Crude Oil Scheduling Research Articles

A framework for crude oil scheduling in an integrated terminal-refinery system under supply uncertainty

Operational decisions for crude oil scheduling activities are determined on a daily basis and have a strong impact on the overall supply chain cost. The challenge is to develop a feasible schedule at a low cost that has a high level of confidence. This paper presents a framework to support decision making in terminal-refinery systems under supply uncertainty. The proposed framework comprises a stochastic optimization model based on mixed-integer linear programming for scheduling a crude oil pipeline connecting a marine terminal to an oil refinery and a method for representing oil supply uncertainty. The scenario generation method aims at generating a minimal number of scenarios while preserving as much as possible of the uncertainty characteristics. The proposed framework was evaluated considering real-world data. The numerical results suggest the efficiency of the framework in providing resilient solutions in terms of feasibility in the face of the inherent uncertainty.

English

Oil refining is one of the most complex chemical industries; the objective of refinery operations is to generate as much profit as possible by converting crude oils into valuable products. The current trend toward processing petroleum needs sufficient compositional information to understand the chemistry of reactions that are involved. The objective of the present paper is to review the simulation technologies and characterization techniques which have been used in refining industry. It is found that simulation can be used in crude oil scheduling, predicting wax precipitation, asphaltene deposition, and for fractionation of crude oil based on equilibrium stage relations. The review on characterization methods clearly illustrates that the spectroscopic techniques (IR/FTIR, NMR, NIR); chromatographic techniques; true boiling point; solvent precipitation; refractive index; differential scanning calorimeter and thermogravimetry; and near- infrared region are mostly used in characterizing crude oil. Key words: Simulation, oil refining, crude oil characterization, true boiling point

A Priority-Slot Based Continuous-Time Formulation for Crude-Oil Scheduling Problems with Oil Residency Time Constraint

The optimal scheduling of crude-oil operation in refineries has been studied by various groups during the past decade leading to different mixed integer linear programming or mixed nonlinear programming formulations. This paper presents a new formulation with oil residency time constraint based on single-operation sequencing (SOS). It is different from previous formulations as it considers oil residency time constraint and pipeline transfer and does not require to postulate the number of priority-slots in which operations take place. This model is also based on the representation of a crude-oil scheduling by a single sequence of transfer operations. A simple MILP procedure has been used to solve this model leading to an satisfactory optimal result.

Hierarchical Decomposition Approach for Crude Oil Scheduling: A SINOPEC Case

This work addresses the large-scale crude oil scheduling problem of China’s SINOPEC Maoming refinery that imports various types of crude oil from two terminals via bidirectional long-distance pipelines. We use a hierarchical decomposition approach to construct a two-stage model of the refinery operations. In the upper-level model, storage and charging tanks are aggregated to determine the inflows and outflows between the two terminals and the refinery plant. The lower-level submodels solve the detailed loading and unloading operations at storage and charging tanks inside the terminals and the refinery plant. To further improve the computational efficiency, we develop a rule-based tank-selection strategy to obtain a feasible schedule. Although state-of-the-art commercial solvers cannot obtain feasible solutions of the relaxed monolithic mixed-integer linear programming model within a reasonable time, our decomposition heuristic can generate schedules that are more flexible than manually generated schedules. It also provides the refinery with annual estimated cost savings of $30 million.

Refinery scheduling of crude oil unloading with tank inventory management

The aim of this study is to develop a methodology for short-term crude oil unloading, tank inventory management, and crude distillation unit (CDU) charging schedule using mixed integer linear programming (MILP) optimization model as an extension to a previous work reported by Lee et al. (1996). The authors attempt to improve the previous model by adding an interval–interval variation constraint to avoid CDU charging rate fluctuation, a shutdown penalty within the scheduling cycle and a set up penalty for tank-tank transfer and introducing demand violation permit for a more flexible model against obtaining infeasible solution. Three different cases from the original paper were used to test the validity of the improved model. Comparison between Cases 1 and 2 shows the advantage of using smaller time interval as the operating cost of Case 2 is lower. Two scenarios were created from Case 3 to show the benefits of the improved model in deciding the best schedule to use. The improved model was implemented using the CPLEX solver in GAMS.

Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems

In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.

