石化企业蒸汽动力系统优化设计策略研究

针对石化企业蒸汽动力系统(SPS)设计和综合方面存在的超结构复杂、模型求解困难的问题,提出了热力学分析与数学规划法相结合的系统设计策略。该策略通过将整个SPS看成是能够独立供应蒸汽或动力的子系统的组合,对各种可能组合子系统进行分析,综合电热供应比和供能效率2个指标剔除明显不合理的子系统,初步确定优化的候选子系统,然后对候选子系统设备设计负荷进行离散化,得到候选设备的数量和设计负荷。通过子系统筛选和候选设备数量和尺寸离散化,既避免了凭经验确定初始超结构遗漏最优解的不足,又大大降低了系统的超结构规模和优化设计的混合整数线性规划模型(MILP模型)的复杂度,保证了SPS多周期优化设计方案的最优性和可操作性。     

考虑不确定汽电需求的蒸汽动力系统优化设计

蒸汽动力系统优化设计不仅要保证系统全周期总费用最小,还要保证系统具有一定可操作性应对各种不确定变工况影响。本文提出了同时考虑汽电需求确定性多周期变化以及燃料、电力价格,汽电需求不确定性变化的蒸汽动力系统优化设计策略,专门提出不确定变化参数对优化目标及约束的影响以及数学表达,建立混合整数线性规划(MILP)优化模型进行求解,并应用于某石化企业蒸汽动力系统优化设计。实例设计结果表明,与按照传统策略设计结果相比,应用本文提出的考虑不确定参数的设计策略获得的优化方案,具体设计了针对不确定因素实现而对应的运行调度安排,降低年总费用,保证系统安全稳定运行,实现经济性和可操作性综合最优。     

厂际氢气网络多周期集成的分步优化方法

厂际氢气系统集成对于化工园区合理配置氢气系统和氢气资源具有重要意义。针对化工园区中多厂氢气网络多周期优化设计的问题,提出了一种三步求解策略优化设计多厂氢气网络的多周期优化设计。该方法首先采用厂际氢气网络的单周期优化模型获取各子周期下的氢气公用工程的传输量,然后采用单厂氢气网络的多周期优化设计模型获得各厂内的氢气系统结构,最后采用厂际氢气系统的多周期优化设计模型确定化工园区中厂际氢气系统的网络结构和氢气调度方案。研究表明,所提出的多厂氢气网络多周期优化设计方法可有效解决厂际氢气网络的优化设计问题,该策略在不增加氢气系统结构复杂度的前提下,可以获得较好的经济性,并可提高优化模型求解的计算效率。     

蒸汽动力系统设计与多周期运行同步优化

蒸汽动力系统(Steam power system,SPS)是炼油企业的重要组成部分,针对炼油企业蒸汽动力系统设计与运行方案上存在的不合理方面,通过对蒸汽动力系统建立混合整数线性优化模型(MILP),改善部分系统结构、调整系统多周期运行方案,从而降低蒸汽动力系统的运行费用实现系统的优化。以某炼油企业蒸汽动力系统实为背景,对企业蒸汽动力系统的设计与多周期生产调度方案进行优化,并验证优化策略的有效性。     

石化企业蒸汽动力系统柔性设计

蒸汽动力系统的设计工作是一项复杂的、系统的工程,除了力求实现全周期内总费用最小的经济性目标外,还要保证实际运行过程中的各种工况下的操作柔性。针对石化企业蒸汽动力系统变工况的特点,提出了蒸汽动力系统柔性优化设计策略;将确定性变化需求转化成多周期问题,将不确定性变化需求转化成虚拟的多工况问题,并对连续不确定性和离散不确定性变化分别处理;以经济性和可操作性的最优综合为目标,建立蒸汽动力系统柔性优化设计的大型混合整数线性（MILP）模型,并采用广义Benders分解算法进行求解。采用本文提出的柔性优化设计模型和求解方法,对某炼油厂蒸汽动力系统进行了柔性优化设计,并与传统设计方案和经济性最优的设计方案进行对比。与经济性最优的方案对比结果表明,该优化设计方案总费用增加不多,但实现了在各种变化工况下的安全、稳定、柔性操作;而与传统设计方案对比结果表明,不但提高了各种工况下的操作柔性,而且节省了大量的投资和运行费用,即柔性设计方案具备可操作性和经济性的综合最优性。从而证明了本文提出的柔性设计建模和求解方法的合理性和实用性。     

