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• An mathematical model for crude oil pipeline design is developed. • Hydraulic and thermal energy consumption are evaluated in the model. • Two cases are studied to verify the effectiveness of the model. • The energy consumption and CO 2 emission are estimated and analysed. The oil and gas industry has paid increasing attention to energy consumption and is taking steps to reduce emissions owing to growing environmental concerns. In this study, a method for designing the optimization of crude oil pipelines is proposed to achieve both construction and energy consumption cost reductions. The method applied in this study is derived from a mathematical programming model with minimal annual construction depreciation cost and energy consumption cost as the objective functions. The model aims to determine the size of the pipeline, as well as the location and operational plan of each station. The hydraulic and thermal calculations, the relationship between viscosity and temperature, and the uncertainty of the flow rate are all included to ensure that the model satisfies all engineering demands. In terms of its application, the constructed model has been applied to two real cases to verify its effectiveness and potential to achieve energy conservation. Moreover, the energy consumption and CO 2 emission were estimated and analyzed with varying electricity and fuel prices, as well as varying flow rate. The results demonstrate that the construction plan is not affected by the price of electricity, while fuel prices and flow rates are the main factors influencing both the construction and operational plans. [ABSTRACT FROM AUTHOR]

Highlights • A novel two-stage modeling framework based on event sequence is developed. • A SIASA algorithm is presented to re-optimize the solution of first-stage model. • A data-driven method is proposed to accomplish the fast optimization of pipeline scheduling. • The experimental results of 50 cases are given to illustrate the superiority of the proposed method. Abstract The detailed scheduling of distributing products through pipelines is one of the most essential tasks in the management of multiproduct pipelines. The current methods of scheduling optimization are still limited by the dilemma between optimality and efficiency, especially when dealing with real-world scheduling issues. This paper introduces a two-stage mathematical model based on the event sequence, whose model scale greatly decreases after stripping the terms of event sequence. Then, a data-driven method is presented to learn a large number of existing scheduling data and accelerate the calculation of first-stage model. The method is decomposed into three parts: (1) take cold start to generate a good deal of basic training data; (2) train the neural network for the fast solution of first-stage model; (3) take real-world cases to improve the self-learning of neural network. A multiproduct pipeline in China is taken as the example to prove the practicability and superiority of the proposed method. The experimental results show that the proposed method could decrease the computation time from about several hours to several minutes. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A multi-objective MINLP model for LLH pipeline scheduling optimization is developed. • The multi-scenario programming is adopted to facilitate the model establishment. • AUGMECON is adopted for model solution compared to the weighting method. • Three cases are given to demonstrate the model's superiority and applicability. Abstract The mixed transportation mode based pipeline system plays a significant role in the supply chain of liquefied light hydrocarbon. Inefficient scheduling may lead to problems of frequent pump stoppage/restart, high energy consumption, and frequent pressure fluctuation. To avoid these problems, this paper implements the discrete-time representation to develop a multi-scenario and multi-objective mixed integer nonlinear programming model to minimize the pump operation cost and the number of the switching operation. In the model, several factors, such as transportation mode, inventory limits of injection stations, flow rate and pressure limits of pipeline segments, are taken into consideration. Additionally, the uncertainty of the liquefied light hydrocarbon production is described by scenarios. The improved augmented ε-constraint method is implemented to solve this model. Finally, the model is successfully applied to a multi-source single-sink liquefied light hydrocarbon pipeline system in Daqing oilfield in China. The experimental results show that the proposed model outperforms another two available models in profit, safety, and robustness aspects. [ABSTRACT FROM AUTHOR]

