Application of a Mathematic Programming Model for Integrated Planning and Scheduling of Petroleum Supply Networks
2008-03-19T00:00:00Z (GMT) by
The scope of this study is concerned with the petroleum supply network operated by a typical oil company, in which the crude oil is consumed to produce ethylene, propylene, liquefied petroleum gas, butadiene, benzene, toluene, xylene, gasoline, kerosene, diesel, and other byproducts. These petrochemical products are usually manufactured with a cluster of strategically located conversion refineries. A complete petroleum supply chain consists of at least 13 different types of production units, that is, the atmospheric distillation units, the vacuum distillation units, the cokers, the fluid catalystic cracking units, the naphtha crackers, the butadiene extraction units, the hydrotreaters, the aromatics extraction units, the reforming units, the xylene fractionation units, the parex units, the xylene isomar units, and the tatory units. Traditionally, the production plan of an industrial supply chain is created first and a compatible schedule is then identified accordingly. Because the detailed scheduling constraints are often ignored in the planning model, there is no guarantee that an operable schedule can be obtained with this hierarchical approach. To address this issue, a single mixed-integer linear program (MILP) has been formulated in this study to coordinate various planning and scheduling decisions for optimizing the supply chain performance. Solving this MILP model yields the proper procurement scheme for crude oils, the schedules for producing various petrochemical products, and the corresponding logistics. The appropriate sources (suppliers) of raw materials, the economic order quantities, the best purchasing intervals, and also the transportation schedules can be identified accordingly. In particular, the optimal production schedule of olefins, aromatics, and other petrochemical products over the specified planning horizon is configured by selecting throughput, operating conditions, and technology options for each unit in the chain, by maintaining the desired inventory level for each process material, by securing enough feedstock, and by delivering appropriate amounts of products to the customers.