Multiproduct Pipeline System Research Articles

Pipeline sharing: Boosting multi-product pipeline transport biofuels in the shift to low-carbon energy

Under the dual pressures of oil depletion and low carbon transition, biofuels are considered effective alternatives for fuel oil due to their renewable nature and environmental friendliness. However, oil demand remains high, which indicates that biofuels and fuel oil will coexist for a long time to come. While pipelines offer an efficient means of transporting liquid fuels, the initial investment required is substantial. Therefore, this paper proposes a comprehensive framework that focuses on sharing already existing multi-product pipelines with biofuels, thus evaluating the pipeline remaining potential to transport biofuels. The core component of this framework is the evaluation method for determining the multi-product pipeline remaining capacity. We coupled fuel oil consignment contracts into the remaining capacity evaluation method and developed a model for evaluating the multi-product pipeline transport biofuels potential, aiming to maximize biofuels pipeline transport volume. The objective of this model is to optimize the transportation volume of biofuels through pipelines. Various constraints are taken into account, such as product batch movement, prohibited sequences, operational constraints, delivery time frames, and inventory levels. Moreover, this study evaluates and discusses the benefits of biofuels transportation capacity using eight real-world multi-product pipeline systems without impacting fuel oil consignment contracts. The findings indicate that pipelines are capable of transporting 49,849 m3 of biofuels per week, increasing revenue by 1.78 million CNY. Simultaneously, the low energy consumption for pipeline transportation can lead to a weekly reduction of 34.6 tons in carbon emissions. The evaluation model anticipatively provides the ability to respond to future market developments.

Deeppipe: A hybrid model for multi-product pipeline condition recognition based on process and data coupling

It is significant for field monitoring and management to identify the operating state of the pipeline and prevent an abnormal accident from occurring. Aiming at the few unsteady and abnormal conditions data samples, different condition switching duration, and strong time-space characteristics of multi-product pipeline condition recognition, a hybrid model for condition recognition based on process and data coupling is proposed. First, collecting the relevant parameters of the real multi-product pipeline system and using Stoner Pipeline Simulator (SPS) to establish a simulation model. Based on the simulation model, performing the simulation of unsteady conditions and abnormal conditions to obtain the corresponding simulated data. Second, the reliability of the simulation model is verified by calculating the distribution similarity between the simulated data and the real pipeline data. Third, the simulated data and real data are processed by variable-length series and input into the proposed hybrid model for training and verification. The results show that the proposed hybrid model has high accuracy, precision, recall, and F1 score of 93.1%, 93.1%, 87.2%, and 84.7%, respectively, which has essential guiding significance for monitoring and ensuring the safe and stable operation of multi-product pipelines.

Scheduling of a branched multiproduct pipeline system with robust inventory management

As the main transportation mode of refined products, multiproduct pipelines play an important role in ensuring the downstream energy supply. Before the market-oriented reform of refined products, the construction and management of multiproduct pipelines were monopolized by giant state-owned enterprises, forming the self-operation mode. However, with the development of refined products market, the market-oriented operation mode has gradually formed. Under the new mode, the market demand presents a stronger uncertainty, resulting in the scheduling method under self-operation mode may not be able to cope with the supply interruption risk caused by demand uncertainty. The robustness of the obtained schedule is low. Aiming at above issue, this paper proposes a method for scheduling of branched multiproduct pipeline system under market-oriented mode. The method considers market demand uncertainty and robust inventory management. It can not only reduce additional operation cost caused by demand uncertainty, but also decrease the adjustment frequency of schedule, so that the schedule is still feasible within a certain fluctuation range. Finally, the proposed model is validated by applying it to a real-world multiproduct pipeline system.

Support of Scheduling of Multiproduct Pipeline Systems Using Simulation in Witness

Support of Scheduling of Multiproduct Pipeline Systems Using Simulation in Witness

An effective decomposition algorithm for scheduling branched multiproduct pipelines

This article develops a novel two-stage algorithm for the detailed scheduling of branched multiproduct pipeline systems with a single refinery and multiple depots. It features continuous-time models allowing multiple batches to be injected/delivered over a slot. The first-stage model reduces the complexity by neglecting the lower flowrate limits, enforced by big-M constraints. The objective is to minimize the makespan. The second-stage model works with the first-stage assignments and with an extended time grid. Coupled with a backtracking mechanism, the proposed algorithm can significantly reduce computational time and improve the pipeline transportation capacity. We use four benchmark instances from the literature to test and validate the effectiveness and superiority of the method proposed in this paper. Compared to the literature, a new optimal solution is reported in one case, while the computational time is reduced by at least one order of magnitude in the other cases.

