基于智能优化算法的原油移动路径规划研究

本文研究了沿海型炼厂原油移动路径规划问题,根据实际化工罐区建立了图结构数学模型,基于多目标优化算法对模型求解,引入Deb准则处理复杂约束条件,得到了较为理想的原油移动路径规划方案。结果表明,在给出多个需同时移动的子任务的情况下,优化得到的路径规划方案均为可行方案,并能够根据实际生产需求选择相应功率的泵机,达到最小化功耗的需求。由此可见,本文设计算法能够有效解决沿海型炼厂原油移动路径规划问题。     

多目标原油选择与混合优化方法

为保证原料的稳定供应,炼厂经常需要寻找相似原油替代库存量少的原油.以混合原油与目标原油质量偏差最小以及混合原料成本最低为目标,构建了一个多目标原油选择与混合优化模型.模型为混合整数非线性规划模型,可以将寻找替代原油转化为原油选择与混合优化问题.在原始多目标布谷鸟搜索(MOCS)算法基础上,对编码以及Lévy飞行进行了改进,结合非支配排序方法提出了一种改进的多目标布谷鸟搜索(IMOCS)算法.利用IMOCS算法求解模型,可同时确定原油的选择和混合比例,且一次计算可得到一组Pareto最优解.通过仿真,与非支配排序遗传算法(NSGA-Ⅱ)进行对比,验证算法的寻优效果.计算得到的混合方案可为炼厂寻找替代原油提供参考.     

炼化企业生产数据集成应用平台的设计与实现

智能炼厂建设的核心是对生产过程的智能管控,建立生产计划优化模型和各类装置的模拟优化模型是其中的重要内容。这些模型的建立和应用涉及到企业多个应用系统中的大量数据,收集、整合各类数据是智能优化工作中重要而繁琐的工作,针对某炼化企业智能优化工作的需求,开发了生产数据集成应用平台。数据集成应用平台的开发采用了先进的B/S技术、数据库技术,通过定时对各类生产数据进行采集整理,生成完善的模型支撑数据库,实现了各类装置数据的快速、方便查询;所具有的数据处理功能多层次展示数据变化趋势,方便用户筛选有效数据;数据应用模版的建立,提高了优化模型建模的便利性和建模数据的可靠性。数据集成应用平台运行稳定,界面友好,效率高,系统响应速度小于15秒,为计划优化模型和过程模拟优化模型的建模及应用提供了及时、准确的数据支撑,为进一步的企业生产优化和智能炼厂建设奠定了数据基础。     

产品开发方案优化的模糊机会约束规划模型及求解

为解决项目网络计划中产品开发方案的时间、成本、质量的不确定优化问题,在模糊机会约束规划的框架下分别建立了基于关键路线的项目周期优化模型、基于资源使用的项目周期-成本优化模型和基于质量功能展开(QFD)的产品周期-质量优化模型,在此基础上构建了项目周期-成本-质量多目标优化模型;在使用模糊模拟技术处理3个目标函数约束的过程中,构建了一个基于遗憾度的适应度函数,然后利用离散微粒群算法对多目标优化模型进行求解.最后,以滚珠丝杆副产品开发项目的方案优化为例验证了文中模型的合理性和算法的有效性;实验结果表明,周期-成本-质量多目标优化模型的求解得到的方案能够取得时间、成本和质量的平衡优化,为产品开发项目的规划提供了定量、可靠的决策依据.     

