperatures of -10°C, 5°C, 13°C and 25°C. Prediction of the stiffness modulus accounted for the effect of temperature, the type of road binders, the sampling location and the type of technology selected. All effects, except temperature, were included in the model by entangling their effects through recycled base course physical and mechanical characteristics, such as indirect tensile strength, compressive strength, creep rate, air void content and moisture resistance. As a result, it was possible to determine a regression model based on multiple regression with a coefficient of determination R² = 0.78. Temperature and compressive strength were found to have the strongest effect on the variability of stiffness modulus. However, indirect tensile strength also significantly affected the Sm characteristic. In addition, FB-RCM (foamed bitumen) recycled mixtures proved to be more favourable than EB-RCM (emulsified bitumen) mixtures as they exhibited a lower deformation rate while retaining limited stiffness.