Fuzzy optimization of carbon management networks based on direct and indirect biomass co-firing

• Main Authors: Aviso, Kathleen B., Sy, C. L., Tan, Raymond Girard R., Ubando, Aristotle T.
• Format: text
• Published: Animo Repository 2020
• Subjects: Biochar; Biomass energy; Carbon dioxide mitigation; Chemical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2218; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3217/type/native/viewcontent
• Institution: De La Salle University

A drastic reduction in greenhouse gas emissions from electricity generation will be needed to mitigate climate change to a safe level. Residual biomass from agriculture is an underutilized energy source that can contribute to the needed emissions cut, but its geographic dispersion presents logistical problems. Direct and indirect co-firing of biomass in existing power plants presents a flexible means of utilizing this resource. Indirect co-firing of biomass with biochar co-production can even give greater reduction in greenhouse gas emissions if the biochar is applied to soil as a form of carbon sequestration. In this paper, a fuzzy linear programming model is developed for optimizing a carbon management network based on direct and indirect biomass co-firing, coupled with biochar application to soil for the latter case. The model can match biomass sources to power plants; the power plants that use indirect co-firing are also matched to biochar application sites. The model is illustrated using a case study representative of a developing country with an agriculture-intensive economy. Results show that not all powerplants need to implement co-firing to reach a balance between reducing GHG emissions and the risk of introducing contaminants in soil. The model provides effective decision support for decarbonizing power generation, particularly in developing countries that still make use of coal-fired power plants and which have abundant biomass resources in the form of agricultural waste. © 2020 Elsevier Ltd