(755c) Efficient Heuristic Algorithm for Short-Term Scheduling of Large-Scale Oil-Refinery Operations

Authors: 
Sahay, N., Aspen Technology
Ierapetritou, M., Rutgers, The State University of New Jersey
Shah, N., Rutgers University


Efficient Heuristic Algorithm for Short-Term Scheduling of Large-Scale Oil-Refinery Operations

Nihar Sahay, Nikisha K. Shah and Marianthi G. Ierapetritou

Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ

The oil-refinery operation is one of the most complex chemical processes, which involves many different and complicated units with various connections. Over the last 20 years, it has grown increasingly more complex due to tighter competition, stricter environment regulations, and lower profit margins. The main objective of the oil-refineries is to transform crude-oil into gasoline, diesel, jet fuel, and other hydrocarbon products that can be used as either feedstock or energy source. To remain competitive in dynamic global marketplace, the oil-refineries are increasingly concerned with improving the scheduling of their operations in order to achieve better economic performance by minimizing quality, quantity, and logistics giveaways. Oil refinery operations can be classified into three sub-operations based on the structure of the refinery configuration. These sub-operations are: (1) crude-oil unloading and mixing, (2) production unit operations, and (3) finished products blending and distribution.

Traditionally, the oil-refinery operations scheduling problem has been tackled by addressing the scheduling optimization of the three sub-operations mentioned above individually.1-7 This decentralized approach for scheduling optimization is not suitable for oil-refineries that do not rely on blend component tanks to connect production units with blending units. Most products are produced by blending components together on an in-line blender where the blending process consists of simultaneously adding several streams of intermediate components into a common blend header without utilizing the component storage tanks. In order to satisfy the finished blend product properties specifications, if any changes happen in the blend components (blend) recipe, it would have a direct impact on the production unit operations. These interdependences of the blending and production unit scheduling operations require an integrated approach to scheduling.

It is thus imperative to model the integrated production unit and finished product blending-delivery scheduling problem to address the issue of online blend scheduling problem. The resulting comprehensive model is complex, hard to build and solve, and has received sparse attention in the literature.8-10 In our previous work, we have proposed a model based on continuous-time representation for the simultaneous scheduling of production unit operations and end-product blending and delivery operations.11 The model incorporates quantity, quality, and logistics decisions related to real-life refinery operations. These involve minimum run-length requirements, fill-draw-delay, one-flow out of blender, sequence dependent switchovers, maximum heel quantity, and downgrading of the better quality product to the lower quality. This work also proposed a set of valid inequalities that improves the computational performance of the model significantly. However, even with the inclusion of the valid inequalities in the scheduling model, the computational expense required to reach an optimal solution is still considerably high for the real-life oil-refinery applications. Hence, there is a need to employ different decomposition approaches such as Lagrange decomposition, Benders decomposition, heuristics, or combination of both heuristics and mathematical decomposition techniques to enable the solution of large-scale problems in a reasonable computational time.

In this work, we decomposed the large-scale integrated problem into a production unit scheduling problem and a finished product blending and delivery problem, following the idea of spatial decomposition.12 A heuristic algorithm is proposed based on the decomposed network to obtain an optimal or a near optimal solution of the original problem. The algorithm is built upon the mathematical formulation given by Shah and Ierapetritou.11The proposed technique focuses on effectively solving the decomposed problem to obtain a feasible solution that satisfies demands while minimizing due date violations and the task changeovers at multipurpose blend units. Based on this spatial decomposition of an oil-refinery structure, the main goal of the production unit scheduling sub-problem (PSP) is to satisfy the demand requirement of final products that are sent to the market directly and the demand of the blend components required by the blend scheduling sub-problem (BSP). Similarly, the goal of the BSP is to satisfy the demand of the finished blend products by mixing the raw materials, supplied by PSP, following the blend recipe and product property specifications. We develop an iterative algorithm that solves these two independent problems (PSP and BSP) to develop a feasible solution that satisfies the final products demand requirements while minimizing demurrage and task changeovers at blend units. A case study with realistic data provided by Honeywell Process Solutions (HPS) is used to illustrate the effectiveness of the proposed heuristic algorithm.

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