Crude Oil Operations In Refinery Research Articles

Many-objective optimization for scheduling of crude oil operations based on NSGA-Ⅲ with consideration of energy efficiency

Abstract Crude oil operations in refineries are characterized as a hybrid system since it contains both discrete-event and continuous processes, and it is extremely difficult to schedule such a system. For scheduling such a system, initially the discrete tasks to be performed during the scheduling horizon is unknown such that heuristics and meta-heuristics are not directly applicable, which further complicates its scheduling problem. Moreover, there are large number of objectives to be optimized, including the minimization of energy consumption due to that crude oil operations consume large amount of energy and therefore lead to large amount of emissions. Furthermore, the energy optimization problem is characterized as highly non-linearity. Hence, the scheduling problem of crude oil operations in refineries belongs to the many-objective optimization problems and it is extremely challenging. This paper addresses this challenging scheduling problem of crude oil operations. This scheduling problem is first converted to a discrete dynamic resource allocation problem such that meta-heuristics can be applicable. Then, with the results of large number of experiments, this work innovatively proposes an NSGA-Ⅲ-based optimization algorithm to efficiently solve the problem for Pareto-optimal solutions. By the proposed method, the genes in a chromosome are generated one by one and, when generating a gene, safeness check is done according to the derived safeness conditions such that each gene is feasible. In this way, the schedule feasibility can be ensured. An industrial case study is given to test its performance and comparison is made with the existing state-of-the-art algorithms for many-objective optimization problems. The results show its good performance in terms of convergence, solution diversity, time efficiency, and its applicability to real-life refinery scheduling problems.

Scheduling of Crude Oil Operations in Refinery without Sufficient Charging Tanks Using Petri Nets

A short-term schedule for crude oil operations in a refinery should define and sequence the activities in detail. Each activity involves both discrete-event and continuous variables. The combinatorial nature of the scheduling problem makes it difficult to solve. For such a scheduling problem, charging tanks are a type of critical resources. If the number of charging tanks is not sufficient, the scheduling problem is further complicated. This work conducts a study on the scheduling problem of crude oil operations without sufficient charging tanks. In this case, to make a refinery able to operate, a charging tank has to be in simultaneous charging and feeding to a distiller for some time, called simultaneously-charging-and-feeding (SCF) mode, leading to disturbance to the oil distillation in distillers. A hybrid Petri net model is developed to describe the behavior of the system. Then, a scheduling method is proposed to find a schedule such that the SCF mode is minimally used. It is computationally efficient. An industrial case study is given to demonstrate the obtained results.

Pareto-Optimization for Scheduling of Crude Oil Operations in Refinery via Genetic Algorithm

With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.

Simultaneous scheduling of front-end crude transfer and refinery processing

Scheduling of front-end crude-oil transfer and refinery processing are two critically important and challenging tasks to petroleum refineries. However, the simultaneous scheduling of front-end crude-oil transfer and refinery operations has never been considered in previous studies due to the large scale and complexity of the resultant optimization problem. In this paper, a systematic methodology for simultaneous scheduling of front-end crude transfer and refinery processing has been developed. It provides a large-scale continuous-time based scheduling model for crude unloading, transferring, and processing (CUTP) to simulate and optimize the front-end and refinery crude-oil operations simultaneously. The CUTP model consists of a newly developed refinery processing sub model, a crude processing status transition sub-model, and a borrowed front-end crude transferring sub model. The objective is to maximize the total operational profit while satisfying various constraints such as operation and production specifications, inventory limits, and production demands. The efficacy of the proposed scheduling model has been demonstrated by an industrial-scale case study.

