Modern refining has become an extremely competitive business due to the deteriorating quality of crude oil coupled with tighter product specifications and more stringent environmental regulations. Process industry supply chains are therefore striving to improve efficiency. Resources can be used more efficiently by ensuring local objective functions along the supply chain are not undermining overall goals. Along the oil supply chain, planning layer decisions are made to reduce logistical cost while the production layer minimizes operational cost. The crude scheduling problem (CSP) receives the shipping vessel's schedule including arrivals, amounts, and types as well as the CDU demand and a blend range determined at the production level. It then must decide in which manner to store and blend the crude before feeding the fractionating section of the refinery (CDU/VDU). The traditional approach to the CSP for a refinery is a discrete time optimization formulation where the scheduling horizon is split into time intervals of equal size and binary variables are used to indicate if an action starts or terminates during this time. Various mathematical models have been developed to solve the CSP. The objective functions of these solutions include sea waiting cost incurred for waiting sea vessels, unloading cost, inventory cost, etc. Yet, the blend of the crude affects the refining cost even when in the operational range. Since crude oil cost accounts for about 85- 90% of the total operating cost, a wide variety of crude blends are processed. Many variables can affect the performance of the refinery units. The interactive nature of the process and complex heat integration schemes due to the presence of pump around and side-strippers make it difficult to operate at the optimal conditions. The change in feed composition often results in inferior unit performance. Since the optimal conditions vary depending on the feed blend selected, optimizing the operation of the crude unit is essential to maximizing refining economics. The goal in the production planning level is maximizing revenue. The conventional objective function includes only the costs associated with the feed, products, utilities and energy. However, the rising concerns on global warming and with implementation of emissions trading programs (“cap and trade”), the environmental costs are becoming significantly higher and therefore have to be considered in the optimization criteria together with technical and economical evaluations. Therefore an improved objective function known as triple bottom line function is used in this work which takes into account the environmental effects. One of the key drawbacks of the previous solutions of the scheduling problem is the production process is not modelled. Consequently, the objective functions do not consider the optimized refining cost when deciding the crude oil schedule. Clearly, the calculated scheduling strategy may be suboptimal considering the difference in crude oil properties. In this paper, we combine the scheduling problem with the optimal operation of the facility and add to the objective function an optimized refining cost equation of the particular blend entering the refinery separation units. The blend is heuristically narrowed to a range of possible values by the production layer. A function is created taking into account the refining cost. The proposed strategy helps fine tune minimizing overall cost along the entire supply chain.
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