BIOCHEMICAL METHANE POTENTIAL TESTING AND MODELLING FOR INSIGHT INTO ANAEROBIC DIGESTER PERFORMANCE
Anaerobic digestion uses a mixed, microbial community to convert organic wastes to biogas, thereby generating a clean renewable energy and reducing greenhouse gas emissions. However, few studies have quantified the relationship between waste composition and the subsequent physical and chemical changes in the digester. This Ph.D. dissertation aimed to gain new knowledge about how these differences in waste composition ultimately affect digester function. This dissertation examined three areas of digester function: (1) hydrogen sulfide production, (2) digester foaming, and (3) methane yield.
To accomplish these aims, a variety of materials from four different large-scale field digesters were collected at different time points and from different locations within the digester systems, including influent, liquid in the middle of the digesters, effluent, and effluent after solids separation. The materials were used for biochemical methane potential (BMP) tests in 43 lab-scale lab-digester groups, each containing triplicate or duplicate digesters. The materials from field digesters and the effluents from the lab-digesters were analyzed for an extensive set of chemical and physical characteristics. The three areas of digester function were examined with the physical and chemical characteristics of the digester materials and effluents, and the BMP performances.
Hydrogen sulfide productions in the lab-digesters ranged from non-detectable to 1.29 mL g VS-1. Higher H2S concentrations in the biogas were observed within the first ten days of testing. The initial Fe(II) : S ratio and OP concentrations had important influences on H2S productions. Important parameters of digester influents related to digester foaming were the ratios of Fe(II) : S, Fe(II) : TP, and TVFA : TALK; and the concentrations of Cu. Digesters receiving mixed waste streams could be more vulnerable to foaming. The characteristics of each waste type varied significantly based on substrate and inoculum type, and digester functioning. The influent chemical characteristics of the waste significantly impacted all aspects of digester function. Using multivariate statistics and machine learning, models were developed and the prediction of digester outcomes were simulated based on the initial characteristics of the waste types.