换热网络综合的广义析取规划模型及求解方法

提出了针对通用换热网络综合(HENS)的广义析取规划(GDP)模型。广义析取规划模型通过引入逻辑变量代替0-1整形变量,使用析取式实现约束的分支选择。相比于传统MINLP模型,换热网络综合的广义析取模型具有以下优点:(1)条件建模的直观性;(2)通过对约束的动态选择,在减少冗余约束的同时更消去了不存在单元所带来的非线性费用计算模块,从而大大降低NLP子问题的求解复杂度,使求解更简单。另外,提出了一种混杂算法用来求解换热网络算例的广义析取规划模型。在外层使用启发式算法搜索最优结构,在内层调用确定型算法求解非线性规划子问题。结果表明,GDP模型无论在简洁性还是易求解性上都优于传统MINLP模型,可以有效地求解通用换热网络的综合问题。     

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。     

有并行设备的多目的间歇生产调度

针对有并行设备的多目的间歇生产调度问题,建立了以所有订单生产步骤排序和订单各阶段所选用分配规则为决策变量的调度优化模型,并提出了列队竞争算法求解该模型的有效个体表示方法及变异操作策略。实例计算表明:所提出方法的求解效率优于文献中所报道的方法,特别在求解有多台并行设备的调度问题时能得到比文献更好的结果,表现出其求解大规模复杂多目的调度问题的潜力。     

油轮到达时间不确定条件下的原油调度优化

原油调度是炼油企业生产的第一个环节,它直接影响后续生产过程的稳定性和经济性.文中采用连续时间建模方法.建立了油轮到达时间不确定条件下的原油从到港、卸载、储存、调合到进料全过程的随机规划机会约束调度优化模型,模型的优化目标是最小化给定调度时界内的总操作费用.采用直方图法对油轮迟到时间进行回归,得到油轮迟到时间的概率密度函数和分布函数,并引入置信水平,将模型中的不确定性约束转化为确定性约束,使得油轮到达时间不确定条件下的随机规划机会约束模型转变为可以求解的确定性混合整数非线性规划模型.针对原油调度模型的特点,采用广义Benders分解算法将原模型分解为两个混合整数线性规划问题和一个非线性规划问题进行迭代求解.避免了直接求解混合整数非线性规划问题的复杂性.最后,将建立的模型和算法应用于背景企业的原油调度过程,结果表明模型和算法都有良好的实用性.     

多级库存集成模型优化预测研究

以供应链多级库存为研究背景,建立以生产为中心的供应、生产和销售3个环节的多级库存集成化动态模型,并对模型进行仿真优化。结合训练神经网络的混合算法GA-BP算法,提出了基于遗传算法与人工神经网络相结合的优化预测模型。最后给出实例说明GA-BP算法优化预测模型的求解过程,验证了模型的可行性。     

基于特征选择的常减压装置模型及在计划优化中的应用

常减压装置将原油切割为不同中间产品,其作为炼油工艺的龙头装置,对炼油过程生产计划排产与效益提升至关重要。通过建立一种高精度且具有良好求解效率的常减压模型,用以求解模型关键指标实沸点（TBP）曲线,即综合考虑切割产品的实沸点（TBP）与原油TBP、流量、温度等变量影响,构建非线性方程组模型来表征输入输出间的关系;利用特征选择方法遴选相关变量（包括进料性质、相邻TBP及其二次项等）,并采用鲸鱼优化算法优化方程组系数。仿真结果表明,该多输出相互关系模型与已有文献工作相比,在兼顾求解效率基础上,常减压装置各蒸馏切割产品TBP曲线预测上有更高的精度,将此模型应用到炼厂计划优化中,与传统的悬摆切割模型对比,优化结果优于传统悬摆切割模型。     

基于信赖域算法的油田开发规划模型研究

根据油田开发的各种成本投资形式,可以建立多种油田开发的非线性模型,结合求解非线性规划的信赖域方法,以最低开采成本规划模型为例,提出一种油田开发规划模型的求解算法,为合理开发油田提供了定量依据,并运用MATLAB进行了算例分析,得出规划模型的最优参数。     

炼油企业全厂调度优化系统的设计与开发

生产调度优化对于炼油企业提高经济效益、增强市场响应速度有着重要的作用。本文设计并开发出一款图形化炼厂生产调度优化软件,采用MVC架构技术,实现了调度优化建模的图形化,可以方便组态炼油厂调度模型;将开源优化代码Coin-OR移植为调度优化软件的求解器,实现了求解器与图形化建模接口的连接;丰富的报表输出,让现场操作工可以各种形式获得调度优化结果;完善的内部模型数据管理和丰富的外部数据接口,包括原油性质指标库和原油评价数据库接口、成品油性质指标库等。仿真结果表明,该软件能够优化出符合炼厂生产实际的调度优化排产方案。     

