| Author: | Ayub, Yousaf |
| Title: | Sustainable valorization of biomass waste : process development, artificial intelligence based optimization, and decision-making |
| Advisors: | Ren, Jingzheng (ISE) Lee, Carman (ISE) |
| Degree: | Ph.D. |
| Year: | 2025 |
| Subject: | Biomass energy Waste products as fuel Sustainable development Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Industrial and Systems Engineering |
| Pages: | xxv, 322 pages : color illustrations |
| Language: | English |
| Abstract: | The primary objectives of this study are to develop, optimize, and decision-making for the sustainable valorization of biomass waste. It includes a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of thermal and biological valorization processes, and a sustainability analysis considering environmental, economic, energy, exergy, and safety (4E, 1S) parameters. Environmental performance was reviewed from existing literature, and also from the models developed in this study. Life Cycle Assessment (LCA) reflects thermal processes more sustainable than direct land disposal of biomass. Economic analysis includes payback period (PBP), and internal rate of return (IRR) results indicate that thermal processes, specifically gasification and pyrolysis, outperform land disposal in both economic and environmental aspects. Although anaerobic digestion (AD) is technically and environmentally feasible at domestic level, it has a longer payback period. Hence, thermal processes are considered better for biomass valorization compared to biological methods when there is large quantity of biomass waste. Sustainability evaluation of different thermal valorization processes have been performed. Hydrothermal gasification (HTG) is one of the thermal processes to convert biomass waste into valuable products. HTG process simulation model for syngas production was developed and artificial intelligence (AI) algorithms were applied to predict high-quality syngas production. Comparative analysis of Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGB), and Random Forest Regression (RFR) models identified XGB as the best predictor, with coefficient of determination (R²) values between 0.85-0.95 and mean square errors (MSE) between 0.008-0.01. Optimization based on process parameters such as temperature, pressure, and biomass concentration were analyzed which predict optimal hydrogen and methane yields around 540°C, 25 MPa, and 20% feedstock concentration. Energy analysis indicated a 61% efficiency, and economic analysis showed HTG to be at least 10% more cost-effective than coal, natural gas, or distillate oil for steam production. LCA confirmed HTG's advantage over direct land disposal of biomass in terms of economy, environmental impact, and energy efficiency. The analysis highlighted that process temperature and resident time significantly affect hydrogen and methane yields. This study also examines the different gasification routes for sustainable valorization of biomass waste through a novel tri-generation process involving gasification, solid oxide fuel cells (SOFC), and combined heat and power systems (CHP). Using Aspen Plus simulations and XGB, the optimal parameters were identified, with biomass to air ratio (BMR) being the most significant factor, achieving a R² greater than 0.97. The process demonstrated an exergy efficiency 34.6% higher than gasification. The tri-generation process, which includes torrefaction and SOFC, showed economic feasibility only above 90% efficiency. Particle Swarm Optimization (PSO) resulted in an energy efficiency of 57%, yielding 242.6 kg/ton of dimethyl ether (DME) at 667°C and 2 bar. The HDMR method predicted gasification outcomes with high accuracy, showing the efficient operation at 765°C, 0.59 BMR, and 1 bar. This integrated approach enhanced economic viability and environmental sustainability compared to traditional methods. Plasma gasification (PG) tri-generation process for biomass waste valorization and DME production has been developed, considering 4E sustainability. Process optimization performed by the application of a radial basis function surrogate algorithm. Optimized process enhanced the DME yield by 6%, with energy efficiencies of 44% and 48% for the base (without optimization) process. It produces 1271 kW of electricity from 10 t/h feedstock processing and has a sustainability index of 2.509. The PBP for the optimized process is 7.2 years at 70% efficiency, while the base process is not feasible below 90% efficiency. A co-gasification process for biomass and plastic waste to produce blue and green hydrogen was proposed. This model, with a 20 t/h capacity, can generate approximately 1079 kW of electric power and surplus electricity for producing around 213.5 kg/d of hydrogen through alkaline electrolysis. Economic analysis shows an IRR of 8% at 70% efficiency. Exergy analysis highlights the gasifier component's lowest efficiency, resulting in a 40% exergy loss, with evergoeconomics costs of approximately $6,561.3 and $6,541.9 per hour for the steam turbine and gasifier, respectively, suggesting potential improvements in these areas for enhanced sustainability. Finally, a comprehensive evaluation of biological and thermal waste valorization methods was conducted using the Interval Valued Fermatean Fuzzy Set (IVFFS) with the Dombi Operator (DO) integrated with the Analytical Hierarchy Process (AHP). The analysis assessed four waste valorization processes—anaerobic digestion, gasification, pyrolysis, and HTG—based on economic, environmental, technological, and social-governance criteria. The Advanced Combinative Distance-Based Assessment (CODAS) ranked these processes with gasification as the most sustainable with an assessment score (As) of 0.063, followed by pyrolysis (0.009) and HTG (-0.033). Threefold validation confirmed gasification’s sustainability. Furthermore, the process safety of biomass thermal valorization technologies, evaluated using the Numerical Descriptive Logistics Equation (NuD), found HTG to be the safest among HTG, pyrolysis, and gasification, with the lowest Process Safety Total Score (PSTS) of 210.2. HTG’s lower temperature operations contribute to its safety profile. The findings align with the Inherent Safety Index (ISI), and risk mitigation strategies have been proposed based on these results. But overall evaluation based on economic, environmental, technological, and social-governance criteria recommend gasification process as a sustainable solution for biomass waste valorization due to process maturity and its wide application. Policymakers can propose short-term, mid-term, and long-term action plans for waste valorization based on the findings of this research. These plans include training and awareness programs, the installation of pilot plants, and the provision of subsidies and loans, among other initiatives, to optimize the waste valorization process, specifically the gasification process. |
| Rights: | All rights reserved |
| Access: | open access |
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