A genetic algorithm approach to short‐term scheduling of crude oil operations in refinery

As a type of process plant, a refinery is characterized by the interaction of discrete events and continuous processes. To schedule crude oil operations in a refinery, it is necessary to define and schedule the jobs simultaneously such that heuristics and meta‐heuristics cannot be directly applied. It is very challenging to schedule crude oil operations. To solve this problem, it is decomposed into two subproblems hierarchically. At the upper level, a refining schedule is found, while at the lower level a detailed schedule is obtained to realize the refining schedule. Given a refining schedule at the upper level, this paper studies the detailed scheduling problem at the lower level. Based on a control‐theoretic perspective, the problem is innovatively transformed to a problem of assigning charging tanks to distillers such that meta‐heuristic methods can be applied. Then, a genetic algorithm (GA) approach is developed to solve the problem. In realizing the proposed GA, based on a set of existence conditions of a feasible schedule, methods are presented to guarantee that each chromosome corresponds to a feasible schedule. An industrial case study is used to show the application of the proposed method. It shows that the method works well and is applicable to real‐life problems. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Source-based discrete and continuous-time formulations for the crude oil pooling problem

Abstract The optimization of crude oil operations in refineries is a challenging scheduling problem due to the need to model tanks of varying composition with nonconvex bilinear terms, and complicating logistic constraints. Following recent work for multiperiod pooling problems of refined petroleum products, a source-based mixed-integer nonlinear programming formulation is proposed for discrete and continuous representations of time. Logistic constraints are modeled through Generalized Disjunctive Programming while a specialized algorithm featuring relaxations from multiparametric disaggregation handles the bilinear terms. Results over a set of test problems from the literature show that the discrete-time approach finds better solutions when minimizing cost (avoids source of bilinear terms). In contrast, solution quality is slightly better for the continuous-time formulation when maximizing gross margin. The results also show that the specialized global optimization algorithm can lead to lower optimality gaps for fixed CPU, but overall, the performance of commercial solvers BARON and GloMIQO are better.

An Efficient Scheduling Method for Crude Oil Operations in Refinery With Crude Oil Type Mixing Requirements

With a combinatorial nature and hybrid characteristics, it is very challenging to schedule crude oil operations in a refinery and, up to now, there is no software tool for practical use in this field. To solve this problem, based on the two-level architecture in our previous work, for cases where crude oil mixing is required, this paper investigates the refining scheduling problem at the upper level. Based on the schedulability conditions, a three-stage approach is proposed. First, a linear programming model is developed to maximize productivity. Second, another linear programming model is built and solved to optimally obtain the mixed crude oil types and their amounts, and assign them into the distillers. Finally, this paper simply sequences and merges the oil parcels obtained at the second stage to form a desired schedule. Because the proposed approach mainly solves two linear programming problems, it is very efficient. An industrial case study is presented to illustrate the proposed approach.

Global Optimal Scheduling of Crude Oil Blending Operations with RTN Continuous-time and Multiparametric Disaggregation

This paper addresses the modeling of crude oil operations in refineries assuming that all properties blend linearly. Guidelines are given on how to generate a Resource-Task Network superstructure that implicitly handles the complex logistics, while extending the scope of a well-known continuous-time formulation to variable recipe tasks with multiple input materials. The new single time grid formulation has the advantage of avoiding computationally inefficient big-M constraints, unlike previously proposed unit-specific and priority-slot based models. Through the solution of a set of test problems from the literature, we show that the resulting mixed-integer nonlinear programs can be solved close to global optimality by the commercial solver GloMIQO for the objective of gross margin maximization but not for operating cost minimization. We also show that adopting a two-step MILP-NLP algorithm where the mixed-integer linear relaxation is derived from multiparametric disaggregation can reduce the optimality ga...

Short-term scheduling of crude oil operations in refinery with high-fusion-point oil and two transportation pipelines

In some refineries, storage tanks are located at two different sites, one for low-fusion-point crude oil and the other for high one. Two pipelines are used to transport different oil types. Due to the constraints resulting from the high-fusion-point oil transportation, it is challenging to schedule such a system. This work studies the scheduling problem from a control-theoretic perspective. It proposes to use a hybrid Petri net method to model the system. It then finds the schedulability conditions by analysing the dynamic behaviour of the net model. Next, it proposes an efficient scheduling method to minimize the cost of high-fusion-point oil transportation. Finally, it gives a complex industrial case study to show its application.