CSTR工艺与控制系统的集成优化设计方法

与传统的序贯设计方法相比,过程工艺与控制系统的集成优化设计可较大地提高过程的可操作性和经济性能,但基于常规动态优化的集成设计问题求解困难。采用最优控制和分层优化策略的思想,将线性二次调节器(LQR)嵌入到过程工艺与控制系统集成设计框架中,以降低集成优化设计问题的求解难度。将该方法应用于连续搅拌釜式反应器(CSTR)的设计中,通过与传统的序贯设计方法相比较,表明了该方法的有效性。     

炼油厂氢气网络柔性优化

随着炼油厂对氢气的需求量不断扩大,氢气系统已成为炼油厂的重要组成部分。炼油厂氢气网络的优化设计既要实现总成本最小的经济性目标,还要保证各种工况下氢气系统的安全稳定运行。针对炼油厂氢气系统不断变化的工况和氢气需求,提出了炼油厂氢气网络柔性优化策略,建立了多工况下的氢气网络柔性优化混合整数非线性规划(MINLP)模型,并对优化模型进行了线性化处理,采用优化建模工具Lingo建模求解。采用本文提出的柔性优化策略对某炼油厂氢气网络进行了柔性优化设计,与炼油厂的原始氢气网络对比表明,柔性优化后的网络可操作性强且具有较好的柔性调节能力,既节省了大量的运行成本,又为各种工况下氢气系统的安全稳定运行提供了保障,对实际炼油厂氢气网络的优化管理起了指导作用。     

基于矩阵编码的遗传算法多杂质间歇用水网络优化

针对多杂质间歇用水网络优化设计问题,建立了以新鲜水用量最小为目标的混合整数非线性规划(MINLP)模型,并提出基于矩阵编码的遗传算法和信赖域序列二次规划(trust region sequential quadratic programming,TRSQP)法相结合的求解策略。本文采用矩阵编码方法为时间条件约束提供了方便,从而使用水单元之间水回用关系更加清晰明确;采用基于矩阵编码的遗传算法对整数变量进行优化,采用TRSQP法对连续变量进行优化,集成应用两种方法进行优化求解,从而获得MINLP模型的最优解;并借助MATLAB软件进行编程并实现。利用本文所提出的求解方法和策略对文献中2个典型案例进行求解,求解结果均优于文献数据。实例计算表明,本文所提求解方法是可行的。     

控制与工艺集成优化设计研究进展

控制与工艺集成优化设计是一种系统化的过程优化设计方法。以典型二元精馏塔设计为例阐述了其过程系统的数学描述，给出其最优工艺设计和控制设计的求解框架和求解策略，以及其中混合整数动态优化问题(MIDO)的求解方法。通过与传统的工艺与控制分步序贯设计比较，讨论了二者各自的优势和不足，指出控制与工艺集成优化设计具有良好的发展前景。     

乙炔加氢反应过程混合建模与优化

针对传统单一建模方法所构建的乙炔加氢反应器数学模型存在预测性能无法满足工业实际应用需求的问题,提出了一种机理与神经网络嵌套的建模方法,充分利用机理模型包含的能质约束信息降低神经网络模型的约束违反度,得到了能够良好描述实际工业乙炔加氢反应过程特性的混合模型。基于反应器混合模型,研究了以运行效益为目标函数的优化问题。主要决策变量包括:一段反应器进料中氢气与乙炔的摩尔比(RH/A)、进料温度和反应器运行周期等几个关键参数。针对反应器长期运行后,催化剂活性降低造成的处理能力下降的问题,提出了反应温度补偿机制和RH/A并行调节的运行优化策略,并采用序列法对反应器运行周期进行离散化处理。通过引入差异化变异策略、潜在解替代策略对两阶段差分算法进行改进,采用增量式编码法结合改进两阶段差分算法,对优化问题进行求解。结果证实了优化策略与改进算法的有效性,并据此确定了反应器最佳运行方案。     

Stochastic optimization based on a novel scenario generation method for midstream and downstream petrochemical supply chain

A two-stage mixed integer linear programming model (MILP) incorporating a novel method of stochastic scenario generation was proposed in order to optimize the economic performance of the synergistic combination of midstream and downstream petrochemical supply chain. The uncertainty nature of the problem intrigued the parameter estimation, which was conducted through discretizing the assumed probability distribution of the stochastic parameters. The modeling framework was adapted into a real-world scale of petrochemical enterprise and fed into optimization computations. Comparisons between the deterministic model and stochastic model were discussed, and the influences of the cost components on the overall profit were analyzed. The computational results demonstrated the rationality of using reasonable numbers of scenarios to approximate the stochastic optimization problem.     