Multi-period optimal schedule of a multi-product pipeline: A case study in Algeria

The oil supply chain plays a vital role for both economic and social sectors in Algeria. Pipeline is the main oil transportation mode from production sites to distribution centers. However, finding the optimal schedule for a multi-product pipeline is among the most difficult optimization problems. In this article, we propose an operational schedule for a long-term horizon for a multi-product pipeline system. In our model, we link a refinery to multiple storage and distribution fuel centers. The proposed approach aims to satisfy the demands of different distribution centers in a timely manner by respecting quality and cost requirements. A mixed integer linear programming (MILP) model is proposed and solved by a branch and cut algorithm. Our model is tested on a real case of an Algerian oil company. The obtained schedule satisfies variable demand while respecting quality and cost requirements.

New Method Based on Model-Free Adaptive Control Theory and Kalman Filter for Multi-Product Unsteady Flow State Estimation

Abstract In this paper, a new methodology is proposed to realize real-time unsteady flow estimation for a multi-product pipeline system. Integrating transient flow model, adaptive control theory, and adaptive filter, this method is developed to solve the contradiction between the efficiency and accuracy in traditional model-based methods. In terms of improving computational efficiency, the linear flow model based on frequency response and difference transforming is established to replace the traditional nonlinear flow model for transient flow state estimation. To reduce the deviation between actual observations and linear model estimates, we first introduce a model-free adaptive control method as linear compensation of the reduced order unsteady flow state model. To overcome the interference of observation noise, the Kalman filter method is applied to the modified state space model to obtain the one-step-ahead transient flow estimation. The proposed method is applied to the transient flow state estimation of a multi-product pipeline system and compared with the model-based method and two data-driven methods. The proposed method can reduce the deviation of transient flow estimation between the reduced order linear model and the traditional nonlinear model to less than 0.5% under unforeseen conditions and shows strong robustness to noise interference and parameter drift.

A solution framework for the long-term scheduling and inventory management of straight pipeline systems with multiple-sources

This paper addresses the long-term scheduling of pumping and delivery operations on straight multiproduct pipeline systems connecting multiple-sources to multiple-destinations. A generic solution framework is proposed, combining heuristic procedures and mixed integer linear programming (MILP) models. The pipeline scheduling problem is decomposed into two sub-problems: the allocation and sequencing of pumping operations of the initial source, and the scheduling of delivery operations and also pumping operations of the intermediate sources. An innovative execution procedure using a rolling horizon strategy is developed to solve both problems. We focus on a new discrete-time MILP model formulation, covering several operational aspects of real-world pipeline systems, such as rigorous treatment of forbidden sequences and preventive tank maintenance and pipeline maintenance periods, while considering rigorous inventory management during the scheduling horizon. The approach is validated by solving an illustrative example and two real-world based pipeline problems. Results show that the proposed framework is able to obtain solutions in reasonable run time with detailed inventory management and respecting several operational constraints of the system.

An integrated methodology for the supply reliability analysis of multi-product pipeline systems under pumps failure

As the main way for the long-distance transportation of refined products, multi-products pipelines are of vital importance to the regional energy security. The supply reliability evaluation of multi-product pipeline systems can improve the effective response to unexpected disruptions and guarantee the reliable oil supply. Based on reliability theory and pipeline scheduling method, an integrated supply reliability evaluation methodology for multi-product pipeline systems is proposed in this paper and the pumps failure, of which influence is the most complex, is focused on. In the methodology, the discrete-time Markov process is adopted to describe the stochastic failure and the Monte Carlo method is used to simulate the system states transition. With the pipeline flowrate upper limits under various pumps failure scenarios optimized in advance, the maximum supply capacity to the downstream markets in each trial is calculated by the pipeline scheduling model. Three indicators are also developed to analyze the pipeline supply reliability from the holistic and individual perspectives. At last, the methodology application is performed on a real-world multi-product pipeline system in China and the supply reliability is analyzed in detail according to the simulation results. It is proved to provide a practical method for the emergency response decision-making and loss prevention.

Scheduling of a single-source multiproduct pipeline system by a matheuristic approach: Combining simulated annealing and MILP

Pipeline systems are used for distribution of oil derivatives, where the optimization of transport activities impact considerably in the oil company’s economy. We focused on the scheduling of pumping and delivery operations on straight multiproduct pipeline systems connecting a single-source to multiple-destinations. This paper develops new preprocessing heuristics and a discrete-time mixed integer linear programming (MILP) formulation for the allocation and sequencing activity (ASM model). The new MILP model includes constraints concerning the scheduling of pumping interruptions considering stoppages in order to avoid inventory problems. A preprocessing block is also proposed to better estimate the delivery operations for the initialization batches using a simulated annealing metaheuristic to provide a more robust input data for the ASM model. The results for two case studies of a real-world pipeline system were studied. Better inventory management is observed when facing low demand instances due to its capacity to effectively propose pipeline interruptions.

