空分短期停车时间阈值对氧气生产调度的影响

针对钢铁企业高炉休风场景下的氧气生产调度问题,提出以空分短期计划性停车为主要手段的调度策略,并基于MILP方法建立了氧气系统的优化调度模型。调度模型的优化目标为整个规划周期内氧气高压管网的综合压力最小化。模型包含了空分和部分氧气压缩机短期停车再启动操作的约束条件,并结合实际情况考虑了前述设备的停车时间阈值和运行时间阈值。以国内某大型钢铁企业为实际案例,验证了调度模型的合理性与可行性,然后基于模型计算分析了空分停车时间阈值对调度目标的影响规律。分析结果表明,减小空分停车时间阈值有利于获得更优的调度目标,但空分停车时间阈值对优化目标的影响规律具有阶跃特性,而非简单的比例关系。     

基于资源——任务网络的生产调度优化

生产调度能为企业带来明显的经济效益,基于资源-任务网络生产调度优化,是理论和应用研究的一个热点问题。文章将基于——资源任务网络的离散时间生产调度优化模型应用于单台变负荷空分生产流程中。求解优化模型可知优化结果优于人工调度。     

印染车间作业计划优化调度

由于基于统一离散时间表示的生产过程优化模型的约束和变量多,基于连续时间的批处理短期调度在近10年得到了广泛的重视和研究。本文简略介绍了生产过程调度模型的情况,通过对生产过程优化调度模型和印染生产工艺的研究,建立了基于连续时间印染生产过程优化调度MILP模型。然后,通过把数学模型转换成IL-OG OPL语言描述的模型,以浙江省某印染企业2个案例为数据,利用ILOG CPLEX进行求解,调度结果以甘特图的形式表达。结果表明印染生产连续时间MILP调度模型的有效性,优化了车间生产资源的配置。     

基于铜基载氧体的化学链空分制氧系统的研究

基于铜基载氧体，运用流程模拟软件Aspen plus对化学链空分制氧系统展开了研究，构建化学链制氧系统模型，分析了反应温度，载气流量等因素对系统性能的影响，对化学链空分制氧系统进行参数优化。此外，本文也构建了传统的深冷空分制氧系统，与化学链制氧系统进行了对比分析。结果表明：较高的释氧反应器温度可提高氧气浓度和氧气的摩尔流量；水蒸气流量的增加也有利于释氧反应的进行。与深冷空分制氧技术相比，化学链空分制氧技术不仅系统能耗较低，而且制备的氧气浓度也较高。     

高校共享停车行为特性及影响机理分析

以调节高校泊位供需状态,优化停车资源利用为目标,通过改变停车场的特定条件来影响驾驶人对于停车设施的选择。首先以华清学院为例分析了高校泊位空闲时间特性;然后利用二元Logit模型对驾驶人的泊位共享选择过程建模,并且基于BP神经网络进行自变量筛选及建模分析;最后通过模型对比发现BP神经网络筛选后的Logit模型能够达到更高的精度要求,故以此来分析高校共享停车行为特性及影响机理。     

深冷空分精馏系统动态过程的研究进展

在能源紧张和产品需求多元化的背景下,不断响应时变电价、满足市场需求的空分系统呈现出越来越多的动态特性。精馏单元动态特征明显,是影响空分装置流程动态行为的关键设备,急需对空分装置中精馏单元的动态过程展开深入研究。文章综述了在空分精馏塔动态过程中的研究方向、模型和求解算法:首先将空分精馏塔动态过程中的研究分为过程变负荷控制、操作优化、调度规划和仿真软件4类,分别阐述了各部分的研究内容;然后介绍了动态建模的机理模型、数据模型和降阶模型,其目标是建立适合问题需要的合适精度模型;最后讨论了基于机理模型的动态优化问题中微分代数方程组的求解算法,并展望了空分系统未来的研究前景,实现机理模型和数据模型的有机融合和空分精馏塔动态过程中性能监控、运行维护将成为今后的研究重点。     

钢铁企业蒸汽系统多周期优化策略

钢铁企业蒸汽系统具有汽源设备较多、能源品种多以及能源供需及价格变化等特点,本文以某大型钢铁企业实际运行的蒸汽动力系统为研究对象,综合考虑其各台锅炉设备的不同生产特点、企业的能源需求和能源价格随时间变化的规律以及设备启停运行转换产生一部分费用等因素建立约束条件,以全时段蒸汽系统总能源成本最低为目标函数,建立蒸汽-电力耦合的多周期、混合整数非线性规划模型(MINLP),利用基于数学规划方法的LINGO软件求出全局最优解。通过分析比较,证明初始运行条件对多周期优化结果有很大影响,影响效果会延续数个周期,同时LINGO计算求解全局最优解的效率高、结果合理、可行,并能够为企业在能源设备调度上提供依据,实现一个运行时段内的低成本和高效益。     

