Optimization-based decision support methodology for the synthesis of negative-emissions biochar systems

• Main Authors: Belmonte, Beatriz A., Benjamin, Michael Francis D., Tan, Raymond Girard R.
• Format: text
• Published: Animo Repository 2019
• Subjects: Biochar; Carbon sequestration; Polygeneration systems; Pyrolysis; Chemical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2225; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3224/type/native/viewcontent
• Institution: De La Salle University

Biochar-based carbon management networks (BCMNs) can be an effective climate change mitigation strategy. Such systems have the potential to sequester stable carbon in biochar through application to soil, thus achieving negative emissions through the net transfer of carbon from the atmosphere into the ground. There may also be secondary co-benefits that result from the complex interactions of the biochar with the receiving soil. In order to maximize system-wide benefits, process systems engineering (PSE) tools can be used to optimize integrated BCMNs so as to customize biochars to specific soil quality limitations. This research gap is addressed in this work. A model is developed to optimize BCMNs where the syngas coproduced with biochar is utilized as fuel for the polygeneration system. The BCMN can achieve negative carbon footprint due to sequestration of carbon in the biochar coupled with displacement of fossil energy-intensive products. Since both optimal and near-optimal are needed for more effective decision-making, a mixed integer linear programming model (MILP) with integer-cut constraints is developed for the synthesis of polygeneration systems with biochar production. Integer cuts of the MILP model can be solved to generate a ranked set of near-optimal solutions alongside the optimal solution; these alternative solutions can then be analyzed during process synthesis. Two case studies are used to demonstrate how the methodology can generate insights for better decision-making, such as revealing patterns of features that repeatedly occur in good solutions. These insights enable practical constraints arising in industrial settings to be accounted for. © 2019