Robust optimization of biomass waste co-firing networks under quality uncertainty

• Main Author: San Juan, Jayne Lois G.
• Format: text
• Language: English
• Published: Animo Repository 2018
• Subjects: Biomass energy; Renewable energy sources; Robust control; Environmental Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etd_masteral/7018
• Institution: De La Salle University

Because of increasing energy consumption and climate change as an effect of greenhouse gas emissions, the interest in more sustainable and renewable sources of energy, such as biomass, has grown. Biomass co-firing in coal power plants is an immediate and practical approach to reduce coal usage and pollutant emissions because only minor modifications are required. With direct co-firing, biomass can be used directly as secondary fuel in power plants to partially displace coal. Although it requires minimal investments, it can lead to equipment corrosion from unconventional fuel properties of the biomass-coal blend. With indirect co-firing, the risk of damage is minimized by separately processing biomass. The solid biochar by-product from the thermochemical processing of biomass can be used as soil fertilizer to achieve further reductions in GHG emissions through carbon sequestration. However, as this calls for a separate biomass energy conversion plant, its investment cost is higher. Biochar is produced in power plants retrofitted for the indirect co-firing scheme. The feedstock used and the processes undergone by the biomass impacts the presence of impurities in the biochar, which must be strategically matched and allocated in biochar sinks to avoid potential contaminant risks and maximize potential for carbon sequestration. Despite being a natural extension of biomass co-firing systems, no study thus far has considered biochar-based carbon management networks integrated with the latter. Moreover, this system faces uncertainties from the inherent variability in biomass quality. This must be accounted for because mixing fuels results in the blending of their properties. Not considering these can negatively impact economic and environmental performance. Particularly, the high moisture and ash content of biomass can negatively impact the low heating value, which dictates the amount of energy that may be produced from the combustion of biomass. The alkalinity of biomass ash content leads to deposit formation in the conversion equipment that can decrease the efficiency of the equipment.A multi-objective mixed integer non-linear robust optimization model for a biomass co- firing network integrating biomass property uncertainty with investment, transportation and production planning is formulated and validated. The robust optimization model produces solutions that are robust against uncertainty in biomass quality, while satisfying cost and emissions target parameters. This approach allows the decision maker to select among non-dominated solutions based on how much risk or uncertainty they are willing to tolerate.Computational experiments reveal that biomass and coal blend ratios should be managed carefully to reach acceptable fuel properties. When improperly managed, it can negatively impact conversion yield and equipment life, which leads to increased costs and emissions because more fuel, capacity expansions, and repairs would have to be performed to satisfy demand. Furthermore, less efficiency loss despite unsuitable feedstock properties encourage the model to use more biomass to replace coal because it will not negatively impact costs and would decrease pollutant emissions. Analysis also shows that pre-treatment facilities are prioritized depending on the effectiveness in improving properties that the biomass input violate the most based on power plant system requirements. Biomass seasonality as it impacts availability and quality are accounted for in purchase and storage planning, where purchases are done during periods when availability and quality is better, when low availability and quality are foreseen in succeeding periods, and the biomass are stored for future use. On the other hand, when quality and quantity of biomass does not experience significant seasonal changes, storage is not performed as much because of additional costs incurred, and resulting increase in emissions from pretreatment processing and transportation. However, when biomass supply is critically limited, the model becomes unbiased of its quality and prioritizes the reduction in environmental emissions it causes by using all available supply up without pretreatment. Unmet demand increases even though coal is available for combustion because the model does not want to increase environmental emissions. The system also proved to be sensitive to the soil contaminant tolerance factor for biochar application to soil as fertilizer. When tolerance is lower, costs and emissions are higher because direct co-firing is implemented more than indirect co-firing, which exposes the conversion equipment to more damage; thus, expansion, maintenance and fuel costs are significantly increased. To minimize damage to the equipment, the system also exerts more effort to improve the quality of the biomass through pretreatment. The costs and emissions decrease as the tolerance factor increases, but costs increase again slightly when the tolerance factor becomes equal to unity because of increased biochar application and other relevant costs.Results of Monte Carlo simulation show that the robust optimal network configuration is relatively immune to uncertainty realizations as compared with the optimum identified with deterministic models. In addition, the results of the robust approach show that the decision-maker has to be conservative to achieve solutions better than the deterministic solution because of the system’s sensitivity to the biomass quality.