柴油调合工艺与短期调度集成优化方法研究

针对柴油调合短期调度过程中存在的柴油油品属性波动较大,难以预测和一次调合成功率要求高等实际应用难题,设计了1种适合实际工程应用的柴油调合工艺与短期调度计划集成优化方法,结合国内某炼油厂的柴油调合短期调度现状,建立柴油调合配方优化模型和柴油短期调度优化模型,选择线性规划算法并构建数据库及创建关联数据表,并将模型集成至基于...     

多因素不确定条件下的间歇生产调度优化

不确定条件下的间歇生产调度优化是生产调度问题研究中具有挑战性的课题。提出了一种基于混合整数线性规划(MILP)的鲁棒优化模型，来优化不确定条件下的生产调度决策。考虑到生产过程中的操作成本和原料成本，建立了以净利润最大为调度目标的确定性数学模型。然后考虑需求、处理时间、市场价格三种不确定因素，建立可调整保守程度的鲁棒优化模型并转换成鲁棒对应模型。实例结果表明，鲁棒优化的间歇生产调度模型较确定性模型利润减少，但生产任务数量增加，设备空闲时间缩短，从而增强了调度方案的可靠性，实现了不确定条件下生产操作性和经济性的综合优化。     

单套空分装置带两台气化炉的优化运行方案

本文以公司生产运行实例对德士古气化工艺中一套空分装置停车、一台气化炉停车后,以单套空分装置带两台气化炉的优化运行方式进行叙述,为同类型煤化工生产运行提供参考。     

钢铁企业煤气-蒸汽-电力系统耦合优化及应用

煤气、蒸汽和电力是钢铁联合企业能源系统的重要组成部分,其消耗占钢铁企业总能耗的60%左右。合理地制定能源系统的生产以及燃料消耗、电力采购计划,对企业降低成本、减少环境污染有着非常重要的意义。针对煤气-蒸汽-电力系统的燃料结构、设备类型、工况变化等特点,以经济运行成本和环境成本最小为目标函数,建立了针对该系统的耦合优化模型。该模型综合考虑了富余煤气的波动、蒸汽和电力的动态需求、多燃料结构、分时电价等影响因素,并使用GAMS进行优化求解。将模型应用到某大型钢铁联合企业,结果表明该模型能够为钢铁企业煤气-蒸汽-电力系统提供合理的生产计划方案,实现了富余煤气的合理分配以及能源的高效利用,降低了能源系统运行成本,提高了企业的经济效益和环境效益。     

钢铁企业蒸汽产生量预测模型研究

提出一种基于小波变换-最小二乘支持向量机的钢铁企业蒸汽产生量预测方法。先对数据进行小波变换以提取数据的特征;然后建立LS-SVM模型,对各分量进行预测以提高预测精度。实验结果表明:小波变换-最小二乘支持向量机预测方法预测精度高、性能好,具有良好的实用性,为蒸汽生产优化调度提供了科学的依据。     

MILP-based optimization of oxygen distribution system in integrated steel mills

Simultaneous multiperiod optimization is conducted for minimizing the oxygen emission of an oxygen-distribution system, based on the generalized MILP-based model, which covers various configurations of the captive oxygen factory in integrated steel mills. By simultaneously optimizing all of the variables, such as the load of air separation units (ASU), the on-off states of compressors, the load of liquefiers, etc., the model can promptly provide mill managers with responsive solutions for adjusting the variables involved on the supply-side to minimize oxygen emission. The case study in this paper shows that the proposed model performs well in minimizing oxygen emission, and provides a global optimization result covering the entire planning horizon. Moreover, based on the proposed model, the emission amounts can be rapidly and readily calculated for various scheduling scenarios of ASU maintenance, which is helpful to the manager seeking to optimally schedule ASU maintenance in time.     

A nested online scheduling and nonlinear model predictive control framework for multi-product continuous systems

In the pursuit of integrated scheduling and control frameworks for chemical processes, it is important to develop accurate integrated models and computational strategies such that optimal decisions can be made in a dynamic environment. In this study, a recently developed switched system formulation that integrates scheduling and control decisions is extended to closed-loop operation embedded with nonlinear model predictive control (NMPC). The resulting framework is a nested online scheduling and control loop that allows to obtain fast and accurate solutions as no model reduction is needed and no integer variables are involved in the formulations. In the outer loop, the integrated model is solved to calculate an optimal product switching sequence such that the process economics is optimized, whereas in the inner loop, an NMPC implements the scheduling decisions. The proposed scheme was tested on two multi-product continuous systems. Unexpected large disturbances and rush orders were handled effectively.     

