Concentrated solar thermal gasification of biomass for continuous electricity generation

Fang, Yi (2024) Concentrated solar thermal gasification of biomass for continuous electricity generation. PhD thesis, University of Glasgow.

Abstract

Bioenergy production is one of the key strategies for reducing CO2 emissions and replacing fossil fuels. Along with other renewable energy sources and emission reduction methods, it provides a variety of solutions for addressing global energy challenges and climate change. Although gasification-based bioenergy generation has been extensively researched, there are still challenges in terms of energy efficiency, environmental sustainability, and economic viability in practical applications. Concentrated solar thermal gasification of biomass (CSTGB) system offers a promising solution. It utilizes concentrated solar thermal energy to enhance gasification efficiency, improve environmental sustainability, increase energy security, and possess potential economic viability.

Under optimal conditions geared towards maximizing energy conversion efficiency, the CSTGB system boasts impressive results, including a remarkable 30% improvement in biomass utilization and substantial 40% increase in total energy efficiency compared to conventional gasification methods. This represents a notable leap forward in the quest for sustainable and efficient bioenergy production. Gasification technologies have been widely investigated for converting biomass into syngas, which can be further utilized for heat or electricity generation. To optimize this process, a stochastic gasification kinetic model has been developed, employing a Monte Carlo (MC) approach coupled with the powerful random forest (RF) algorithm. This innovative approach aims to predict the ideal gasification process parameters, encompassing variables such as water content, particle size, porosity, thermal conductivity, emissivity, shape, and reaction temperature, all with the goal of achieving maximum producer gas yield and quality. The model’s accuracy and reliability have been rigorously confirmed through comparison with existing literature data, underscoring its value as a valuable tool for the design and operation of gasification processes.

This system emerges as a highly promising solution to reduce greenhouse gas (GHG) emissions and address energy cost challenges. To evaluate the system comprehensively, a life cycle assessment (LCA) and techno-economic analysis (TEA) were conducted with a focus on global warming potential (GWP) and economic feasibility. Sensitivity analysis has effectively pinpointed cost and emissions hotspots within the system. While the net present worth (NPW) of the proposed system at 30th year stands at approximately €–0.7 billion, two key strategies can be employed to enhance its economic viability. These strategies include a 19% reduction in operation and maintenance (O&M) costs to 43.9 €/MWh or a 20% increase in overall system efficiency. The proposed system has the potential to annually save 787.7 kgCO2- eq/tonwaste-wood and generate approximately 0.8 million MWh of electricity, concurrently promoting energy security and contributing significantly to carbon emission reduction. This synthesis of sustainable technologies underscores its pivotal role in our transition towards a greener and more energy-efficient future.

The multi-objective optimization (MOO) of the proposed system on LCA and TEA is conducted. The analysis employed the long short-term memory recurrent neural network (LSTM-RNN) algorithm and MC approach expand scenarios due to limited specialized models and experimental. Influenced by considerations related to carbon taxes (CT), the results highlight a robust optimal configuration capable of reducing GWP by 415,960 tons of CO2 and generating a NPW of €4,298 million over a 30-year life span. However, in the absence of CT revenue, the analysis reveals trade-offs, resulting in a reduction of 132,615 tons of CO2 and a net present worth of €3,042 million.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: T Technology > TA Engineering (General). Civil engineering (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: You, Dr. Siming
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84151
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 26 Mar 2024 13:26
• Last Modified: 26 Mar 2024 13:29
• Thesis DOI: 10.5525/gla.thesis.84151
• URI: https://theses.gla.ac.uk/id/eprint/84151

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