钢铁智能制造背景下物质流和能量流协同方法

钢铁企业面临库存增加、产能过剩、盈利艰难等问题,迫切需要通过绿色化和智能化的发展来应对当前的严峻形势.基于钢铁企业制造流程特点,通过分析企业以铁素流为核心的物质流和相应能量流网络特征,针对企业现有信息化系统在物质流和能量流协同方面存在的问题,提出钢铁制造过程的物质流和能量流的协同方法,明确物质流和能量流的耦合应从钢铁制造的单元工位设备与整体流程网络两个层面进行规划、设计和实施,并且指出可从完善信息监控、进行计划协同和调度协同三个方面来实现协同优化.构想基于现有信息系统架构,通过增加相应企业资源计划系统、制造执行系统、能源管理系统等信息系统的功能,以及建立物质流和能量流协同优化信息子系统的方式,以钢铁制造过程的物质流和能量流相关信息的数字化及模型化为支撑,实现制造流程的物质流和能量流协同优化,达成生产优化、资源优化和能源优化的效果.     

钢铁工业智能制造的集成优化

 钢铁工业智能制造的目的，是围绕钢铁流程功能，进行结构优化与程序优化，实现全流程动态有序、协同连续运行和多目标整体优化。介绍了钢厂结构优化和程序优化的主要内容和需要研发的关键技术，包括产品质量闭环管控、一体化计划调度、物质流能量流协同和多工序协调优化。提出了钢铁工业推进智能制造需要突破的一些共性问题和实施路径。     

TFT-LCD制造cell阶段多目标绿色调度问题研究

针对薄膜晶体管液晶显示器(TFT-LCD)制造cell阶段的多目标绿色调度问题,构建了最小化最大完工时间、总能耗和总生产成本为目标的优化模型。采用基于机器和工序的两段式编码,使用在步长因子前加入动态系数的改进布谷鸟搜索算法,结合双元锦标赛和动态淘汰制,根据聚集距离法筛选Pareto最优解来获得Pareto最优解集,对TFT-LCD制造cell阶段多目标绿色调度问题进行求解。结果表明,改进布谷鸟搜索算法优于标准布谷鸟搜索算法和带精英策略的快速非支配排序遗传算法,可以提高TFT-LCD制造cell阶段多目标绿色调度问题的求解效率和质量。     

炼钢-连铸过程生产计划与调度问题综合优化研究

生产计划与调度是炼钢厂生产管理与控制的重要环节。对特殊钢厂炼钢-连铸过程的生产计划与调度问题进行述评,对国内外其研究现状进行分析,并指出了其生产计划与调度结构的发展趋势。结果表明,合理的生产计划与调度方案,是特殊钢厂实现高品质、高效生产的重要途径,亦可对其全过程进行高效协调与管理,从而降低能耗和冶炼成本。     

车间多载自动导引车绿色物流调度

随着现代制造业的飞速发展,企业在生产效率和生产能耗方面有越来越高的要求,智能生产车间的自动化程度逐渐提高。主要研究作业车间自动导引车(automated guided vehicle,AGV)的智能绿色物流调度问题。首先,建立以降低AGV能耗和最优AGV路径为目标的AGV物流调度优化模型;然后,提出一种以任务排序为约束的改进遗传粒子群算法;最后,以某针织车间的实际物流调度为例对文中方法进行验证。计算结果表明,文中提出的AGV物流调度模型能够较好地模拟AGV绿色调度耗能问题,提出的改进遗传粒子群算法具有较快的收敛速度和较好的寻优能力。     

氨纶生产的整体优化与自动控制系统开发

为解决我国氨纶的需求量高、产量低问题，对聚合反应的时序、流程图进行了分析和改造，同时利用计算机互联网的方法和改进硬件设备的方法，实现了生产线的最佳改造。采用DCS（Distributed Control System）控制方法对多条生产线的综合优化进行了研究,并针对辽源得亨公司的生产现状，开发了基于现代集成制造系统、综合自动化技术和最优工艺调度技术的氨纶计算机综合控制系统。该系统最大限度地减少生产成本，增加企业效益的目的。     

钢坯热轧加热炉区生产调度模型与算法

钢坯热轧加热炉区生产调度属于组合优化中的NP-complete问题.本文根据加热炉区生产特点建立了分别以生产能耗最小化和加热质量最优化为主次目标的钢坯加热炉区调度数学模型,将其归结为布尔可满足性问题,构造了采用二进制编码方式的遗传禁忌搜索算法进行求解.基于实际生产数据的模拟优化结果表明,该模型和求解方法充分满足了现场加热炉区生产调度的需求,在满足生产工艺约束的前提下,缩短了生产时间,提高了钢坯入炉温度和加热质量,与传统人工调度方法的结果相比具有更好的节能、高产效果.     

