不确定条件下炼化企业计划与调度整合策略

A strategy for the integration of production planning and scheduling in refineries is proposed.This strategy relies on rolling horizon strategy and a two-level decomposition strategy.This strategy involves an upper level multiperiod mixed integer linear programming(MILP) model and a lower level simulation system,which is extended from our previous framework for short-term scheduling problems [Luo,C.P.,Rong,G.,"Hierarchical approach for short-term scheduling in refineries",Ind.Eng.Chem.Res.,46,3656-3668(2007)].The main purpose of this extended framework is to reduce the number of variables and the size of the optimization model and,to quickly find the optimal solution for the integrated planning/scheduling problem in refineries.Uncertainties are also considered in this article.An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.     

Rolling horizon based planning and scheduling integration with production capacity consideration

The rolling horizon method has been proposed to address the integrated production planning and scheduling optimization problem. Since the method can generally result in small-scale optimization model and fast solution, it has been used in a number of applications in realistic industrial planning and scheduling problems. In this paper, it is first pointed out that the incorporation of valid production capacity information into the planning model can improve the solution quality in the rolling horizon solution framework. A novel method is then proposed to derive the production capacity information representing the detail scheduling model based on parametric programming technique. A heuristic process network decomposition strategy is further applied to reduce the computational effort needed for larger and more complex process networks. Several case studies have been studied, which illustrate the efficiency of the proposed methodology in improving the solution quality of rolling horizon method for integrated planning and scheduling optimization.     

Modular supply chain optimization considering demand uncertainty to manage risk

Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. In this work, the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two-stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote risk-averse decisions, a downside risk measure is incorporated in the model. The results demonstrate the several advantages of modular designs in meeting product demands. A pareto-optimal curve for minimizing the objectives of expected cost and downside risk is obtained.     

Strategic network design of downstream petroleum supply chains: Single versus multi-entity participation

The petroleum supply chain (PSC) is a highly competitive system that motivates complex studies for decisions involving different problems such as the redesign aimed at optimizing existing distribution networks. This paper considers a multi-entity, multi-echelon, multi-product and multi-transportation downstream PSC network with shared installations, resource capacities, supply sources and demand requirements. A deterministic mixed integer linear program (MILP) is developed for strategic design and planning of the downstream PSC network that determines optimal depot locations, capacities, transportation modes, routes and network affectations for long term planning. The MILP maximizes the multi-echelon total profits for the petroleum companies along the supply, refining, distribution and retail stages. A multi-entity PSC network is considered, involving companies’ financial participation in refineries, transportation and storage depots. The MILP is tested with the real-case Portuguese PSC network involving production at local refineries and supply from a regional hub. Uni-entity networks as well as multi-entity networks with competitive or individualistic operation are modeled, presenting the current, grassroots and retrofit designs.     

An integrated model of supply network and production planning for multiple fuel products of multi-site refineries

An integrated model of supply network and production planning is proposed for the collaboration among refineries manufacturing multiple fuel products at different locations. The simulation and optimization based on the model indicate the following. The distribution costs can be reduced by relocating distribution centers as well as by reconfiguring their linkages to various markets. Moreover, the multiple fuel products manufactured need to be segregated during storage and transportation to be able to satisfy the demands of the various markets. The production planning, therefore, should be an integral part of the supply-network planning, and vice versa. Specifically, the proposed integrated model is for the nationwide supply of multiple fuel products manufactured by the individual refineries. The efficacy and usefulness of the integrated model is illustrated with an example involving three refineries and four varieties of fuel products.     

Optimal planning of oil and gas development projects considering long-term production and transmission

This paper proposes an integrated model for making a group of strategic decisions about oil and gas development projects simultaneously over a long-term planning horizon. These decisions involve: selection of field and pipeline development projects, scheduling of selected projects, production planning, and upstream transmission planning. The proposed model is formulated as a linear mixed-integer-programming model. It is implemented in a case study to demonstrate its usefulness and applicability in practice. Finally, a number of sensitivity analyses are carried out to analyze the impact of most influential uncertainties on the solutions and the corresponding results are discussed.     

Multisite facility network integration design and coordination: An application to the refining industry

This paper addresses the design and analysis of multisite integration and coordination strategies within a network of petroleum refineries using different crude combination alternatives. In addition, production capacity expansion requirements are also accounted for. The main feature of the paper is the development of a methodology for simultaneous analysis of process network integration alternatives in a multisite refining system through a mixed-integer linear program (MILP) with the overall objective of minimizing total annualized cost. The State Equipment Network (SEN) representation was used for modeling the network as it provides a consistent modeling strategy and proper handling of units that operate under different operating modes, which is common in the refining industry. The integrated network design specifically addresses intermediate material transfer between processing units at each site. The performance of the proposed model was tested on several industrial-scale examples to illustrate the economic potential and trade-offs involved in the optimization of the network. The use of mathematical programming models on an enterprise-wide scale to address strategic decisions considering various process integration alternatives yielded substantial benefits. These benefits not only materialize in terms of economic considerations, but also in terms of process flexibility and improvements in the understanding of the process interactions and systems limitations. Although the methodology was applied on a network of refineries, it can be readily extended to cover any network of continuous chemical processes.     