An Approximation Solution to Refinery Crude Oil Scheduling Problem with Demand Uncertainty Using Joint Constrained Programming

This paper is devoted to develop an approximation method for scheduling refinery crude oil operations by taking into consideration the demand uncertainty. In the stochastic model the demand uncertainty is modeled as random variables which follow a joint multivariate distribution with a specific correlation structure. Compared to deterministic models in existing works, the stochastic model can be more practical for optimizing crude oil operations. Using joint chance constraints, the demand uncertainty is treated by specifying proximity level on the satisfaction of product demands. However, the joint chance constraints usually hold strong nonlinearity and consequently, it is still hard to handle it directly. In this paper, an approximation method combines a relax-and-tight technique to approximately transform the joint chance constraints to a serial of parameterized linear constraints so that the complicated problem can be attacked iteratively. The basic idea behind this approach is to approximate, as much as possible, nonlinear constraints by a lot of easily handled linear constraints which will lead to a well balance between the problem complexity and tractability. Case studies are conducted to demonstrate the proposed methods. Results show that the operation cost can be reduced effectively compared with the case without considering the demand correlation.

A Novel Approach to Optimization of Refining Schedules for Crude Oil Operations in Refinery

Short-term scheduling for crude oil operations is a combinatorial problem and involves extreme detail. Thus, it is very complicated and, up to now, there is no efficient technique and software tool for it. To search for efficient techniques, a two-layer hierarchical solution is proposed for it. At the upper level, one finds a realizable refining schedule to optimize some objectives. At the lower level, a detailed schedule is obtained to realize it. A methodology has been presented to solve the lower level problem from a control perspective by the authors of this paper. In this paper, the upper level problem for finding optimal refining schedules is addressed, and a novel method is proposed based on the results obtained at the lower level. This method solves a linear programming problem to determine the maximal production rate and a transportation problem to optimally assign crude oil types and volume to the distillers. This way, the method is computationally very efficient. An industrial case study is presented to show the application of the proposed method.

Short-Term Scheduling of Refinery Crude Oil Operations

Scheduling of crude oil unloading and loading operations is an important optimization problem for minimizing the significant costs at the front end of a refinery. The scheduling problem involves fi...

Schedulability Analysis of Short-Term Scheduling for Crude Oil Operations in Refinery With Oil Residency Time and Charging-Tank-Switch-Overlap Constraints

For short-term scheduling of crude oil operations in refinery, often crude oil residency time constraint and charging-tank-switch-overlap constraint are ignored by mathematical programming models to make the problem solvable. Thus, a schedule obtained by such mathematical programming models is infeasible and cannot be deployed. To solve this problem, this work studies the short-term scheduling problem of crude oil operations in a control theory perspective. The system is modeled by a hybrid Petri net and a short-term schedule is seen as a series of control commands. With this model, schedulability analysis is carried out and schedulability conditions are presented. These conditions can be used as constraints for finding a realizable and optimal refining schedule. Moreover, based on the proposed approach, a detailed schedule can be easily obtained given a realizable refining schedule. In this way, the complexity for the short-term scheduling problem of crude oil operations in refinery is greatly reduced and effective techniques and tools for practical applications can be obtained.

Hybrid Petri Net Modeling and Schedulability Analysis of High Fusion Point Oil Transportation Under Tank Grouping Strategy for Crude Oil Operations in Refinery

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> There are varieties of constraints for a short-term scheduling problem of crude oil operations in a refinery. These constraints are difficult to model and complicate the short-term scheduling problem. Among them, oil residency time and high fusion point crude oil transportation constraints are the challenging ones. With high setup cost for high fusion point oil transportation, it is desired that the volume of high fusion point oil can be transported as much as possible by a single setup. This may result in late transportation of other types of crude oil, leading to the violation of crude oil residency time constraint. These constraints are ignored by existing methods in the literature. To solve this problem, this paper studies the problem in a control theory perspective by viewing an operation decision in the schedule as a control. With this idea, the system is modeled by a hybrid Petri net. With this model and tank grouping strategy, schedulability analysis is carried out and schedulability conditions are presented with tank charging and discharging costs being taken into consideration. These conditions are necessary for determining a refining schedule and can be used to check whether a target-refining schedule is realizable or not. If so, a feasible detailed schedule for the refining schedule can be easily obtained by creating the operation decisions one by one. </para>