动态规划模型及其在确定农作物最佳种植方案中的应用

文章从求解最优化模型的数学规划方法出发,引出了动态规划方法,介绍了动态规划与线性规划的联系、区别,大致给出了动态规划的表示方法,概括了动态规划问题的特性。接着文章以某地种植规划为例,介绍了动态规划模型在确定农作物最佳种植方案中的应用。最后总结了动态规划的应用特点,并提出结合线性规划能更好的实现最优化目的。     

换热网络合成问题的并行BB/SQP混合算法

换热网络合成问题通常可用非凸、非线性、不可微的混合整数非线性规划模型描述。基于GPU的并行计算技术为求解大规模模型提供了高效支撑。针对已有并行SQP算法求解换热网络合成问题中存在二元变量组合数过多、并行SQP算法求解结果严重依赖初值等问题,提出了BB/SQP混合并行算法。该算法采用BB算法代替枚举法,不但大大减少了模型求解中可能的二元变量组合,而且为SQP算法选出了可行的初值,从而提高了算法的求解质量。研究表明,所提出的混合并行算法能够有效求解换热网络合成问题,且并行计算相比串行计算的求解速度显著增加,加速比可达39。     

Modeling and Optimization of a Tunnel Crape Dryer

A mathematical model of a tunnel dryer for the dehydration of grapes is presented and applied to the determination of optimal operating conditions of the dryer. The dryer is of semi-batch structure, operating with trucks and trays. The cycle period is determined by meeting appropriate quality specifications for the final product. The nominal conditions were evaluated bv suitably minimizing. the total fuel demand, expressed as fuel consumption to production capacity, under some constraints regarding the production rate of the dryer and the maximum permissible air temperature. An nominal air humidity value was evaluated suggesting a minimum cycle period value for the production capacity and fuel demand. The nominal conditions required operation of the dryer on the maximum permissible air temperature. The optimum operation was evaluated by maximizing the total profit resulting from the operation of the dryer. The optimization variables were temperature and humidity of the drying air stream. A charteristic case study of industrial grape was included to illustrate the effectiveness of the proposed approach.     

Integrated safety stock management for multi-stage supply chains under production capacity constraints

In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher safety stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized.The complexities in safety stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with safety stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of safety stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on safety stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based safety stock management model is developed, in which the safety stock control variables (the target inventory levels used in production planning and scheduling models, base-stock levels for the base-stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the safety production capacity limits in safety stock management are properly represented.To demonstrate the effectiveness of the proposed safety stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3–8 warehouses supplying 10 final products to 30 sales regions.     

Modeling and Optimization of a Tunnel Crape Dryer

ABSTRACT

A mathematical model of a tunnel dryer for the dehydration of grapes is presented and applied to the determination of optimal operating conditions of the dryer. The dryer is of semi-batch structure, operating with trucks and trays. The cycle period is determined by meeting appropriate quality specifications for the final product. The nominal conditions were evaluated bv suitably minimizing. the total fuel demand, expressed as fuel consumption to production capacity, under some constraints regarding the production rate of the dryer and the maximum permissible air temperature. An nominal air humidity value was evaluated suggesting a minimum cycle period value for the production capacity and fuel demand. The nominal conditions required operation of the dryer on the maximum permissible air temperature. The optimum operation was evaluated by maximizing the total profit resulting from the operation of the dryer. The optimization variables were temperature and humidity of the drying air stream. A charteristic case study of industrial grape was included to illustrate the effectiveness of the proposed approach.     

Tactical capacity planning for semiconductor manufacturing: MILP models and scalable distributed parallel algorithms

A multiperiod stochastic mixed‐integer linear programming model is developed to address the tactical capacity planning of semiconductor manufacturing with considerations of complex routing of material flows, in‐process inventory, demand and capacity variability, multisite production, capacity utilization rate, and downside risk management. Both planning level decisions (i.e., capacity allocation and customer service level decisions) as well as operational level decisions (i.e., production, inventory, and shipment decisions) can be simultaneously determined based on the two proposed multiobjective optimization models. To address the huge number of scenarios needed to characterize the uncertainty and the large number of first‐stage integer variables in industrial scale applications, two novel scalable distributed parallel optimization algorithms are developed to mitigate the computational burden. The proposed mathematical models and algorithms are illustrated through two case studies from a major US semiconductor manufacturer. Results from these case studies provide key decision support for capacity expansion in semiconductor industry. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3930–3946, 2016     

Multisite supply planning for drug products under uncertainty

In the pharmaceutical industry, the goal of a supply planner is to make efficient capacity allocation decisions that ensure an uninterrupted supply of drug products to patients and to maintain product inventory levels close to the target stock. This task can be challenging due to the limited availability of manufacturing assets, uncertainties in product demand, fluctuations in production yields, and unplanned site downtimes. It is not uncommon to observe uneven distribution of product inventories with some products carrying excess inventories, while other products may be close to a stockout. Maintaining high stock levels can have economic repercussions due to the risk of expiration of unused products (whereas products facing a stockout can adversely affect the treatment regimen of patients). The network complexity of pharmaceutical supply-chains coupled with regulatory constraints and siloed planning systems force supply planners to rely on manual (error-prone) decision-making processes. Such an approach results in suboptimal capacity allocation and inventory management decisions. In this work, we propose a stochastic optimization methodology for the production scheduling of multiple drug products in lyophilization units across multiple sites. The framework leverages information obtained from historical and forecast data to generate scenarios of uncertain parameters (e.g., yield, demand, and downtimes) that can realize in the future. The optimization model determines a product filling schedule that maintains product stock levels close to targets under diverse scenarios. We show that this approach helps in avoiding reactive scheduling and in maintaining a more robust production plan than deterministic procedures (which ignore uncertainty). Specifically, planning under a stochastic optimization approach reduces the number of scenarios under which backlogs are observed and also reduces the magnitude of the backlogs.     

