Digitalization Program Blends Clean Fuel and Smart Enterprise
The optimization process presented in this paper gradually incorporates process control, digital twins, planning, scheduling, and blending for refiners.
With an increased awareness of the polluting effects of emissions on the environment, minimizing greenhouse gases and particulate matter from cleanup activities, whether they are fossil- or alternative-fuel based, is at the core of green remediation strategies.
Refiners are increasingly focusing on environmental protection by implementing clean fuel oil campaigns. The product specifications are tighter, and the refinery margins are decreased significantly with existing refinery setups. Refiners, therefore, must revamp units and expand their product portfolio. They also must assess digitalization and optimization. Digitalization can convert a refinery to a “smart enterprise” to achieve operational excellence and use of existing plant setup to archive regulation and market changes through smart discovery.Optimization of capital investments can help efficiently meet future market demand.
A nonlinear optimization problem/mixed-intiger nonlinear optimization problem (NLP/MINLP) blending application can lead to operational results with accurate property estimations and profit increases in the nonlinearization of the blending model. The MILP blending model is developed to improve blending accuracy by reducing quality giveaway and can increase profit by optimizing the blend recipe. Combined with linear processing technology for planning and scheduling as well as automated reconciliation, this creates digital planning and scheduling and enables informed decisions.
The refinery mission is to supply the highest value products at a competitive cost in a timely manner through a competent and optimized work force while maintaining safety, reliability, and environmental protection. The digitalization program presented in this paper gradually incorporates process control, digital twins, planning, scheduling, and blending.