Optimization and Modifications of Thermodynamic Models for the Biomass Gasification Process to Predict the Syngas Composition

다국어 초록 (Multilingual Abstract)

Global energy demand is mostly fulfilled by the burning of fossil fuels and their derivatives like coal, oil, and natural gas. These conventional energy sources have dominated all the other sources such as wind, solar, and biomass energy. Due to rapid industrialization and use of fossil fuels have hosted several environmental issues and pollutants like greenhouse gas emissions. Modern research paved that sustainable energy sources like wind, solar, and biomass have the potential to help meet the environmental and energy demand problems of the world. Renewable biomass conversion is being utilized as the most promising clean energy source. Among all biomass conversion technologies like combustion, pyrolysis, and gasification, etc., biomass gasification is the most reliable thermochemical conversion technology which converts the biomass into gaseous fuel like H2, CO, CH4. This producer gas can be used for heat, power, and liquid fuel generation via various synthesis technologies.
The main objective of this study is to develop the different thermochemical equilibrium biomass gasification models for the downdraft gasifiers. There are two types of equilibrium models: (a) stoichiometric equilibrium models, (b) Non-stoichiometric equilibrium models. Stoichiometric equilibrium models are based on the chemical reactions involved inside the gasifier. A set of chemical reactions is selected and their equilibrium constants are calculated to predict the syngas composition and behavior of other parameters like gasification agent, temperature, and moisture contents in the available biomass sample. While non-stoichiometric equilibrium models are independent of the reaction mechanism involved in the gasification process. These models are based on the components of the product gas and the equilibrium conditions are achieved by the minimization of the Gibbs free energy to predict the syngas gas composition. These models are easy to develop in modern computers and their applications.
Finally, to improve efficiency, these developed models are modified and optimized with the correction factors to get the best suitable validations against the published experimental studies. These correction factors are calculated based on the large data set of experimental results to enhance the efficiency and to reduce the errors of the predicted results. The results of the developed modified models are also compared and validated with the already published experimental and modelling studies. In stoichiometric models, the overall RMSE is reduced from 2.59 and 2.77 to 2.30 and 2.68 respectively and in non-stoichiometric models, the overall RMSE is reduced from 2.25 and 1.86 to 1.49 and 1.23 respectively. For the future perspective, this study can be very helpful in designing the new gasification system to integrate the modern combine heat and power generation systems and synthesis of renewable liquid fuels through liquefaction technologies.

목차 (Table of Contents)

• Chapter 1 Introduction 1
• 1.1 Background 1
• 1.2 Biomass characteristics 1
• 1.3 Biomass conversion technologies 4
• 1.4 Overview of biomass gasification 6
• 1.5 The Objectives of the study 8
• 1.6 Summary of the dissertation 9
• Chapter 2 Literature review 11
• 2.1 Introduction 11
• 2.2 Types of gasifiers 12
• 2.2.1 Downdraft gasifier 12
• 2.2.2 Updraft gasifier 13
• 2.2.3 Fluidized gasifier 14
• 2.3 Reactions involved in gasification 16
• 2.4 Modelling of the biomass gasification process 17
• Chapter 3 Thermodynamic equilibrium modelling 22
• 3.1 The stoichiometric thermodynamic equilibrium model 22
• 3.2 The non-stoichiometric thermodynamic equilibrium model 26
• Chapter 4 Results and discussions 32
• 4.1 Model Accuracy 32
• 4.2 Stoichiometric model modifications 34
• 4.3 Non stoichiometric model modifications 34
• 4.4 Stoichiometric model implementation 36
• 4.5 Non stoichiometric model implementation 39
• 4.6 Stoichiometric model validations 40
• 4.7 Non-stoichiometric models validations 43
• 4.8 Comparison of the objective function for stoichiometric models 45
• 4.9 Comparison of the objective function for non-stoichiometric models 47
• 4.10 Comparison of the stoichiometric and non-stoichiometric models 49
• 4.11 Comparison of the syngas composition for stoichiometric models 50
• 4.12 Comparison of the syngas composition for non-stoichiometric models 54
• 4.13 Effect of parameters on syngas composition 58
• 4.13.1 Effect of biomass moisture content 58
• 4.13.2 Effect of gasification temperature 60
• 4.13.3 Effect of Equivalence Ratio (ER) 61
• Chapter 5 Conclusion and recommendations 62
• 초록 65
• Bibliography 67
• Resume 73