原油库存调度优化技术研究

根据原有库存实际生产需要运用库存优化理论,在重点考虑热耗因素下,以此作为目标函数,建立数学模型,对所建立的模型进行分析,利用分支界限法进行求解,对原油库存优化后结果明显好于优化前。优化后调度方案满足原油库实际生产需要,按照库存优化后计算,所损失的热量为4,05×10^9kJ,而在原油库存优化前实际的损耗热量为4．4×10^9kJ,两者之差为3．5×10^8kJ,按照原料高位发热值计算,每天节省下来1．5t燃料,起到了很好的经济效益,降低了企业成本。     

Stochastic inventory management for tactical process planning under uncertainties: MINLP models and algorithms

We address in this article the mid‐term planning of chemical complexes with integration of stochastic inventory management under supply and demand uncertainty. By using the guaranteed service approach to model time delays in the flows inside the network, we capture the stochastic nature of the supply and demand variations, and develop an equivalent deterministic optimization model to minimize the production, feedstock purchase, cycle inventory, and safety stock costs. The model determines the optimal purchases of the feedstocks, production levels of the processes, sales of final products, and safety stock levels of all the chemicals. We formulate the model as a mixed‐integer nonlinear program with a nonconvex objective function and nonconvex constraints. To solve the global optimization problem with modest computational times, we exploit some model properties and develop a tailored branch‐and‐refine algorithm based on successive piecewise linear approximation. Five industrial‐scale examples with up to 38 processes and 28 chemicals are presented to illustrate the application of the model and the performance of the proposed algorithm. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

带路径约束的聚烯烃牌号切换操作优化方法

产品多样化需求使得聚烯烃生产过程中经常需要进行牌号切换操作。以往关于牌号切换优化的研究大多只关心切换过程结束后聚合物质量指标是否达到目标牌号值,对过渡过程中质量指标及状态变量的波动情况缺少关注,而过程的波动会影响到最终产物的质量性质和操作平稳性。为此,本文以聚乙烯气相流化床反应器为对象,通过在牌号切换优化命题中加入关于熔融指数等的路径约束,防止过渡过程中的状态变量剧烈波动影响聚合物树脂质量。为求解此类带路径约束的动态优化问题,对常规的控制变量参数化方法进行了改进,通过求解微分代数方程(DAE方程)将路径约束转化为控制变量约束。仿真结果表明,加入路径约束可以有效避免牌号切换中变量的剧烈波动,增强过程平稳性。     

Hybrid simulation based optimization approach for supply chain management

In this work, we propose a hybrid simulation optimization approach that addresses the problem of supply chain management. We formulated the problem as a mathematical model which minimizes the summation of production cost, transportation cost, inventory holding and shortage costs, subject to capacity and inventory balance constraints and propose a hybrid approach combining mathematical programming and simulation model to solve this problem. The main objective of this approach is to overcome the computational complexity associated with solving the underlying large-scale mixed integer linear problem and to provide a better representation of supply chain reality. The simulation-based optimization strategy uses an agent-based system to model the supply chain network. Each entity in the supply chain is represented as an agent whose activity is described by a collection of behavioral rules. The overall system is coupled with an optimization algorithm that is designed to address planning and scheduling level decisions.     

流化床反应器中气相丙烯聚合反应的动力学和预测控制(英文)

A two-phase dynamic model, describing gas phase propylene polymerization in a fluidized bed reactor, was used to explore the dynamic behavior and process control of the polypropylene production rate and reactor temperature. The open loop analysis revealed the nonlinear behavior of the polypropylene fluidized bed reactor, jus- tifying the use of an advanced control algorithm for efficient control of the process variables. In this case, a central- ized model predictive control （MPC） technique was implemented to control the polypropylene production rate and reactor temperature by manipulating the catalyst feed rate and cooling water flow rate respectively. The corre- sponding MPC controller was able to track changes in the setpoint smoothly for the reactor temperature and pro- duction rate while the setpoint tracking of the conventional proportional-integral （PI） controller was oscillatory with overshoots and obvious interaction between the reactor temperature and production rate loops. The MPC was able to produce controller moves which not only were well within the specified input constraints for both control vari- ables, but also non-aggressive and sufficiently smooth for practical implementations. Furthermore, the closed loop dynamic simulations indicated that the speed of rejecting the process disturbances for the MPC controller were also acceotable for both controlled variables.