Anaerobic Digestion and Biogas Potential: Simulation of Lab and Industrial-Scale Processes

In this study, a simulation was carried out using BioWin 3.1 to test the capability of the software to predict the biogas potential for two different anaerobic systems. The two scenarios included: (1) a laboratory-scale batch reactor; and (2) an industrial-scale anaerobic continuous lagoon digester....

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Main Authors: Ihsan Hamawand, Craig Baillie
Format: Article
Language:English
Published: MDPI AG 2015-01-01
Series:Energies
Subjects:
Online Access:http://www.mdpi.com/1996-1073/8/1/454
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spelling doaj-7899db949db2480cabf96aec8147796c2020-11-24T20:50:03ZengMDPI AGEnergies1996-10732015-01-018145447410.3390/en8010454en8010454Anaerobic Digestion and Biogas Potential: Simulation of Lab and Industrial-Scale ProcessesIhsan Hamawand0Craig Baillie1National Centre for Engineering in Agriculture (NCEA), University of Southern Queensland (USQ), Toowoomba, 4350 QLD, AustraliaNational Centre for Engineering in Agriculture (NCEA), University of Southern Queensland (USQ), Toowoomba, 4350 QLD, AustraliaIn this study, a simulation was carried out using BioWin 3.1 to test the capability of the software to predict the biogas potential for two different anaerobic systems. The two scenarios included: (1) a laboratory-scale batch reactor; and (2) an industrial-scale anaerobic continuous lagoon digester. The measured data related to the operating conditions, the reactor design parameters and the chemical properties of influent wastewater were entered into BioWin. A sensitivity analysis was carried out to identify the sensitivity of the most important default parameters in the software’s models. BioWin was then calibrated by matching the predicted data with measured data and used to simulate other parameters that were unmeasured or deemed uncertain. In addition, statistical analyses were carried out using evaluation indices, such as the coefficient of determination (R-squared), the correlation coefficient (r) and its significance (p-value), the general standard deviation (SD) and the Willmott index of agreement, to evaluate the agreement between the software prediction and the measured data. The results have shown that after calibration, BioWin can be used reliably to simulate both small-scale batch reactors and industrial-scale digesters with a mean absolute percentage error (MAPE) of less than 10% and very good values of the indexes. Furthermore, by changing the default parameters in BioWin, which is a way of calibrating the models in the software, as well, this may provide information about the performance of the digester. Furthermore, the results of this study showed there may be an over estimation for biogas generated from industrial-scale digesters. More sophisticated analytical devices may be required for reliable measurements of biogas quality and quantity.http://www.mdpi.com/1996-1073/8/1/454wastewateranaerobic digestion (AD)biogasBioWinmeat industrysimulation
collection DOAJ
language English
format Article
sources DOAJ
author Ihsan Hamawand
Craig Baillie
spellingShingle Ihsan Hamawand
Craig Baillie
Anaerobic Digestion and Biogas Potential: Simulation of Lab and Industrial-Scale Processes
Energies
wastewater
anaerobic digestion (AD)
biogas
BioWin
meat industry
simulation
author_facet Ihsan Hamawand
Craig Baillie
author_sort Ihsan Hamawand
title Anaerobic Digestion and Biogas Potential: Simulation of Lab and Industrial-Scale Processes
title_short Anaerobic Digestion and Biogas Potential: Simulation of Lab and Industrial-Scale Processes
title_full Anaerobic Digestion and Biogas Potential: Simulation of Lab and Industrial-Scale Processes
title_fullStr Anaerobic Digestion and Biogas Potential: Simulation of Lab and Industrial-Scale Processes
title_full_unstemmed Anaerobic Digestion and Biogas Potential: Simulation of Lab and Industrial-Scale Processes
title_sort anaerobic digestion and biogas potential: simulation of lab and industrial-scale processes
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2015-01-01
description In this study, a simulation was carried out using BioWin 3.1 to test the capability of the software to predict the biogas potential for two different anaerobic systems. The two scenarios included: (1) a laboratory-scale batch reactor; and (2) an industrial-scale anaerobic continuous lagoon digester. The measured data related to the operating conditions, the reactor design parameters and the chemical properties of influent wastewater were entered into BioWin. A sensitivity analysis was carried out to identify the sensitivity of the most important default parameters in the software’s models. BioWin was then calibrated by matching the predicted data with measured data and used to simulate other parameters that were unmeasured or deemed uncertain. In addition, statistical analyses were carried out using evaluation indices, such as the coefficient of determination (R-squared), the correlation coefficient (r) and its significance (p-value), the general standard deviation (SD) and the Willmott index of agreement, to evaluate the agreement between the software prediction and the measured data. The results have shown that after calibration, BioWin can be used reliably to simulate both small-scale batch reactors and industrial-scale digesters with a mean absolute percentage error (MAPE) of less than 10% and very good values of the indexes. Furthermore, by changing the default parameters in BioWin, which is a way of calibrating the models in the software, as well, this may provide information about the performance of the digester. Furthermore, the results of this study showed there may be an over estimation for biogas generated from industrial-scale digesters. More sophisticated analytical devices may be required for reliable measurements of biogas quality and quantity.
topic wastewater
anaerobic digestion (AD)
biogas
BioWin
meat industry
simulation
url http://www.mdpi.com/1996-1073/8/1/454
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AT craigbaillie anaerobicdigestionandbiogaspotentialsimulationoflabandindustrialscaleprocesses
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