Computationally Efficient MILP Model for Scheduling a Branched Multiproduct Pipeline System

This paper presents a novel mixed-integer linear programming (MILP) continuous-time formulation for the detailed scheduling of a branched pipeline system with a single refinery and multiple depots,...

Multiproduct pipeline scheduling integrating for inbound and outbound inventory management

This paper addresses the operation of a unidirectional multiproduct pipeline system integrated with inventory tanks management at the refinery and distribution center. A Mixed Integer Linear Programming (MILP) formulation is proposed to model the scheduling of a pipeline operation consisting of a single source node and a single destination node representing a refinery and a distribution center, respectively. Different operational aspects such as product availability, inventory limits, product quality, forbidden sequences, transition between different products injected into the pipeline and tanks inventory management are considered. Time synchronization among refinery, pipeline, and distribution center is guaranteed enhancing the model representation of real operations. Additionally, a solution procedure is developed to improve the model solution performance that explores the reduction of the number of variables and constraints. Different examples based on a real world case are solved showing the model's applicability, which proves to be effective in generating feasible solutions in reasonable computational times.

Planning of multi-product pipelines by economic lot scheduling models

In chemical and petroleum industry pipelines are one of the most important means of transportation. However, flexibility of pipeline transport systems is limited by many restrictions. Therefore, the planning of pipeline operations is a crucial part of logistics management in these industries. A particularly challenging problem is the pipeline scheduling which is concerned with finding the sequences, times, and sizes of batch injections in pipeline systems. This paper specifically studies the underlying core scheduling problem by assuming a simple multi-product pipeline system. It is shown that finding a sequence of batches which minimizes stock holding and setup costs in the long run is an NP-hard scheduling problem, namely a variant of the economic lot scheduling problem (ELSP) with additional constraints. Therefore, a powerful heuristic for the sequence-dependent ELSP is adapted and extended to meet the requirements of the outlined pipeline scheduling problem. The application of the heuristic is illustrated by case studies from chemical and petroleum industry.

A mathematical model for scheduling of real-world tree-structured multi-product pipeline system

In this paper, a mathematical model to petroleum derivatives transportation scheduling is developed. The problem discussed in this paper is concerned with the scheduling of one-source tree-structured pipeline connected to several output terminals. A tree-structured pipeline is composed of a mainline conveying high-volume of petroleum products over long ways and secondary lines transporting smaller volumes over shorter distances. In such a pipeline, batches of petroleum products are pumped back-to-back at the origin of the mainline, without any separation device between them. This paper introduces a continuous mathematical representation, mixed integer linear programming, for the operational planning of tree-structured pipeline systems. Previous contributions on tree-structured pipeline planning deal with the sequence deliveries at receiving terminals, i.e., at any time only a unique terminal receives material from the pipeline. On the contrary, the proposed approach permits a receiving terminal on the mainline to simultaneously receive products when one of the secondary lines is taking material from the mainline. The problem’s aim is to find the optimal sequences of product injection and dispatching operations that satisfy product demands at minimum total cost, accounting for pumping and backordered demand costs during the specified planning horizon. The approach has been validated by solving three case studies of growing complexity.

A mixed-integer linear programming for scheduling a multi-product pipeline with dual-purpose terminals

Pipelines carry different types of petroleum products from production areas to long-distance terminals. This paper introduces a novel mixed-integer linear programming based on a continuous time representation for scheduling a multi-product pipeline system connecting a unique refinery to several distribution centers where the mid-terminals are able to both inject product into the pipeline and receive product from it. This model allows the multiple removal terminals to withdraw product from the pipeline at the same time. The problem goal is to minimize the overall operational cost of the pipeline, such as interface and pumping costs. The result shows that the proposed method leads to better pipeline schedules than previous approaches in solution quality.

