Yongtu Liang

Hydraulic model optimization of a multi-product pipeline

An optimization model is established for a multi-product pipeline which has a known delivery demand and operation plan for each off-take station. The aim of this optimization model is to minimize the total pumping operation cost, considering not only factors including the energy equilibrium constraint, the maximum and minimum suction and discharge pressures constraints of pump stations, and pressure constraint at special elevation points, but also the regional differences in electricity prices along the pipeline. The dynamic programming method is applied to solve the model and to find the optimal pump configuration.

A methodology to restructure a pipeline system for an oilfield in the mid to late stages of development

Abstract One important issue in the mid to late development stages of oilfields is maintaining stable production, especially when the existing gathering pipeline system cannot fully satisfy the development of low pressures and low production rates. In these cases, it is necessary to restructure the original gathering pipeline system. In this study, an optimal design method is proposed to restructure a pipeline system in an oilfield in the mid to late development stages. Based on the demand of stable production and the existing structure of the pipeline system, a mixed-integer nonlinear programming (MINLP) model with an objective function that minimizes the total cost is developed. Hydraulic, technical and economic constraints are considered. The model is linearized based on a piecewise method and solved by the branch-and-bound algorithm. This method is applied to a real case study of a pipeline system in an oilfield.

An MILP approach for detailed scheduling of oil depots along a multi-product pipeline

Oil depots along products pipelines are important components of the pipeline transportation system and down-stream markets. The operating costs of oil depots account for a large proportion of the total system’s operating costs. Meanwhile, oil depots and pipelines form an entire system, and each operation in a single oil depot may have influence on others. It is a tough job to make a scheduling plan when considering the factors of delivering contaminated oil and batches migration. So far, studies simultaneously considering operating constraints and contaminated oil issues are rare. Aiming at making a scheduling plan with the lowest operating costs, the paper establishes a mixed-integer linear programming model, considering a sequence of operations, such as delivery, export, blending, fractionating and exchanging operations, and batch property differences of the same oil as well as influence of batch migration on contaminated volume. Moreover, the paper verifies the linear relationship between oil concentration and blending capability by mathematical deduction. Finally, the model is successfully applied to one of the product pipelines in China and proved to be practical.

Supply-based optimal scheduling of oil product pipelines

Oil product pipelines have features such as transporting multiple materials, ever-changing operating conditions, and synchronism between the oil input plan and the oil offloading plan. In this paper, an optimal model was established for a single-source multi-distribution oil product pipeline, and scheduling plans were made based on supply. In the model, time node constraints, oil offloading plan constraints, and migration of batch constraints were taken into consideration. The minimum deviation between the demanded oil volumes and the actual offloading volumes was chosen as the objective function, and a linear programming model was established on the basis of known time nodes’ sequence. The ant colony optimization algorithm and simplex method were used to solve the model. The model was applied to a real pipeline and it performed well.

A MILP model based on flowrate database for detailed scheduling of a multi-product pipeline with multiple pump stations

Abstract Multi-product pipelines usually transport several products in batches to respective delivery stations. As for a multi-product pipeline with multiple pump stations, this paper develops a continuous-time mixed-integer linear programming (MILP) model based on flowrate database to optimize its detailed scheduling. In the proposed model, various unit pump cost and flowrate constraints, which strongly depend on pump operation schemes, are introduced for the economy and safety of solved scheduling plans. Moreover, this paper considers the actual field processing constraints which vary with batch interface migration and rarely considered in previous work. And a novel method of historical flowrate database preprocessing is presented to enhance solving efficiency. Finally, through comparing with three real-world cases solved by another two available models, the proposed one performs the best in scheduling optimization as well as substantial reduction of pump cost.

A novel particle swarm optimization based on prey–predator relationship

Abstract Particle swarm optimization (PSO) is a widely used nature-inspired optimization algorithm based on population and has strong robustness and good global astringency. In the mid–late iterations in the latest PSOs, there are plenty of dense gathering “slothful particles” with low velocities, which not only contribute little to the optimization but also impact the computation speed. Inspired by the prey–predator relationship in nature, we propose a novel prey–predator PSO (PP-PSO) that employs the three strategies of catch, escape, and breeding. In PP-PSO, slothful particles can be deleted or transformed, and while the former helps to speed up convergence and computation speed, the latter improves optimization results. In addition, a proportional-integral (PI) control is introduced for population control, where the population fluctuates but within a relative stability over iteration, thus enhancing population diversity. The experimental study on 10 basic benchmark functions and 30 advanced benchmark functions from CEC 2017 with different dimensions shows that our PP-PSO has a superior performance in comparison with ten other peer algorithms. In this study, a novel relationship between species behavior and swarm intelligence algorithm is found and the mechanism of particle motion in the convergence process of PSO is further revealed.

Erratum to: Supply-based optimal scheduling of oil product pipelines

Oil product pipelines have features such as transporting multiple materials, ever-changing operating conditions, and synchronism between the oil input plan and the oil offloading plan. In this paper, an optimal model was established for a single-source multi-distribution oil product pipeline, and scheduling plans were made based on supply. In the model, time node constraints, oil offloading plan constraints, and migration of batch constraints were taken into consideration. The minimum deviation between the demanded oil volumes and the actual offloading volumes was chosen as the objective function, and a linear programming model was established on the basis of known time nodes’ sequence. The ant colony optimization algorithm and simplex method were used to solve the model. The model was applied to a real pipeline and it performed well.