考虑石脑油两种用途的原油混输调度模型

针对炼厂石脑油用于催化重整和乙烯生产两种用途的原油混输调度问题建立数学模型，把重整过程和乙烯生产对石脑油芳烃潜含量的不同要求用线性规划的方法实现，以原油混输调度中对不同类的原油根据需要分别输送和储存，为常减压装置准备好“重整石脑油方案”和“乙烯石脑油方案”的原料，真正符合“宜芳则芳”、“宜烯则烯”的调度原则。建模中应用了MILP(混合整数线性规划)技术和多周期技术，并给出一个应用在6种不同的国外原油及6个生产调度周期的应用实例，考虑了原油从装置罐到厂区罐的一次混输调度，尽量保证管道和厂区罐调度周期末原油质量合格。     

炼钢-连铸-热轧一体化生产计划编制方法研究

为解决炼钢一连铸一热轧一体化生产计划编制问题,建立了一种一体化生产计划模型体系,将一体化生产计划编制这个复杂优化问题分解为5个局部优化问题。每一个局部优化问题采用多模型协作方式来编制各自范围内的生产计划,5个部分之间采用多系统协作方式完成一体化生产计划的编制。通过实际一体化生产计划软件系统的开发与应用,表明这种模型体系可以很好解决一体化计划编制问题。     

原油选择优化模型在企业中的应用

本文论述了用于炼油工业的原油选择优化模型评价系统。精确企业模型建立是原油选择优化的基础,描述了企业模型的框架结构,并详细阐述了工艺流程模型、能耗模型、硫分布模型等建立精确企业模型所需要的内容。线性规划是求解大型优化命题的有力工具,文中论述了线性规划的特点及意义。在分析原油选择优化命题特点的基础上,利用成熟的线性规划软件和原油数据库、工艺模型、油品调合等技术建立企业模型系统,准确描述炼油工业中的物流传递、能量传递和性质传递。所建立的模型系统以经济目标为导向,结合国家环保政策法规要求,新工艺发展,为企业制定合理的原油优化选择提供定量的解决方案。应用案例结果证明利用线性规划和建立企业模型能有效解决原油优化选择问题。     

基于异步时间段的原油混输调度连续时间建模研究

采用严格的数学规划方法对沿海炼厂的原油混输调度问题进行了建模和求解,提出了一个基于异步时间段的连续时间混合整数非线性规划(MINLP)数学模型。该模型具有整型变量少,求解快的优点。采用求解一系列MILP来近似求解非凸的MINLP,避免了原油混输MILP模型产生的浓度不一致问题。文中采用提出的新模型和算法对文献中的9个实例进行了计算,相同计算条件的4个实例比离散时间模型效率提高86％-1011％。     

考虑结垢问题的常减压换热网络优化

原油预热装置是炼厂热量回收利用的重要组成部分,提高原油的终温可有效降低加热炉瓦斯气的消耗。而结垢则严重地影响换热器的效率,导致工艺参数偏离设计值。根据结垢阈值模型,结垢速率与换热器壁温呈正相关。本文在利用传统的夹点技术改造某常减压预热装置的基础上,考虑换热器的结垢问题。通过重新匹配冷热物流降低壁温,采用夹点技术从能量回收和结垢两方面提出改造方案。计算结果表明,考虑结垢的换热网络优化方案,节能效益显著。     

约束规划方法在原油混输调度中的应用

应用约束规划方法建立炼厂原油混输模型,并通过搜索求解获得可操作的调度方案。约束规划允许以逻辑约束表达混输过程中的操作规则,使模型简洁直观,而对整型变量及非线性约束不敏感,则降低了模型的求解难度。选取了一个时间跨度为300小时实际调度问题作为案例,模型中包含约8000个变量和14000条约束,在普通PC机上计算求解,能够在30秒内获得调度方案,方案具有可操作性且优于人工方案,表明以约束规划方法解决原油混输调度问题是可行的。     

炼油企业生产计划与重点装置工艺条件集成优化

传统的生产计划优化由于不考虑过程装置的操作优化,从而无法保证企业生产计划层与过程操作层的全局最优.为了在获得炼油企业最优生产计划的同时,确保计划优化中重点装置的操作条件可以实现,本文建立了集成装置工艺条件的炼油企业生产计划优化模型.该模型引入常减压装置侧线产品切割点温度、催化裂化装置转化率等过程工艺条件,基于物料质量平衡、产品质量指标约束等关系,进行厂级生产计划建模与求解,确定可达的装置操作条件.应用案例中重点通过与传统常减压装置侧线收率固定的生产计划方案比较,证明在满足可达的操作条件下,集成装置工艺条件操作范围的生产计划优化模型,可以实现更高的全厂利润与更优的装置收率分布,同时优化结果对炼厂实际生产更具有指导意义.     