Oil spill response planning with consideration of physicochemical evolution of the oil slick: A multiobjective optimization approach

This paper addresses the optimal planning of oil spill response operations under economic and responsive criteria, with consideration of oil weathering process. The economic criterion is measured by total cost, while the measure of responsiveness is the time span of the entire response operations. A bi-criterion, multiperiod mixed-integer linear programming (MILP) model is developed that simultaneously predicts the optimal time trajectories of oil volume and slick area, transportation profile, response resource utilization levels, cleanup schedule, and coastal protection plan. The MILP model integrates with the prediction of an oil weathering model that accounts for oil physicochemical properties, spilled amount, hydrodynamics, and weather conditions. The multi-objective optimization model is solved with the epsilon-constraint method and produces a Pareto optimal curve that reveals how the optimal total cost and response operations change under different specifications of responsiveness. We present two illustrative examples for oil spill incidents in the Gulf of Mexico and New England.     

Inventory pinch algorithm for gasoline blend planning

Current gasoline blend scheduling practice is to optimize blend plans via fixed duration (e.g., days) multiperiod NLP or MINLP models and schedule blends via interactive simulation. Solutions of multiperiod models typically have different blend recipes for each time period. We introduce inventory pinch points and use them to construct an algorithm based on single‐period nonlinear model to minimize the number of different blend recipes. The algorithm optimizes multigrade blend recipes for each period delimited by the inventory pinch points and then uses a fine‐grid multiperiod fixed‐recipe MILP to compute blend volumes profile. If MILP is infeasible, a corresponding period between the pinch points is subdivided and recipes are reoptimized. In our case studies, solutions are computed in significant less time and are most often within 0.01% of the solutions by multiperiod MINLP. Reduced number of blend recipes makes it easier for the blend scheduler to create a schedule by interactive simulation. © 2013 American Institute of Chemical Engineers AIChE J, 59: 3748–3766, 2013     

基于分片线性逼近的聚氯乙烯生产计划优化分解算法

聚氯乙烯全流程生产过程计划优化往往描述为复杂MINLP模型,求解难度非常大,为此引入分片线性技术逼近实际生产中的非线性特征,建立基于HH的MILP模型,进一步提出一种基于离线层级模型的分解算法来加速求解过程：第一层在对生产设备以最优能耗点进行层级划分得到离线层级模型的基础上优化一个等价MILP问题,确定表征设备操作状态的二值变量;第二层以HH模型为基础,在二值变量确定的情况下,代入计划优化模型调整设备的工作点,最终确定模型的最优操作决策方案。最后,以一个实际工厂规模的案例来验证模型和算法的有效性,结果表明本算法在基本不损失优化结果性能的前提下可以大大提高求解效率,缩短求解时间达99%以上。     

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.     

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     

An MILP-MINLP decomposition method for the global optimization of a source based model of the multiperiod blending problem

The multiperiod blending problem involves binary variables and bilinear terms, yielding a nonconvex MINLP. In this work we present two major contributions for the global solution of the problem. The first one is an alternative formulation of the problem. This formulation makes use of redundant constraints that improve the MILP relaxation of the MINLP. The second contribution is an algorithm that decomposes the MINLP model into two levels. The first level, or master problem, is an MILP relaxation of the original MINLP. The second level, or subproblem, is a smaller MINLP in which some of the binary variables of the original problem are fixed. The results show that the new formulation can be solved faster than alternative models, and that the decomposition method can solve the problems faster than state of the art general purpose solvers.     

换热网络的新设计方法──三温差法的应用研究

在Ｇｒａｓｅｍａｎｎ新近提出的以双温差法为基础的垂直ＭＩＬＰ转运模型基础上，建立了三温差法的ＭＩＬＰ转运模型，并编制了相应的软件用于换热网络系统设计。还对实际中涉及到的流股相变问题作了研究，并提出相应的设计方法。     

Multiperiod design of azeotropic separation systems. I. An agent based solution

In this paper, the first in a two-part series, we explore an agent based solution approach to the multiperiod design of azeotropic separation systems. Asynchronous Teams (A-Teams), a software organization that has been successfully applied to combinatorial optimization problems in other disciplines, is applied to this chemical process system design problem. A-Teams is a framework in which multiple design techniques can work harmoniously in the solution of a problem too demanding for any one method. We introduce a simple architecture for constructing A-Teams that incorporates the use of persistent data. Several approaches for generating improved multiperiod azeotropic design solutions are developed and are used simultaneously in a large-scale demonstration. The software organization is found to be capable of developing and improving a population of preliminary designs for the multiperiod azeotropic design problem.