Joint capacity planning and distribution network optimization of coal supply chains under uncertainty

A two‐stage stochastic integer programming model is developed to address the joint capacity planning and distribution network optimization of multiechelon coal supply chains (CSCs) under uncertainty. The proposed model not only introduces the uses of compound real options in sequential capacity planning, but also considers uncertainty induced by both risks and ambiguities. Both strategic decisions (i.e., facility locations and initial investment, service assignment across the entire CSC, and option holding status) and scenario‐based operational decisions (i.e., facility operations and capacity expansions, outsourcing policy, and transportation and inventory strategies) can be simultaneously determined using the model. By exploiting the nested decomposable structure of the model, we develop a new distributed parallel optimization algorithm based on nonconvex generalized Bender decomposition and Lagrangean relaxation to mitigate the computation resource limitation. One of the main CSCs in China is studied to demonstrate the applicability of the proposed model and the performance of the algorithm. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1246–1261, 2018     

A planning model for multiple blending schemes☆

The key of production planning of refineries is to determine the production planning of units and blending schemes of blends in each period of the plan horizon, since they affect the effective utilization of components of refineries and hence profits. The optimization is difficult, because of many complicated product production–consumption relation-ships in production processes, which are closely related to the running modes of the units. Additional y, the blending products, such as gasoline and diesel, may use multiple blending schemes for their production that increase the complexity of the problem. This paper models the production planning problem as a mixed integer nonlinear programming. Computational experiments for a refinery show the effectiveness of the model. The optimal results give the effective utilization of the self-produced components and increase of the profit.     

A game theoretic framework for petroleum refinery strategic production planning

A game theoretic framework for strategic refinery production planning is presented in which strategic planning problems are formulated as non‐cooperative potential games whose solutions represent Nash equilibria. The potential game model takes the form of a nonconvex nonlinear program (NLP) and we examine an additional scenario extending this to a nonconvex mixed integer nonlinear program (MINLP). Tactical planning decisions are linked to strategic decision processes through a potential game structure derived from a Cournot oligopoly‐type game in which multiple crude oil refineries supply several markets. Two scenarios are presented which illustrate the utility of the game theoretic framework in the analysis of production planning problems in competitive scenarios. Solutions to these problems are interpreted as mutual best responses yielding maximum profit in the competitive planning game. The resulting production planning decisions are rational in a game theoretic sense and are robust to deviations in competitor strategies. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2751–2763, 2017     

Integration strategies of hydrogen network in a refinery based on operational optimization of hydrotreating units

Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries. It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries. An integration strategy of hydrogen network and an operational optimization model of hydrotreating (HDT) units are proposed based on the characteristics of reaction kinetics of HDT units. By solving the proposed model, the operating conditions of HDT units are optimized, and the parameters of hydrogen sinks are determined by coupling hydrodesulfurization (HDS), hydrodenitrification (HDN) and aromatic hydrogenation (HDA) kinetics. An example case of a refinery with annual processing capacity of eight million tons is adopted to demonstrate the feasibility of the proposed optimization strategies and the model. Results show that HDS, HDN and HDA reactions are the major source of hydrogen consumption in the refinery. The total hydrogen consumption can be reduced by 18.9% by applying conventional hydrogen network optimization model. When the hydrogen network is optimized after the operational optimization of HDT units is performed, the hydrogen consumption is reduced by 28.2%. When the benefit of the fuel gas recovery is further considered, the total annual cost of hydrogen network can be reduced by 3.21×10₇ CNY·a_-1, decreased by 11.9%. Therefore, the operational optimization of the HDT units in refineries should be imposed to determine the parameters of hydrogen sinks base on the characteristics of reaction kinetics of the hydrogenation processes before the optimization of the hydrogen network is performed through the source-sink matching methods.     