Scheduling a multi-product pipeline network

Daily some millions barrels of oil are moved around the world in imports and exports and domestically within countries. While ships are the main mode for intercontinental transport, pipelines are the chief form of transcontinental transport, while regional and local transports is performed by trains and trucks. Despite high installation costs, pipelines are considered highly efficient as a mode for transporting large amounts of oil and oil products over long distances, because they offer lower operation costs, higher reliability rates, lower product loss rates, less environmental impact, and less susceptibility to adverse weather conditions than other modes. This study deals with a multi-product pipeline system that transports a set of oil products (diesel, gasoline and kerosene, for example), which have to be moved from points (operating areas) where they are produced or stored (refineries, terminals) to points where they are needed (other refineries, distribution centers, terminals, ports, customers) through a pipeline or set of pipelines.The present study contributes primarily by offering an efficient tool for the problem of scheduling multi-product pipeline networks. The methodology proposed takes the approach of discretizing both pipelines and planning horizon and combines an efficient MILP model with a post-processing heuristic. When compared with previous models, we propose a more efficient one in which the set of volumetric constraints is modeled in the form of knapsack cascading constraints and constraints on products in pipeline sections, which made for significantly improved performance in the experiments that were conducted. The proposed methodology thus constitutes an advance in terms of modeling the problem, making it feasible to solve problems increasingly close to the realities confronting oil industry operators.

A novel continuous time representation for the scheduling of pipeline systems with pumping yield rate constraints

Pipeline systems play a key role in the petroleum business. These operational systems provide connection between ports and/or oil fields and refineries (upstream), as well as between these and consumer markets (downstream). The purpose of this work is to propose a novel MINLP formulation based on a continuous time representation for the scheduling of multiproduct pipeline systems that must supply multiple consumer markets. Moreover, it also considers that the pipeline operates intermittently and that the pumping costs depend on the booster stations yield rates, which in turn may generate different flow rates. The proposed continuous time representation is compared with a previously developed discrete time representation [Rejowski, R., Jr., & Pinto, J. M. (2004). Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling. Computers and Chemical Engineering, 28, 1511] in terms of solution quality and computational performance. The influence of the number of time intervals that represents the transfer operation is studied and several configurations for the booster stations are tested. Finally, the proposed formulation is applied to a larger case, in which several booster configurations with different numbers of stages are tested.

Optimal scheduling of multiproduct pipeline systems using a non-discrete MILP formulation

Multiproduct pipelines permit to transport large volumes of a wide range of refined petroleum products from major supply sources to distribution centers near market areas. Batches of refined products and grades are pumped back-to-back in the same pipeline, often without any separation device between batches. The sequence and lengths of such pumping runs should be carefully selected in order to meet market demands at the promised dates while satisfying many pipeline operational constraints. This paper deals with the scheduling of a multiproduct pipeline system receiving a number of liquid products from a single refinery source to distribute them among several depots. A novel MILP continuous mathematical formulation that neither uses time discretization nor division of the pipeline into a number of single-product packs is presented. By developing a more rigorous problem representation, the quality of the pipeline schedule is significantly improved. Moreover, a severe reduction in binary variables and CPU time with regards to previous approaches is also achieved. To illustrate the proposed approach, a pair of real-world case studies was solved. Both involve the scheduling of a single pipeline carrying four oil derivatives from an oil refinery to five distribution depots. Higher pumping costs at daily peak periods were also considered. Compared with previous work, better solutions were found at much lower computational time.

Scheduling of a multiproduct pipeline system

Pipelines provide an economic fluid transportation mode for petroleum systems, especially when large amounts of petroleum derivatives have to be pumped for long distances. The system reported in this paper is composed by an oil refinery, one multiproduct pipeline connected to several depots and to the local consumer markets that receive large amounts of oil products. Extensive distances must be covered to reach the depots and the pipeline operates intermittently due to periodic increases in energy costs. The pipeline is divided into segments that connect two consecutive depots and packs that contain one product that compose the segments. Mixed-integer linear programming optimization models that are generated from linear disjunctions and rely on discrete time are proposed for the scheduling system. In the first model it is assumed that the pipeline is divided into packs of equal size, whereas the second one relaxes such assumption. Key decisions of this model involve loading and unloading operations of tanks and of the pipeline These models satisfy all operational constraints, such as mass balances, distribution constraints, product demands, sequencing constraints and logical constraints for pipeline operation. Results generated include the inventory levels at all locations, the distribution of products among the depots and the best ordering of products in the pipeline. Two examples are solved, including a real-world system that is composed of five depots and distributes gasoline, diesel, liquefied petroleum gas and jet fuel for a 3-day time horizon.

An milp formulation for the scheduling of multiproduct pipeline systems

Pipelines provide an economic mode of fluid transportation for petroleum systems, specially when large amounts of these products have to be pumped for large distances. The system discussed in this paper is composed of a petroleum refinery, a multiproduct pipeline connected to several depots and the corresponding consumer markets that receive large amounts of gasoline, diesel, LPG and aviation fuel. An MILP optimization model that is based on a convex-hull formulation is proposed for the scheduling system. The model must satisfy all the operational constraints, such as mass balances, distribution constraints and product demands. Results generated include the inventory levels at all locations, the distribution of products between the depots and the best ordering of products in the pipeline.