Optimization of Retrofit Decarbonization in Oil Refineries

Abstract

The chemical industry is actively pursuing energy transition and decarbonization through renewables and other decarbonization initiatives. However, navigating this transition is challenging due to uncertainties in capital investments, electricity costs, and carbon taxes. Adapting to decarbonization standards while preserving existing valuable infrastructure presents a dilemma. Early transitions may lead to inefficiencies, while delays increase the carbon footprint. This research proposes a framework to find an optimal retrofit decarbonization strategy for existing oil refineries. We start with a generic process flowsheet representing the refinery's current configuration and operations, and consider various decarbonization alternatives. Through superstructure optimization, we identify the most cost-effective retrofit strategy over the next three decades to achieve decarbonization goals. We develop a Mixed-Integer Linear Programming (MILP) model, integrating simplified process equations and logical constraints to identify the most economical retrofit decarbonization strategy. The paper presents numerical results from the MILP model. Furthermore, the trends exhibited by the outcomes across various scenarios considering distinct electricity costs and carbon tax levels are presented. These results provide valuable insights into the economic feasibility of retrofit electrification strategies for decision-makers in the chemical industry.