Speed-up Benders decomposition using maximum density cut (MDC) generation

The classical implementation of Benders decomposition in some cases results in low density Benders cuts. Covering Cut Bundle (CCB) generation addresses this issue with a novel way generating a bundle of cuts which could cover more decision variables of the Benders master problem than the classical Benders cut. Our motivation to improve further CCB generation led to a new cut generation strategy. This strategy is referred to as the Maximum Density Cut (MDC) generation strategy. MDC is based on the observation that in some cases CCB generation is computational expensive to cover all decision variables of the master problem than to cover part of them. Thus MDC strategy addresses this issue by generating the cut that involves the rest of the decision variables of the master problem which are not covered in the Benders cut and/or in the CCB. MDC strategy can be applied as a complimentary step to the CCB generation as well as a standalone strategy. In this work the approach is applied to two case studies: the scheduling of crude oil and the scheduling of multi-product, multi-purpose batch plants. In both cases, MDC strategy significant decreases the number of iterations of the Benders decomposition algorithm, leading to improved CPU solution times.

Large-Scale Refinery Crude Oil Scheduling by Integrating Graph Representation and Genetic Algorithm

Scheduling is widely studied in process systems engineering and is typically solved using mathematical programming. Although popular for many other optimization problems, evolutionary algorithms have not found wide applicability in such combinatorial optimization problems with large numbers of variables and constraints. Here we demonstrate that scheduling problems that involve a process network of units and streams have a graph structure which can be exploited to offer a sparse problem representation that enables efficient stochastic optimization. In the proposed structure adapted genetic algorithm, SAGA, only the subgraph of the process network that is active in any period is explicitly represented in the chromosome. This leads to a significant reduction in the representation, but additionally, most constraints can be enforced without the need for a penalty function. The resulting benefits in terms of improved search quality and computational performance are established by studying 24 different crude oil operations scheduling problems from the literature.

Crude Oil Tanker Routing and Scheduling

This paper illustrates how operations research can be applied to large-scale maritime crude oil transportation. The more the industry strives to improve the efficiency of their supply chains the more complex and involved planning tasks become. Crude oil tanker routing and scheduling is a good example for a highly complex planning problem in the petroleum and shipping industry. It is also a prevalent problem for which research can provide solutions that exploit the existing potential for improvement. We describe the maritime transportation part of the crude oil supply chain which we denote as the crude oil tanker routing and scheduling problem (COTRASP). Throughout this article, mathematical notation is used as a method to illustrate the various aspects of the problem. Cited literature shows the efforts made to guarantee efficient transportation and realize cost savings in related problems. Finally, solution approaches are discussed and suggestions for further research are made.

A new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling

The aim of this paper is to introduce a methodology to solve a large-scale mixed-integer nonlinear program (MINLP) integrating the two main optimization problems appearing in the oil refining industry: refinery planning and crude-oil operations scheduling. The proposed approach consists of using Lagrangian decomposition to efficiently integrate both problems. The main advantage of this technique is to solve each problem separately. A new hybrid dual problem is introduced to update the Lagrange multipliers. It uses the classical concepts of cutting planes, subgradient, and boxstep. The proposed approach is compared to a basic sequential approach and to standard MINLP solvers. The results obtained on a case study and a larger refinery problem show that the new Lagrangian decomposition algorithm is more robust than the other approaches and produces better solutions in reasonable times.

The application of event-tree based approach in long-term crude oil scheduling

This paper addresses the problem of optimal operation in long-term crude oil scheduling, which involves unloading crude oil from vessels, transferring it to charging tanks and feeding it to the distillation units. The application of a new approach for modeling and optimization of long-term crude oil scheduling is presented and the event-tree based modeling method that is very different from mathematical programming is employed. This approach is developed on the basis of natural language modeling and continuous time representation. Event triggered rules, decomposition strategy, depth-first search algorithm and pruning strategy are adopted to improve the efficiency of searching the optimum solution. This approach is successfully applied to an industrial-size problem over a horizon of 4 weeks, involving 7 vessels, 6 storage tanks, 6 charging tanks, 2 crude oil distillation units, and 6 crude oil types. The CPU (AMD 3000+, 2.0GHz) solving time is less than 70 seconds.

