Hydrolysis modeling for combined primary and RAS sludge fermentation at water resource recovery facilities

Abstract

Water resource recovery facilities (WRRFs) across the country have implemented primary sludge (PS) and return activated sludge (RAS) fermenters to generate soluble carbon and volatile fatty acids (VFA) needed for biological nutrient removal (BNR). In this study, SUMO simulations were utilized to capture fermentation trends of PS and RAS, coupled with experimental data. Additionally, through this work, key parameters for modeling of hydrolysis were identified. The reduction factor for anaerobic hydrolysis (η_HYD), the yield of H₂ during fermentation, and the rate of methanogenic growth were found to be crucial parameters when modeling PS and RAS fermentation. Two different hydrolysis models were used to calibrate the experimental data, SUMO1 and a modified version of the SUMO1 model (SUMO1_mod); the latter as a dual hydrolysis model that distinguishes between slowly biodegradable COD from influent sources (X_B) and from endogenous biomass decay (X_BE). The results of this study showed that several factors in the overall hydrolysis rate equation changed with an increase in the proportion of PS blend. Firstly, with an increasing PS percentage, the product of the hydrolysis rate and η_HYD increased due to the higher X_B from influent, as opposed to the slower degrading X_BE from biomass decay. The best fitting anaerobic hydrolysis reduction factor and hydrolysis rate product shifted from 0.2 to 0.4 for the SUMO1 model, and 0.12 to 0.3 as a weighted average based on the PS/RAS ratio for the SUMO1_mod SUMO1 model. Additionally, the composition of the solids changed with an increase in PS percentage, resulting in a much lower proportion of heterotrophic biomass (X_Het) per g VSS but a higher X_B content per g VSS. Finally, the model structure changed as the solids composition changed, impacting the hydrolysis rate. With 100% RAS fermentation, both X_B and X_Het concentrations affected the rate following Monod-like kinetics. However, as the PS content increased, the model indicated that the rate kinetics might be influenced only by the X_Het content. This work provides guidance and a framework through which modeling can be used to predict fermentation rates that can be achieved through combined PS and RAS fermentation.