An optimization approach for integrating planning and CO₂ emission reduction in the petroleum refining industry

The petroleum refining industry plays a very important role in international economics and in our daily life. The world refining capacity has increased rapidly during the past decade, and this makes operation planning, scheduling, and general optimization become important tools for the refinery industry. However, environmental regulations and risks of climate change are pressuring the refinery industry to minimize its greenhouse gas emissions. In this research, a mixed-integer nonlinear programming (MINLP) model is proposed for the production planning of refinery processes to achieve maximum operational profit while reducing CO₂ emissions to a given target through the use of different CO₂ mitigation options. The options considered in this study are flow-rate balancing (decreasing the inlet flow rate to a unit that emits more CO₂), fuel switching (changes in a certain operation to run with a different fuel that emits less CO₂ emissions, such as natural gas), and installation of a CO₂ capture process (e.g., the monoethanolamine (MEA) process). The objective of the MINLP model is to determine suitable CO₂ mitigation options for a given reduction target while meeting the demand of each final product and its quality specifications, while simultaneously maximizing profit. In this study, a global optimization algorithm is used on the different case studies considered.