考虑需求不确定性的化工生产计划与调度集成

生产计划与调度是化工供应链优化中两个重要的决策问题。为了提高生产决策的效率,不仅要对计划与调度进行集成,而且要考虑不确定性的影响。对于多周期生产计划与调度问题,首先在每个生产周期内,分别建立计划与调度的确定性模型,通过产量关联对二者进行集成。然后考虑需求不确定性,使用有限数量的场景表达决策变量,建立二阶段随机规划模型。最后运用滚动时域求解策略,使计划与调度结果在迭代过程中达到一致。实例结果表明,在考虑需求不确定性时,与传统方法相比,随机规划方法可以降低总费用,结合计划与调度的分层集成策略,实现了生产操作性和经济性的综合优化。     

A robust possibilistic programming approach for pharmaceutical supply chain network design

In this paper, a bi-objective mixed integer linear programming (BOMILP) model is developed for a pharmaceutical supply chain network design (PSCND) problem. The model helps to make several decisions about the strategic issues such as opening of pharmaceutical manufacturing centers and main/local distribution centers along with optimal material flows over a mid-term planning horizon as the tactical decisions. It aims to concurrently minimize the total costs and unfulfilled demands as the first and second objective functions. Since the critical parameters are tainted with great degree of epistemic uncertainty, a robust possibilistic programming approach is used to handle uncertain parameters. In order to verify and analyze the proposed model, it is tested on a real case study and managerial insights are provided.     

Multiscale strategic planning model for the design of integrated ethanol and gasoline supply chain

The design and planning of an integrated ethanol and gasoline supply chain is addressed, and is composed of harvesting and production sites for ethanol, petroleum refineries, distribution centers where blending takes place, and the retail gas stations where blends of gasoline and ethanol are sold. We postulate a superstructure that combines all the components of the supply chain and different means of transportation, and model this multiscale design problem as a multiperiod MILP model. In order to identify regions where investments are needed and the optimal configuration of the network, a strategic planning model is considered in which gasoline stations are aggregated in different regions. A detailed formulation is considered where regions are disaggregated into gas stations to determine the retrofit projects for the selection of blending pumps over their expected life. Also, the application of these MILP models with two large‐scale problems are illustrated. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4655–4672, 2013     

A data‐driven rolling‐horizon online scheduling model for diesel production of a real‐world refinery

A rolling‐horizon optimal control strategy is developed to solve the online scheduling problem for a real‐world refinery diesel production based on a data‐driven model. A mixed‐integer nonlinear programming (MINLP) scheduling model considering the implementation of nonlinear blending quality relations and quantity conservation principles is developed. The data variations which drive the MINLP model come from different sources of certain and uncertain events. The scheduling time horizon is divided into equivalent discrete time intervals, which describe regular production and continuous time intervals which represent the beginning and ending time of expected and unexpected events that are not restricted to the boundaries of discrete time intervals. This rolling‐horizon optimal control strategy ensures the dimension of the diesel online scheduling model can be accepted in industry use. LINGO is selected to be the solution software. Finally, the daily diesel scheduling scheme of one entire month for a real‐world refinery is effectively solved. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1160–1174, 2013     

Investment portfolios under uncertainty for utilizing natural gas resources

Numerous reasons including lower carbon and sulfur emissions have led to the rapid growth of natural gas (NG) demand. However, more than one-third of world NG reserves are stranded, i.e., either remote (e.g., offshore) or in regions with saturated markets. This reality makes the investment decisions complex and uncertain for NG field developers. In this study, we consider the case of a company that wishes to develop a stranded natural gas reserve for some potential nearby markets under uncertain prices of crude oil and feed gas, and demands of liquefied NG (LNG), compressed NG (CNG), and gas-to-liquid (GTL) products. We present a 2-stage stochastic mixed-integer linear program (MILP) that yields maximum-ENPV (expected NPV) decisions on production capacities, market allocations, and delivery vessels. The small model size allows us to consider many stochastic scenarios in our scenario-based approach. We illustrate our approach using several examples.     

炼油厂氢气网络建模与多目标优化(英文)

The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming (MINLP). A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.     

天然气管网参数优化概述

在天然气管网的建设过程中,由于要兼顾技术性与经济性两个重要因素,因此需要在众多可行方案中做出最优选择。重点介绍了天然气管网参数优化的原则,以投资运行费用最小为目标建立目标函数,并在满足管道强度、稳定性、气体流动性以及节点流量的约束条件下进行运算。介绍了常用的复合型算法、罚函数算法以及遗传算法,将多种算法相结合可以简化对优化数学模型的运算。     

Multi‐period planning,design, and strategic models for long‐term,quality‐sensitive shale gas development

In this work we address the long‐term, quality‐sensitive shale gas development problem. This problem involves planning, design, and strategic decisions such as where, when, and how many shale gas wells to drill, where to lay out gathering pipelines, as well as which delivery agreements to arrange. Our objective is to use computational models to identify the most profitable shale gas development strategies. For this purpose we propose a large‐scale, nonconvex, mixed‐integer nonlinear programming model. We rely on generalized disjunctive programming to systematically derive the building blocks of this model. Based on a tailor‐designed solution strategy we identify near‐global solutions to the resulting large‐scale problems. Finally, we apply the proposed modeling framework to two case studies based on real data to quantify the value of optimization models for shale gas development. Our results suggest that the proposed models can increase upstream operators’ profitability by several million U.S. dollars. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2296–2323, 2016