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A Bayesian Approach to The Assessment of Fuel Composition Variability Effects on Grate-bed Biomass Combustion


A Bayesian Approach to The Assessment of Fuel Composition Variability Effects on Grate-bed Biomass Combustion

Hosseini Rahdar, Mohammad ORCID: https://orcid.org/0000-0002-0293-2296 (2020) A Bayesian Approach to The Assessment of Fuel Composition Variability Effects on Grate-bed Biomass Combustion. PhD thesis, Concordia University.

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Combustion systems are the most energy-intensive facilities in the world. They are responsible for releasing the majority of the greenhouse gases (GHG) and NOx into the earth’s atmosphere. Biomass is the only renewable energy source consisting of fixed carbon elements which can be substituted for fossil fuels in combustion systems. The main distinction between biomass and fossil fuel combustion is fewer pollutant emissions of biomass combustion, as well as, biomass combustion’s lower price and simpler storage facility. So far, direct combustion of the solid biomass is the most popular method, both thermally and economically, among all various bioenergy systems, which is due to the price of biofuels process cost. Grate firing technology is of interest to burn solid biomass because it has less sensitivity to feed composition and size, which shows the excellent potential of this technology. However, owing to the intrinsic composition variability of biomass, there are still uncontrolled deflections associated with biomass combustors operations.
This study is an effort to quantify the overall impact of fuel compositions variability on moving bed biomass combustion, which will facilitate the understanding of biomass combustion. Randomly selected biomass pellets were individually investigated via a Thermogravimetric Analysis (TGA) to specify the fuel compositions; moisture, volatile, char, and ash. This data, together with the predefined fuel composition provided by fuel supplier are utilized to train a model using a Bayesian approach to populate our measured data. Simultaneously, a 1D transient numerical model of moving bed biomass combustion is deliberately developed corresponding to the research goals. The model iteratively runs with distributed fuel composition made by the Bayesian data generator and simulates the combustor under uncertain conditions. The comprehensive thermo-economic and environmental analysis of the biomass boiler operated with the three most common biomass types was conducted. Specifically, this includes biomass pellets, wood waste, and municipal solid waste and through this research showed that biomass pellets are the most efficient in terms of thermal operation and financial revenue. An experiment-based approach to the composition uncertainty impact of biomass pellets and bamboo chips on moving bed combustors were also practiced. While a notable heat flux deviation from mean operation conditions was observed for both, the pelletizing helped pellets to limit the level of uncertainty to a satisfying degree. Higher char content can limit the combustion uncertainty to a strong extent, while the moisture content was found to be the main contributor to the level of uncertainty. As well, NOx emission arising from biomass combustion fluctuated up to 17% due to composition variability. Finally, combustor operations under more reliable input data via the Bayesian data generator showed a remarkable system deviation from that of predefined input conditions. Overlooking the fuel compositions variability caused an overestimation of heat generation of up to 8.5%. Moreover, a notable amount of unburned biomass particles was sent to an ash bin, which is not in line with biomass harvesting sustainability. To avoid this in the future, the system must be regulated to correspond to the fuel compositions offered by the Bayesian model.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Hosseini Rahdar, Mohammad
Institution:Concordia University
Degree Name:Ph. D.
Program:Building Engineering
Date:26 August 2020
Thesis Supervisor(s):Fuzhan, Nasiri and Bruno, Lee
ID Code:987502
Deposited By: Mohammad HosseiniRahdar
Deposited On:29 Jun 2021 20:51
Last Modified:29 Jun 2021 20:51
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