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

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

Operation Adaptation of Moving Bed Biomass Combustors under Various Waste Fuel Conditions

This paper analyzes a moving grate biomass boiler operating with three alternative waste fuels, including biomass pellets, wood waste, and refuse-derived fuel (RDF) from a combination of thermal, economic, and environmental perspectives. The focus of this paper is on system functionality adaptation to retrofit the current systems operational conditions. A one-dimensional numerical bed model integrated with a black-box overbed model was developed to carefully investigate the fuel bed’s thermal characteristics, as well as the boiler’s output. According to the results, the system operates more efficiently under the biomass pellets feeding and annually generates 548 GJ heat, while it drops significantly in other scenarios. The system was economically evaluated based on a 25-year life cycle cost analysis. Subsequently, an internal rate of return (IRR) of 36% was calculated for biomass pellets, while the value reduced by 50% and 27% regarding wood waste and RDF, respectively. The fuel cost was identified as the main contributor to the total life cycle cost of the heating system, regardless of which feeding fuel was utilized. A long-term environmental impacts assessment of the boiler operation emerged, to show how plant-based fuels can significantly decrease the impacts of climate change that have originated from fossil fuel usage. The current study concludes that all the proposed scenarios are feasible to different degrees, and can extensively benefit a diverse set of energy sectors

Fault Identification and Fault Impact Analysis of The Vapor Compression Refrigeration Systems in Buildings: A System Reliability Approach

The Vapor Compression Refrigeration System (VCRS) is one of the most critical systems in buildings typically used in Heating, Ventilation, and Air Conditioning (HVAC) systems in residential and industrial sections. Therefore, identifying their faults and evaluating their reliability are essential to ensure the required operations and performance in these systems. Various components and subsystems are included in the VCRS, which need to be analyzed for system reliability. This research’s objective is conducting a comprehensive system reliability analysis on the VCRS by focusing on fault identification and determining the fault impacts on these systems. A typical VCRS in an office building is selected for this research regarding this objective. The corresponding reliability data, including the probability distributions and parameters, are collected from references to perform the reliability evaluation on the components and subsystems of the VCRS. Then the optimum distribution parameters have been obtained in the next step as the main findings. Additionally, by applying optimization techniques, efforts have been taken to maximize the system’s reliability. Finally, a comparison between the primary and the optimized systems (with new distribution parameters) has been performed over their lifetime to illustrate the system’s improvement percentage