Highlights • A MINLP model is developed for scheduling of branched multiproduct pipeline networks. • The model rigorously accounts for nonlinear hydraulic calculation associated with flowrate. • A priority algorithm based on continuity, urgency and compatibility is presented. • Three cases are given to validate the high efficiency of the proposed method. Abstract This paper aims at the detailed scheduling of branched multiproduct pipeline networks with multiple sources and an output terminal. Nevertheless, the complexity of the pipeline structure, coupling of hydraulic and scheduling factors, impacts of physical properties of products, and simultaneous injections at different sources make it challenging to draw out the injection operations at each source. Based on a continuous-volume and discrete-time representation, this paper develops a MINLP model to find the optimal injection operations of all sources at minimum total operational cost, rigorously tracking batch migration and power consumption at each pipeline segment. A priority algorithm is presented to obtain a high-quality solution of this large-scale and nonlinear scheduling problem. Finally, experimental results on two virtual pipeline networks and a real-world pipeline network in China are given to validate the proposed approach. [ABSTRACT FROM AUTHOR]

Highlights • A mixed-time MILP model is proposed aiming at optimal scheduling of COPD. • Multiple transportation modes such as pipeline, road and railway are considered. • The detailed operation and transport scheduling of a crude oil port depot can be obtained. • A real case study in China is given to demonstrate this method’s practicality. Abstract Crude oil port depots are key hubs in oil trade and thus play a vital role in the crude oil supply chain. Based on the characteristics of the scheduling system in an oil port depot, the development of a precise schedule is essential for handling asynchronous upstream supply and downstream market demand, thereby reducing operation cost as well as the impact of market fluctuations. Most previous studies focused only on the depot system, and few have taken the export transport scheduling into consideration. In this study, the diversity of crude oil storage modes, complexity of operations, and variety of transportation modes are considered. To minimize the total operation cost, a mixed-time representation based on a mixed-integer linear programming model is established. Finally, three real cases in a crude oil port depot in China are studied, and the monthly schedules of the depot are developed. Compared with previous algorithms, this method yields more accurate results in a considerably shorter time with improved computational stability. [ABSTRACT FROM AUTHOR]

The logistics management of refined oil under a separation of production and transportation leads to high logistics costs and a mismatch between the supply and demand sides. This paper intends to develop a general framework to assess the impact of the integration of the production and transportation in terms of economic, environmental, and energy benefits. Firstly, this paper proposes a tactical-level mathematical model for optimizing the integration of production and transportation of refined oil to minimize the total cost. In the model, several factors, such as level of market demand, production capacity limits, transportation modes, and transportation capacity, are taken into consideration. Then, the energy, economy, and environment analysis method are applied to assess the impact of the integration on the field of refined oil logistics. Four scenarios are set up and a comparative analysis is carried out in detail in China. The optimal resource allocation scheme and production adjustment scheme for each scenario are obtained. The results show that after the integration, the logistics cost is reduced by 6.8 %− 11 %, the greenhouse gas emission is reduced by 7.3 %− 17.7 %, and the energy consumption per unit turnover is reduced by 4.4 %− 7.4 %. This proves that the integration of production and transportation guided by the proposed method performs positive economic, environmental, and energy benefits. Finally, policy implications are provided. • A framework is raised to assess the integration of oil production and transportation. • A logistics optimization model considering production adjustment is proposed. • The economic, environmental and energy benefits in a real case are identified. • Policy prescriptions are recommended for China. [ABSTRACT FROM AUTHOR]

• A hybrid-time MILP model is developed for the low-energy scheduling of straight pipelines. • The proposed model can be reformulated as adjusting model to improve solving efficiency. • An efficient decomposition approach that integrates heuristics and MILP models is proposed. • High-quality schedules are obtained by the proposed approach with the shorter time. This paper proposes a hybrid-time mixed integer linear programming model for low-energy scheduling of single-source multiproduct pipelines. The model divides the time horizon into slots by event points of batches reaching stations and fixed points of electricity price changing. Through this division, some practical factors are easily modelled as linear items, including time-varying electricity price and flowrate limits related to interfaces. The key technology for this model is the initialization of all time points' sequence, so a decomposition approach is developed to efficiently seek satisfactory sequence of all time points. Its superiority is validated by two real-world cases. [ABSTRACT FROM AUTHOR]