炼钢厂系统生产模式优化

根据冶金过程工程理论,针对钢铁制造过程系统中制造过程最为复杂的炼钢厂系统的生产模式优化问题进行探讨,阐述了生产模式在炼钢厂系统运行过程中的地位及其逻辑关系,并结合四个国内高效连铸工厂实际进行了研究,论述了生产模式优化的三个效用:(1)车间平面布置趋向合理、简洁;(2)工序缓冲趋向合理、有效;(3)工序运行趋向高效、优化.此外,上述研究结果已经拓展到特殊钢流程的运行优化与控制研究中,并取得有益的效果.     

带柔性工艺的多品种批量生产投产计划仿真优化

针对带柔性工艺的多品种批量生产系统,提出了一种基于Witness仿真的投产计划优化方法。描述了待研究的科学问题。针对带柔性工艺的多品种批量生产特点,设计了仿真逻辑。以Witness为仿真平台,构建了多品种批量生产投产计划与调度仿真模型。通过仿真试验验证了所提方法的有效性。     

集成式工艺规划与车间调度问题研究现状及发展

集成式工艺规划与车间调度(IPPS)问题的研究对于制造系统整体性能的提高具有重要意义,综述与分析了近年来IPPS问题研究的发展现状.概述了IPPS的3种主要建模方法,分析了特定生产环境下工艺规划与车间调度进行集成的特殊性.从单目标优化与多目标优化两方面综述了IPPS求解方法的研究概况,分析了多目标IPPS求解方法研究中存在的问题.总结了不确定扰动下的IPPS问题及面向绿色制造的IPPS问题研究现状,分析了2种情况下IPPS模型构建和求解方法设计上带来的新问题.基于上述综述分析,展望了未来开展集成式工艺规划与车间调度问题研究的一些方向.     

考虑生产组织约束的钢铁企业合同计划

针对典型钢铁生产流程A,分析实现流程高效运行的铁钢界面、脱磷与脱碳转炉、精炼设备以及铸轧界面的生产组织约束。建立了考虑生产组织约束的钢铁企业合同计划模型,模型中充分考虑了不同生产线的排产紧凑与协调以及合同投产日期的优化,之后采用禁忌搜索算法对模型进行了求解。最后,从钢厂A中随机选取3组不同规模的实际生产合同,并对其进行仿真实验。仿真结果表明：所建立的合同计划模型与求解算法能够有效协调和优化生产,实现钢铁制造流程高效运行。     

Distributed Scheduling Problems in Intelligent Manufacturing Systems

Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations.Nowadays, distributed manufacturing systems have been widely adopted in industrial production processes. In recent years, many studies have been done on the modeling and optimization of distributed scheduling problems.This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems. By summarizing and evaluating existing studies on distributed scheduling problems, we analyze the achievements and current research status in this field and discuss ongoing studies. Insights regarding prior works are discussed to uncover future research directions, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems. This work focuses on journal papers discovered using Google Scholar. After reviewing the papers, in this work, we discuss the research trends of distributed scheduling problems and point out some directions for future studies.     

基于项目拆分决策的多模式资源投入

结合一类实际生产决策需求,提出了基于项目拆分决策的多模式资源投入调度问题,并以资源投入最小化为优化目标,建立了问题的数学模型.针对模型特点,提出了包含项目拆分算法和多模式资源投入型项目调度算法的双层优化算法,其中项目拆分算法通过将作业在不同子项目之间有效移动获得合理的拆分方案,多模式资源投入型项目调度算法通过分析不同作业对时间约束和资源约束的影响来确定优先级规则,进而得到最佳调度方案.应用PSPLIB标准算例进行数据实验,结果证明了算法的有效性和可靠性.     

多Agent蚁群算法求解炼钢-连铸动态调度问题

为了求解炼钢-连铸动态调度问题,提出了多Agent系统与蚁群算法相融合的方法。建立了该问题的约束满足模型和多Agent协商机制;并将炉次与蚂蚁相对应,把调度过程看作路径寻优来处理,在满足约束的前提下搜索炉次全程占用时间最短的路线。针对国内某钢厂建立了仿真实验模型。结果表明,该方法能够缩短炉次在炼钢-连铸过程占用的时间,并能够在发生突发事件时做出快速合理的调整。     

