Designing and planning of Ethiopia's biomass-to-biofuel supply chain through integrated strategic-tactical optimization model considering economic dimension

Abstract

Despite biofuels’ significance in substituting petroleum products in the transportation sector, many countries, including Ethiopia, have not taken advantage of their biomass resources potential to develop biofuel. This is attributed to several challenges, in which the economic unattractiveness, especially the requirement of excessively high initial investment costs, is the major one. To address this challenge, a quantitative-based design and planning of the biomass-to-biofuel supply chain (BBSC), which is backed by optimization, is compulsory. Henceforth, this study intends to develop an optimization model that seeks to maximize the profit and minimize the investment cost of the BBSC. The optimization model is applied to the real case of Ethiopia's BBSC to provide a planning support tool for the policy-makers over a 20-year horizon. Although several economic optimization models developed so far, this contribution broadens the preceding research endeavors by considering the following efforts simultaneously. An integrated strategic-tactical level model is developed to prevent non-optimal and infeasible results, which might arise from separate models. Adverse to the integrated strategic-tactical models developed so far, a multi-product BBSC that produces bioethanol and biodiesel with the associated co-products and a comprehensive supply chain, which incorporates a decentralized preprocessing unit, along with the other relevant components, is considered. The investment decisions related to location, technology, and capacity of facilities happen on yearly basis, unlike many studies that consider this decision only at the beginning of the planning period. The model size boosts as it is a real and comprehensive national-level case study. Therefore, a new approach is proposed here to analyze the compromise between the objectives in a justifiable computational time with efficient optimization resolution. A sensitivity analysis is also carried out on biomass purchase price and availability, as well as biofuel demand and selling price.