A Petri Net-Based Heuristic Algorithm for Realizability of Target Refining Schedule for Oil Refinery

In discrete manufacturing, a just-in-time schedule is pursued so as to respond better to the market. It is also required in oil refinery. However, the existing techniques for short-term scheduling in oil refinery are based on the push production mode. This paper addresses the short-term scheduling problem for crude oil operations in a pull production way. A target refining schedule resulting from production planning is given as a constraint to make an executable schedule. The system is modeled by a timed hybrid Petri net. This model is structurally simple and can describe the dynamic behavior and all the constraints of the system without any difficulty. Based on the model, an efficient heuristic algorithm is proposed to test the realizability of a target refining schedule. If it is realizable, a feasible short-term schedule realizing it is created. A real-life industrial case study is presented to show the industrial application of the proposed method.     

A cutting plane algorithm for the site layout planning problem with travel barriers

Site layout planning is an imperative procedure that may significantly impact the productivity and the efficiency of logistical operations undertaken on a construction site. This paper considers the site layout planning problem (SLPP) which entails the allocation of temporary facilities on a construction site in the presence of travel barriers such that the total transportation cost between facilities is minimised. In order to account for travel barriers, the SLPP is typically solved under the assumption that the available region for facility layout can be discretised. In this paper, we propose a general Mixed Integer Programming (MIP) model to represent the SLPP, accounting for the presence of barriers, and we show how space-discretised formulations can be derived from this model. In particular, we propose a novel MIP model, which permits facilities to cover multiple locations. This is then benchmarked against a commonly adopted MIP model in the literature. We also highlight a systematic procedure to convert the continuous feasible space in SLPP to a set of discretised locations based on the concept of d-visibility, enabling us to approximate the barrier distance function embedded in the objective function. In particular, we focus on presenting a simple space discretisation approach for converting the continuous SLP into a discrete problem for which the discrete SLP models would be applicable. Space-discretised MIP formulations are highly combinatorial and we introduce a cutting plane algorithm to improve their tractability. Specifically, we propose a novel exact location-decomposition algorithm which works from a relaxed MIP formulation and iteratively generates feasibility cuts to converge to an optimal solution. Both space-discretised MIP models and the decomposition algorithm are tested on a large group of instances to analyse their effectiveness in solving the SLPP. Computational results indicate that the proposed location-decomposition algorithm improves on the pure MIP approach and provides a competitive framework to solve realistic SLPP instances.     

Stochastic analysis of crude oil procurement and processing under uncertain demand for bunker fuel oil

Bunker fuel oil (ifo), one of the products of petroleum refining, has a strong impact in the production process because it drives the availability of heavy residues that depend on the crude quality. A simplified stochastic model for the Administración Nacional de Combustibles Alcohol y Portland refinery, based on the uncertainty of the demand for ifo, is proposed for comparison with the current approach of deterministic demand. In this model, the benefits of the production process are maximized, taking decisions on the more suitable raw material, intermediate products and final blends in order to fulfill quality and demand requirements of final products. A specific case is analyzed where the maximum benefit is achieved when the most expensive crude quality is purchased, due to a lack of incentive to produce extra amounts of heavy fuel oil that must be exported at a non‐attractive price. Results are compared with the solution of a deterministic model with mean demand. In addition, the stochastic model solution depicts how the refinery should operate for each scenario of ifo demand.     