基于遗传算法的离散型并行生产的调度排优

在分析了CIM体系结构中离散型并行生产调度问题的基础上,基于遗传算法,针对调度排优问题,建立了以总加工完成时间最短为优化目标的数学模型,并设计了遗传算法的编码、交叉、变异算子,详细描述了遗传算法的各个组成部分·利用数据库技术,开发了实现该算法的可视化软件,并在实际应用中实现了调度系统与CIMS其他模块之间的集成·理论分析与实践表明,遗传算法简明、有效并且实用,所求解得到的优化作业排序对于并行生产的车间作业调度具有很好的指导意义·     

制造单元快速构建技术研究

针对制造单元的快速构建和提高可实施性的问题,提出了一种采用聚类分析与启发式算法相结合的解决方案. 在零件存在多工艺路线、工序存在多可选设备的基础上,综合考虑加工时间、加工顺序、生产批量、设备能力、任务交货期等实际生产约束条件,以最短加工时间、最佳设备及单元平衡、最少跨单元加工等作为重构优化目标,重视生产瓶颈环节的识别和处理,采用设备单元构建、资源优化分配和单元负荷均衡的三阶段处理技术,实现了制造单元的快速构建,导出了零件族、设备集合以及瓶颈问题的解决方案,并通过实例验证了算法的有效性.     

基于多智能体的虚拟企业任务调度研究

采用多智能体技术构建了虚拟企业任务调度运行模型,该模型包括管理智能体、任务智能体、资源智能体等。针对模型中的调度优化问题,以资源智能体所承担的生产任务为对象,综合考虑资源智能体自身已确定的生产任务、完成虚拟企业生产任务所需作业时间及转运时间等因素,建立了以生产延续时间最小化为目标的优化调度模型,并给出了粒子群优化求解算法。工业机械手实例说明了任务调度模型及实现方法的有效性。     

Intelligent logistics system of steel bar warehouse based on ubiquitous information

Internet of Things and artificial intelligence technology are the key elements of the intelligent construction of iron and steel production warehouse. This paper puts forward a whole set of intelligent scheme for bar warehouse crane for the guidance of metallurgical process engineering, including cluster rapid self-awareness technology of the smart crane, precise self-executing technique of crane with rigid-flexible hybrid structure, multi-body system kinematics model of the smart crane sling and the swing characteristics model at different azimuth, antiswing control technology based on the optimization objective function, the vehicle model recognition system based on lidar, and the clustering crane dynamic scheduling method based on multi-agent reinforcement learning. The complete intelligent logistics system of the bar warehouse has changed the original operation mode of the warehouse area and realized the unmanned operation and intelligent scheduling of the crane,which is of great significance for improving the production efficiency, reducing the production cost, and improving the product quality.     

基于能源枢纽的钢铁制造过程多能源协同优化

The production process of iron and steel is accompanied by a large amount of energy production and consumption. Optimal scheduling and utilization of these energies within energy systems are crucial to realize a reduction in the cost, energy use, and CO₂ emissions. However, it is difficult to model and schedule energy usage within steel works because different types of energy and devices are involved. The energy hub (EH), as a universal modeling frame, is widely used in multi-energy systems to improve its efficiency, flexibility, and reliability. This paper proposed an efficient multi-layer model based on the EH concept, which is designed to systematically model the energy system and schedule energy within steelworks to meet the energy demand. Besides, to simulate the actual working conditions of the energy devices, the method of fitting the curve is used to describe the efficiency of the energy devices. Moreover, to evaluate the applicability of the proposed model, a case study is conducted to minimize both the economic operation cost and CO₂ emissions. The optimal results demonstrated that the model is suitable for energy systems within steel works. Further, the economic operation cost decreased by 3.41%, and CO₂ emissions decreased by approximately 3.67%.     

Multi-energy synergistic optimization in steelmaking process based on energy hub concept

The production process of iron and steel is accompanied by a large amount of energy production and consumption. Optimal scheduling and utilization of these energies within energy systems are crucial to realize a reduction in the cost, energy use, and CO_2 emissions.However, it is difficult to model and schedule energy usage within steel works because different types of energy and devices are involved. The energy hub(EH), as a universal modeling frame, is widely used in multi-energy systems to improve its efficiency, flexibility, and reliability.This paper proposed an efficient multi-layer model based on the EH concept, which is designed to systematically model the energy system and schedule energy within steelworks to meet the energy demand. Besides, to simulate the actual working conditions of the energy devices, the method of fitting the curve is used to describe the efficiency of the energy devices. Moreover, to evaluate the applicability of the proposed model, a case study is conducted to minimize both the economic operation cost and CO_2 emissions. The optimal results demonstrated that the model is suitable for energy systems within steel works. Further, the economic operation cost decreased by 3.41%, and CO_2 emissions decreased by approximately 3.67%.