Accelerating Benders method using covering cut bundle generation

AbstractOver the years, various techniques have been proposed to speed up the classical Benders decomposition algorithm. The work presented in the literature has focused mainly on either reducing the number of iterations of the algorithm or on restricting the solution space of the decomposed problems. In this article, a new strategy for Benders algorithm is proposed and applied to two case studies in order to evaluate its efficiency. This strategy, referred to as covering cut bundle (CCB) generation, is shown to implement in a novel way the multiple constraints generation idea. The CCB generation is applied to mixed integer problems arising from two types of applications: the scheduling of crude oil and the scheduling problem for multi‐product, multi‐purpose batch plants. In both cases, CCB significantly decreases the number of iterations of the Benders method, leading to improved resolution times.

Robust Optimization Model for Crude Oil Scheduling under Uncertainty

In this article, a two-stage robust model is proposed to solve the crude oil scheduling problem under uncertain conditions. The first stage of the model is developed using chance-constrained programming and fuzzy programming that can be transformed into the deterministic counterpart problem, whereas the second-stage is scenario-based. Through the combination of the approaches, the two-stage model can deal with uncertain parameters with both continuous and discrete probability distributions within a finite number of scenarios. The model was tested on several small examples and an industrial-size case. Uncertainties were introduced in ship arrival times and fluctuating product demands. The computational results demonstrate the effectiveness and robustness of the proposed approach. The tradeoff between solution robustness and model robustness was also analyzed.

A Novel Priority-Slot Based Continuous-Time Formulation for Crude-Oil Scheduling Problems

The optimal scheduling of crude-oil operations in refineries has been studied by various groups during the past decade leading to different mixed integer linear programming (MILP) or mixed integer nonlinear programming (MINLP) formulations. This paper presents a new continuous-time formulation, called single-operation sequencing (SOS) model, which can be used to solve a crude-oil operations problem introduced in previous publications. It is different from previous formulations as it requires to postulate the number of priority-slots in which operations take place, instead of specifying the number of time intervals or event points to be used in the schedule. This MINLP model is also based on the representation of a crude-oil schedule by a single sequence of transfer operations. It allows breaking symmetries involved in the problem, thus enormously reducing the computational times (all instances can be solved within 2 min). A simple two step MILP-NLP procedure has been used to solve the nonconvex MINLP mode...

Flexible scheduling model of crude oil operations under crude supply disturbance

Today’s changeable market and resultant disturbance of crude oil supply require agile and flexible scheduling of crude oil operation. The objective of flexible scheduling is to keep stable crude oil operation and satisfy production demands under the circumstances of supply disturbance. In this paper, a new mixed integer non-linear programming (MINLP) formulation is set up for crude oil scheduling firstly, and then some heuristic rules worked out by some experts are proposed to linearize bilinear terms and prefix some binary variables in the MINLP model. These rules not only reduce the complexity of the MINLP model, but also can be used to solve the scheduling problems in various conditions. In case study, the new model with heuristic rules and the best models reported in the literature are compared and evaluated in three benchmark examples in the normal situation, and then three abnormal situations of supply delay are considered based on the new approach. The results of case study show that the new flexible approach can handle crude oil scheduling problems efficiently in both normal and abnormal conditions.

Continuous Nonlinear Programming Techniques to Solve Scheduling Problems

One of the most known applications of Discrete Optimization is on scheduling. In contrast, one of the most known applications of Continuous Nonlinear Optimization is on the control of dynamic systems. In this paper, we combine both views, solving scheduling problems as dynamic systems, modeled as discrete-time nonlinear optimal control problems with state and control continuous variables subjected to upper and lower bounds. Complementarity constraints are used to represent scheduling decisions. One example we discuss in detail is the crude oil scheduling in ports, with numerical results presented.

Optimizing Crude Oil Operations under Uncertainty

Abstract This paper presents a novel approach for refinery crude oil operations under uncertainty. Due to the flexibility of the crude oil scheduling, decisions made by deterministic optimizations are often conservative or lack of robustness or even infeasible, so in this paper future uncertainties are considered to improve feasibility and robustness of the schedule. To handle fluctuating product demand and uncertain ship arrival time, deterministic formulation is replaced by chance constrained programming. Through a series of examples, it proves that by using probabilistic programming, the solution of the problem provides a more robust scheduling under a comprehensive confidence level. The relationship between the probability and reliability of a planned operation is also discussed.