• A discrete-time MOMILP model for pump scheduling of products pipelines is built. • Various factors about pump operation are considered comprehensively in the model. • A two-stage strategy is adopted to deal with the proposed model. • Two cases from a products pipeline system in China illuminate model's practicability. As one of the major means to link refineries to local markets, pipelines are crucial for refined oil supply chains. Pump scheduling is vital to optimally operate the refined products pipelines. Existing studies mostly quantified the number of pump stop/restart in the form of cost, whereas the cost coefficient was often subjectively selected. The present study built a multi-objective mixed-integer linear programming (MOMILP) model for refined products pipelines to minimize the number of pump stop/restart and reduce the pump running cost simultaneously. In this study, the minimum continuous running time of pumps, changing electricity price (i.e., time-changing electricity price and local electricity) and the pressure limits of special points were considered meticulously, e.g. high-elevation points, low-elevation points and pump stations. In the first stage, the improved augmented ε-constraint method (AUGMECON) was adopted to deal with this model, and obtained a seris of pump operating schemes, acting as a Pareto set. Then the results of the AUGMECON were evaluated by an established evaluation model based on neural network. Finally, two cases from a refined products pipeline system in China were employed to verify the practicability and accuracy of the proposed model. The results of this study can effectively guide the pump scheduling of refined products pipelines. [ABSTRACT FROM AUTHOR]

• An MILP model for DOSC planning is developed. • Distribution plan and new pipeline route plan are optimized simultaneously. • Refined oil transportation from SDs to SDs is considered. • A real-world application of DOSC planning in China is studied. The planning of downstream oil supply chains (DOSCs) is important to guarantee that the demand of retail markets (RMs) can be satisfied. As an economic method for refined oil transport, pipelines are widely used in many DOSCs. The distribution cost can be reduced, and the efficiency can be improved by constructing new pipelines in a DOSC. In this paper, an MILP model that optimises a DOSC with new pipeline route planning is developed. Constraints for refined oil distribution and pipeline route planning are proposed. The model also considers the transport between storage depots (SDs) and makes it practical for the real-world application of a DOSC in China. Based on the current DOSC, the routes of new pipelines and supply chain planning after new pipeline construction can be obtained. This method is applied to a real DOSC case in China, and two situations are analysed. This method can also help decision makers conduct optimal pipeline route planning and create distribution plans for other DOSCs. [ABSTRACT FROM AUTHOR]

Highlights • A discrete-time MILP model is developed for multi-product pipelines in pressure control mode. • Pipeline scheduling and pump scheduling are optimized simultaneously by the model. • Hydraulic constraints associated with flowrate are linearized by piecewise linear approximation. • Two cases are given to illustrate the model’s superiority. Abstract Scheduling with hydraulic constraints is one of the crucial aspects to guarantee multi-product pipeline safety. This paper develops a discrete-time mixed-integer linear programming (MILP) model for a single-source multi-product pipeline in pressure control mode, minimizing the pump cost as well as the labor cost of pump stoppage/restart. Instead of limiting pressure through flowrate constraints, this paper adopts piecewise linear approximation to deal with pump characteristic curves and frictional loss associated to flowrate and establish corresponding pressure constraints. The pressure at all key points along the pipeline should be within the allowable range to ensure transport safety. In this way, the proposed model can attain the integrated optimization of the pipeline scheduling and pump scheduling, thereby avoiding the possible mismatch between pressure and flowrate obtained by two-step solving strategy. Finally, the proposed method is compared with another discrete-time MILP model (Chen et al., 2017) which sets the minimum pump rate variation as objective function for the operating economy. Two cases tested on a Chinese real-world pipeline are given to demonstrate that the optimal detailed schedules obtained by the proposed model perform better in both economy and the convenience of pump operations. [ABSTRACT FROM AUTHOR]