A mathematical programming approach for cyclic production and cleaning scheduling of multistage continuous plants

The objective of this paper is to address the cyclic scheduling of cleaning and production operations in multiproduct multistage plants with performance decay. A mixed-integer nonlinear programming (MINLP) model based on continuous time representation is proposed that can simultaneously optimize the production and cleaning scheduling. The resulting mathematical model has a linear objective function to be maximized over a convex solution space thus allowing globally optimal solutions to be obtained with an outer approximation algorithm. Case studies demonstrate the applicability of the model and its potential benefits in comparison with a hierarchical procedure for the production and cleaning scheduling problem.     

大型蒸汽管网系统的运行优化调度

介绍了国内大型钢铁企业低压蒸汽系统存在的主要问题,在分析低压蒸汽系统结构的基础上,针对大型蒸汽管网不同产汽源系统建立相应的数学模型,利用改进单纯型法求已建立的线性规划模型的最优解,并以某大型钢铁企业为例,证明了应用优化模型的有效性,通过蒸汽系统优化调度,钢铁企业具有巨大的节能潜力。     

大型钢铁企业能源管理系统的设计与实现

通过对大型钢铁企业能耗情况和现有能源管理模式的分析,系统地介绍了能源管理系统的设计,主要包括数据集成、能源系统实时监控、能源管理软件开发及能源优化调度等。通过能源管理系统的实现,可有效提高企业的能源和资源利用率。     

Advanced enterprise resource management systems for the batch industry. The TicTacToe algorithm

Since 1997 the authors have prototyped an enterprise resource management (ERM) system recommending essential improvements to the available software packages, which today claim automatic real time performance and an efficient transaction-oriented approach. The ERM system proposed is a decision-making tool for the manufacturing industry, which makes integrated financial/production trade-off planning and optimisation in the supply chain management. The application supports management cycles because during its use are available degrees of freedom to make the system work as a decision-making tool. The enterprises must be aware of its internal problems to be dynamically approachable instead of prematurely evolving to networked enterprises by links in the electronic commerce. ERM systems are capable of managing dynamically realistic and optimal delivery dates with price-time trade-off during the marketing activities. Cost objective functions integrated with system performance measures are developed, including a cost model to evaluate the due date policy economics in real time. The order management system proposed is based on the TicTacToe algorithm, which follows an exact non-combinatorial deterministic approach to time and optimise production sequences in multiproduct plants in very short time using asymmetric travelling salesman problem formulations for the scheduling task. The resulting tool is appropriate to fulfil the requirements of autonomous order entry systems in integrated real time systems.     

Optimal response under partial plant shutdown with discontinuous dynamic models

This work describes mathematical formulations for modeling aspects of partial shutdowns in multiunit plants. The specific type of partial shutdown considered is one that permits the decoupling of affected units from the rest of the plant, thus enabling continued plant operation, albeit in a more limited fashion. Parsimonious and computationally efficient mixed-integer formulations are presented for specific discontinuous phenomena that arise in partial shutdown modeling, such as shutdown thresholds, induced shutdowns, discontinuous costs, and minimum shutdown durations. It is demonstrated that induced shutdowns (secondary shutdowns triggered by the original shutdown) can be correctly penalized in the objective by capturing the shutdown's true discontinuous economic cost. The computed optimal solution is implemented in closed-loop by employing a multitiered model predictive shutdown controller, in which a discrete-time mixed-integer dynamic optimization (MIDO) problem is embedded. Both objectives of maximizing economics and minimizing restoration (shutdown recovery) time are considered.     

Integrated production planning and scheduling using a decomposition framework

To ensure the consistency between planning and scheduling decisions, the integrated planning and scheduling problem should be addressed. Following the natural hierarchy of decision making, integrated planning and scheduling problem can be formulated as bilevel optimization problem with a single planning problem (upper level) and multiple scheduling subproblems (lower level). Equivalence between the proposed bilevel model and a single level formulation is proved considering the special structure of the problem. However, the resulting model is still computationally intractable because of the integrality restrictions and large size of the model. Thus a decomposition based solution algorithm is proposed in this paper. In the proposed method, the production feasibility requirement is modeled through penalty terms on the objective function of the scheduling subproblems, which is further proportional to the amount of unreachable production targets. To address the nonconvexity of the production cost function of the scheduling subproblems, a convex polyhedral underestimation of the production cost function is developed to improve the solution accuracy. The proposed decomposition framework is illustrated through examples which prove the effectiveness of the method.