Tank cycling and scheduling analysis of high fusion point oil transportation for crude oil operations in refinery

For short-term scheduling of crude oil operations, oil residency time and high fusion crude oil transportation constraints are difficult to model. With high setup cost for high fusion point oil transportation, it is desired that the volume of such oil should be transported as much as possible by a single setup. To do so and obtain a feasible schedule, charging tank cycling is an effective strategy. With a hybrid Petri net model, scheduling analysis is carried out and schedulability conditions under a charging tank cycling strategy are presented. They can be used for determining a refining schedule and checking if a target refining schedule is realizable. If so, a detailed feasible schedule can be easily obtained by creating the operation decisions one by one and put into immediate industrial use.

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...

Short-Term Schedulability Analysis of Multiple Distiller Crude Oil Operations in Refinery With Oil Residency Time Constraint

Because of the complexity of a short-term scheduling problem for crude oil operations, some constraints are ignored in modeling the system by the existing approaches, leading to an infeasible solution. To avoid this, the short-term scheduling problem can be studied in control theory perspective by viewing an operation decision in the schedule as a control. With this idea, this paper conducts the schedulability analysis for systems with two and more than two distillers, and the schedulability conditions are presented with the help of Petri net theory. It shows that the number of charging tanks and their capacity, the amount of crude oil of different types in the charging tanks, the oil transportation rate of the pipeline, and the production rate of the system affect the safeness of the system. It also presents the conditions under which the system can reach its maximal production rate. With the safeness conditions and proved results presented in this paper, if a refining schedule is realizable, a feasible detailed schedule for the refining schedule can be easily obtained by creating the operation decisions for the schedule one by one. In the schedule obtained, the starting time of each operation decision can be at any continuous time point, and the schedule is certainly feasible, which overcomes the difficulty faced by techniques that are based on mathematical programming methods.

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.

Short-Term Schedulability Analysis of Crude Oil Operations in Refinery With Oil Residency Time Constraint Using Petri Nets

A short-term schedule for oil refinery should arrange all the activities in every detail for the whole scheduling horizon, leading to a complex problem. There lacks efficient techniques and software tools for its solution applicable to industrial oil refinery. Considering that the feasibility of a schedule is essential, this paper studies the feasibility problem from a control perspective. A short-term schedule is composed of a series of operation decisions (ODs), each of which can be seen as a control. When an OD is executed, it transfers the system from one state to another. To guarantee the schedule feasibility, the system should be always be kept in safe states. The system is modeled by a Petri net model that is under control of the ODs. With this model, schedulability conditions for a system with one distiller are presented. These conditions reveal the relationship among the number of charging tanks, the oil transportation flow rate of the pipeline, and the production rate. The conditions are presented in a constructive way. Based on the conditions, when a realizable refining schedule is verified, a detailed short-term schedule is created for practical use.

Global optimization for scheduling refinery crude oil operations

In this work we present an outer-approximation algorithm to obtain the global optimum of a nonconvex mixed-integer nonlinear programming (MINLP) model that is used to represent the scheduling of crude oil movement at the front-end of a petroleum refinery. The model relies on a continuous time representation making use of transfer events. The proposed algorithm focuses on effectively solving a mixed-integer linear programming (MILP) relaxation of the nonconvex MINLP to obtain a rigorous lower bound (LB) on the global optimum. Cutting planes derived by spatially decomposing the network are added to the MILP relaxation of the original nonconvex MINLP in order to reduce the solution time for the MILP relaxation. The solution of this relaxation is used as a heuristic to obtain a feasible solution to the MINLP which serves as an upper bound (UB). The lower and upper bounds are made to converge to within a specified tolerance in the proposed outer-approximation algorithm. On applying the proposed technique to test examples, significant savings are realized in the computational effort required to obtain provably global optimal solutions.