Abstract
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 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. 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. In this work, a robust optimization model is proposed to design cost and environmentally effective biomass co-firing networks that decides on appropriate co-firing configurations and fuel blends. A case study is solved to demonstrate validity. 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agbor, E., Zhang, X., & Kumar, A. (2014). A review of biomass co-firing in North America. Renewable and Sustainable Energy Reviews, 40, 930–943.
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165–4176.
Ba, B. H., Prins, C., & Prodhon, C. (2016). Models for optimization and performance evaluation of biomass supply chains: An operations research perspective. Renewable Energy, 87, 977–989.
Belmonte, B. A., Benjamin, M. D., & Tan, R. R. (2018). Bi-objective optimization of biochar-based carbon management networks. Journal of Cleaner Production, 188, 911–920.
Belmonte, B. A., Tan, R. R., & Benjamin, M. D. (2017). A two-stage optimization model for the synthesis of biochar-based carbon management networks. Chemical Engineering Transactions, 61, 379–384.
Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations Research Letters, 25(1), 1–13.
Boundy, B., Diegel, S. W., Wright, L., & Davis, S. C. (2011). Biomass energy data book. U.S. Department of Energy.
Castillo-Villar, K. K., Eksioglu, S., & Taherkhorsandi, M. (2017). Integrating biomass quality variability in stochastic supply chain modeling and optimization for large-scale biofuel production. Journal of Cleaner Production, 149, 904–918.
Dundar, B., McGarvey, R. G., & Aguilar, F. X. (2016). Identifying optimal multi-state collaborations for reducing CO2 emissions by co-firing biomass in coal-burning power plants. Computers & Industrial Engineering, 101, 403–415.
Ghaderi, H., Pishvaee, M. S., & Moini, A. (2016). Biomass supply chain network design: An optimization-oriented review and analysis. Industrial Crops and Products, 94, 972–1000.
Gonela, V., Zhang, J., Osmani, A., & Onyeaghala, R. (2015). Stochastic optimization of sustainable hybrid generation bioethanol supply chains. Transportation Research Part E: Logistics and Transportation Review, 77, 1–28.
Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operations Research, 263, 108–141.
He, Y., Zhou, X., Jiang, L., Li, M., Du, Z., Zhou, G., & Xu, C. (2017). Effects of biochar application on soil greenhouse gas fluxes: A meta-analysis. GCB Bioenergy, 9, 743–755.
Madanayake, B. N., Gan, S., Eastwick, C., & Ng, H. K. (2017). Biomass as an energy source in coal co-firing and its feasibility enhancement via pre-treatment techniques. Fuel Processing Technology, 159, 287–305.
Mohd Idris, M. N., Hashim, H., & Razak, N. H. (2018). Spatial optimisation of oil palm biomass co-firing for emissions reduction in coal-fired power plant. Journal of Cleaner Production, 172, 3428–3447.
Ng, T. S., & Sy, C. L. (2014). A resilience optimization approach for work-force inventory control dynamics under uncertainty. Journal of Scheduling, 17, 427–444.
Otte, P. P., & Vik, J. (2017). Biochar systems: Developing a socio-technical system framework for biochar production in Norway. Technology in Society, 51, 34–45.
PĂ©rez-Fortes, M., LaĂnez-Aguirre, J. M., Bojarski, A. D., & Puigjaner, L. (2014). Optimization of pre-treatment selection for the use of woody waste in co-combustion plants. Chemical Engineering Research and Design, 92(8), 1539–1562.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35, 637–649.
Ramos, A., Monteiro, E., Silva, V., & Rouboa, A. (2018). Co-gasification and recent developments on waste-to-energy conversion: A review. Renewable and Sustainable Energy Reviews, 81, 380–398.
Shabani, N., & Sowlati, T. (2013). A mixed integer non-linear programming model for tactical value chain optimization of a wood biomass power plant. Applied Energy, 103, 353–361.
Tan, R. R. (2016). A multi-period source–sink mixed integer linear programming model for biochar-based carbon sequestration systems. Sustainable Production and Consumption, 8, 57–63.
U.S. EPA. (2018). EPA’s treatment of biogenic carbon dioxide (CO2) emissions from stationary sources that use forest biomass for energy production. Policy Statement.
Veijonen, K., Vainikka, P., Järvinen, T., & Alakangas, E. (2013). VTT processes, biomass co-firing: An efficient way to reduce greenhouse gas emissions. Retrieved from European Bioenergy Networks.
Woolf, D., Amonette, J. E., Street-Perrott, F., Lehmann, J., & Joseph, S. (2010). Sustainable biochar to mitigate global climate change. Nature Communications, 1, 1–9.
Zandi Atashbar, N., Labadie, N., & Prins, C. (2016). Modeling and optimization of biomass supply chains: A review and a critical look. IFAC-PapersOnLine, 49(12), 604–615.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendices
Appendix 1: Biomass Data
Appendix 2: Co-firing Scheme Parameters
Appendix 3: Power Plant Data
Appendix 4: Biochar Sink Data
Appendix 5: Other Relevant Parameters
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
San Juan, J.L.G., Sy, C.L. (2022). Multi-objective Robust Optimization for the Design of Biomass Co-firing Networks. In: Kuo, YH., Fu, Y., Chen, PC., Or, C.Kl., Huang, G.G., Wang, J. (eds) Intelligent Engineering and Management for Industry 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-94683-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-94683-8_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94682-1
Online ISBN: 978-3-030-94683-8
eBook Packages: Business and ManagementBusiness and Management (R0)