Mathematical model applications for the bioaugmented anaerobic digestion (BAD) process seem to be lacking in the scientific literature, even more so when related to microbiological data. The present study suggests a simplified mathematical model to investigate and simulate the process kinetics of bioaugmented anaerobic digestion (BAD) aimed at improving biogas production from wheat straw (WS). Bioaugmented conditions were obtained through a mixed inoculum of anaerobic ruminal fungi (ARF) and hydrogen-producing fermenting bacteria (F210) added to a methanogenic inoculum. The investigation focused on two process configurations characterized by a mono (I-BAD) and two-stage (II-BAD) process and a conventional anaerobic digestion (AD) control test. Each configuration was used on two operating scales (i.e., 120 ml and 12,000 ml reactor volume) to provide different data sets for the calibration and validation of the mathematical model proposed. The model calibration step was used to determine the optimal values of selected parameters displaying higher significance for experimental result predictability. The model calibration results highlighted a similar behavior for both BAD tests, which was further strengthened by a statistical analysis supporting the observed correlation regardless of the BAD configuration involved. The BAD configuration always enhanced the CH4 production (>70%) with a faster kinetic in the II-BAD test. The microbiological results support the superior performance of the II-BAD test, displaying higher Archaea fractions (up to 14.5% on day 23) with values more than double compared to I-BAD and triple compared to the AD test. Furthermore, the presence of Methanosarcina inside the Archaea guild (6.4% and 4.5% at days 11 and 61, respectively) ensures a greater diversification of the metabolic pathways and supports the strength of the process performance. Cell density values are strongly in line with these results.

A simplified model to simulate bioaugmented anaerobic digestion of lignocellulosic biomass: Biogas production efficiency related to microbiological data

Ferraro A.
;
2019-01-01

Abstract

Mathematical model applications for the bioaugmented anaerobic digestion (BAD) process seem to be lacking in the scientific literature, even more so when related to microbiological data. The present study suggests a simplified mathematical model to investigate and simulate the process kinetics of bioaugmented anaerobic digestion (BAD) aimed at improving biogas production from wheat straw (WS). Bioaugmented conditions were obtained through a mixed inoculum of anaerobic ruminal fungi (ARF) and hydrogen-producing fermenting bacteria (F210) added to a methanogenic inoculum. The investigation focused on two process configurations characterized by a mono (I-BAD) and two-stage (II-BAD) process and a conventional anaerobic digestion (AD) control test. Each configuration was used on two operating scales (i.e., 120 ml and 12,000 ml reactor volume) to provide different data sets for the calibration and validation of the mathematical model proposed. The model calibration step was used to determine the optimal values of selected parameters displaying higher significance for experimental result predictability. The model calibration results highlighted a similar behavior for both BAD tests, which was further strengthened by a statistical analysis supporting the observed correlation regardless of the BAD configuration involved. The BAD configuration always enhanced the CH4 production (>70%) with a faster kinetic in the II-BAD test. The microbiological results support the superior performance of the II-BAD test, displaying higher Archaea fractions (up to 14.5% on day 23) with values more than double compared to I-BAD and triple compared to the AD test. Furthermore, the presence of Methanosarcina inside the Archaea guild (6.4% and 4.5% at days 11 and 61, respectively) ensures a greater diversification of the metabolic pathways and supports the strength of the process performance. Cell density values are strongly in line with these results.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/237521
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