A heuristic relief transportation planning algorithm for emergency supply chain management

Emergency supply chain operations have to fulfil all the demands in a very short period of time, using the limited resources at its disposal. Mixed integer programming (MIP) is a popular method to solve emergency supply chain planning problems. However, as such problem increases in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. This study proposes a heuristic algorithm, called the Emergency Relief Transportation Planning Algorithm (ERTPA). ERTPA will group and sort demands according to the required products, the imposed due dates, the possible shared capacities, and the distances from the demand nodes to the depots. Then, ERTPA plans the demands individually, using a shortest travelling-time tree and a minimum cost production tree. To show the effectiveness and efficiency of the heuristic algorithm, a prototype was constructed and tested, using complexity and computational analyses.     

Short-term harvest planning including scheduling of harvest crews

The problem we consider is short‐term harvesting planning for a total planning period of 4–6 weeks where we want to decide the harvest sequences or schedules for harvest crews. A schedule is an order or sequence of harvest areas assigned to each crew. The harvesting of areas is planned in order to meet industrial demand. The total cost includes harvesting, transportation, and storage. One considerable cost is due to the quality reduction of logs stored at harvest areas. There are a number of restrictions to be considered. Areas are of varying size and the composition of assortments in each area is different. Each harvest team has different skills, a different home base, and different production capacity. Another aspect is the road network. There is a cost related to road opening (restoring, snow removal). In this paper, we develop a mixed integer programming (MIP) model for the problem. The schedules are represented by 0/1 variables. With a limited number of schedules, the problem can be solved by a commercial MIP solver. We have also developed a heuristic solution approach that provides high‐quality integer solutions within a distinct time limit to be used when more schedules are used. Computational results from a major Swedish forest company are presented.     

IGP/MPLS混合的IP网络不确定流量规划方法

随着IP网络的迅速发展,网络的业务量越来越呈不确定性和不可预测性。针对此问题,提出了以软管模型为基础的不确定业务量模型下的IGP/MPLS混合IP流量规划问题的混合整数规划(MIP)模型,并将该模型分解成权重设计子问题和流量分配子问题进行求解。实验结果表明,该方法能在标签交换路径(LSP)较少的情况下达到较理想的优化效果。     

流程企业计划调度体系结构研究

以石化企业为背景，在深入分析流程企业计划调度系统结构特征的基础上，结合流程企业计划调度的关键目标，提出了一种基于模型的中心化流程企业计划调度通用结构，突出了计划调度的中心地位．以此为指导，进一步建立并实现了基于知识的智能计划调度总体结构，为流程企业生产系统的运行优化提供了方法论的指导。     

Efficient solution of the 3G network planning problem

The 3G universal mobile telecommunications system (UMTS) planning problem is combinatorially explosive and difficult to solve optimally, though solution methods exist for its three main subproblems (cell, access network, and core network planning). We previously formulated the entire problem as a single integrated mixed-integer linear program (MIP) and showed that only small instances of this global planning problem can be solved to optimality by a commercial MIP solver within a reasonable amount of time ( St-Hilaire, Chamberland, & Pierre, 2006). Heuristic methods are needed for larger instances. This paper provides the first complete formulation for the heuristic sequential method ( St-Hilaire, Chamberland, & Pierre, 2005) that re-partitions the global formulation into the three conventional subproblems and solves them in sequence using a MIP solver. This greatly improves the solution time, but at the expense of solution quality. We also develop a new hybrid heuristic that uses the results of the sequential method to generate constraints that provide tighter bounds for the global planning problem with the goal of reaching the provable optimum solution much more quickly. We empirically evaluate the speed and solution accuracy of four solution methods: (i) the direct MIP solution of the global planning problem, (ii) a local search heuristic applied to the global planning problem, (iii) the sequential method and (iv) the new hybrid method. The results show that the sequential method provides very good results in a fraction of the time needed for the direct MIP solution of the global problem, and that optimum results can be provided by the hybrid heuristic in greatly reduced time.