New approach for scheduling crude oil operations

Scheduling of crude oil operations is crucial to petroleum refining, which includes determining the times and sequences of crude oil unloading, blending, and CDU feeding. In the last decades, many approaches have been proposed for solving this problem, but they either suffered from composition discrepancy [Lee et al. 1996. Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Industrial and Engineering Chemistry Research 35, 1630–1641; Jia et al., 2003. Refinery short-term scheduling using continuous time formulation: crude-oil operations. Industrial and Engineering Chemistry Research 42, 3085–3097; Jia and Ierapetritou, 2004. Efficient short-term scheduling of refinery operations based on a continuous time formulation. Computer and Chemical Engineering 28, 1001–1019] or led to infeasible solutions for some cases [Reddy et al., 2004a. Novel solution approach for optimizing crude oil operations. A.I.Ch.E. Journal 50(6), 1177–1197; 2004b. A new continuous-time formulation for scheduling crude oil operations. Chemical Engineering Science 59, 1325–1341]. In this paper, coastal and marine-access refineries with simplified workflow are considered. Unlike existing approaches, the new approach can avoid composition discrepancy without using iterative algorithm and find better solution effectively. In this approach, a new mixed integer non-linear programming (MINLP) formulation is set up for crude oil scheduling firstly, and then some heuristic rules collected from expert experience are proposed to linearize bilinear terms and prefix some binary variables in the MINLP model. Thus, crude oil scheduling can be expressed as a complete mixed integer linear programming (MILP) model with fewer binary variables. To illustrate the advantage of the new approach, four typical examples are solved with three models. The new model is compared with the most effective models (RKS(a) and RKS(b) models) presented by Reddy et al. [2004a. Novel solution approach for optimizing crude oil operations. A.I.Ch.E. Journal 50(6), 1177–1197; 2004b. A new continuous-time formulation for scheduling crude oil operations. Chemical Engineering Science 59, 1325–1341], which proves that the new approach is valid and feasible in most small-size and medium-size problems.

A new continuous-time formulation for scheduling crude oil operations

In today's competitive business climate characterized by uncertain oil markets, responding effectively and speedily to market forces, while maintaining reliable operations, is crucial to a refinery's bottom line. Optimal crude oil scheduling enables cost reduction by using cheaper crudes intelligently, minimizing crude changeovers, and avoiding ship demurrage. So far, only discrete-time formulations have stood up to the challenge of this important, nonlinear problem. A continuous-time formulation would portend numerous advantages, however, existing work in this area has just begun to scratch the surface. In this paper, we present the first complete continuous-time mixed integer linear programming (MILP) formulation for the short-term scheduling of operations in a refinery that receives crude from very large crude carriers via a high-volume single buoy mooring pipeline. This novel formulation accounts for real-world operational practices. We use an iterative algorithm to eliminate the crude composition discrepancy that has proven to be the Achilles heel for existing formulations. While it does not guarantee global optimality, the algorithm needs only MILP solutions and obtains excellent maximum-profit schedules for industrial problems with up to 7 days of scheduling horizon. We also report the first comparison of discrete- vs. continuous-time formulations for this complex problem.

Mixed-Integer Programming Approach for Short-Term Crude Oil Scheduling

This work addresses the problem of crude oil inventory management of a real-world refinery that receives several types of oil that are delivered through a pipeline. The problem involves transfers from the pipeline to the crude tanks, settling time constraints for separating brine from the oil, interface separation between different types of oil, and charges to the crude distillation units. The short-term scheduling problem is represented as mixed-integer programming models that rely on a continuous-time formulation. The main objective is to maintain a target crude oil load while minimizing the crude tank farm operating cost subject to crude oil fluctuations in terms of quantity and quality. Property calculations yield a set of nonlinear constraints. Two formulations are developed: the first one relies on a mixed-integer nonlinear programming (MINLP) model, and the second adopts a discretization procedure for the inventory levels of the tank farm, thus generating a mixed-integer linear programming (MILP) problem. An application is developed for the scheduling of crude oil in the REVAP refinery in São José dos Campos (Brazil). It is shown that the MINLP model is able to generate a more efficient schedule for a horizon of 4 days.