基于带补偿随机规划的蒸汽动力系统优化设计

在蒸汽动力系统优化设计中,考虑不确定因素的优化策略能避免基于确定性设计策略的保守设计,并能针对不确定因素的实现提出相应的调度调节策略.本研究分析了蒸汽动力系统设计包含的不确定因素的特性及其对蒸汽动力系统优化目标和约束条件的影响.不确定因素的表达分成两类:基于时间变化表达和基于发生概率表达.对基于时间变化表达的因素,转化为多周期问题进行处理;对外部工艺过程变化引起的汽电需求不确定波动等基于发生概率表达的因素,应用随机规划策略,补偿不确定参数的实现可能引起的约束背离.基于本研究建立的多周期带补偿的二阶段随机规划MILP模型,求解蒸汽动力系统结构,同时优化调度调节策略,用调节决策和惩罚不足应对汽电需求等不确定因素的实现,实现系统安全稳定运行和经济效益最优.     

基子Benders分解的不确定条件下过程优化研究

基于Benders分解的二阶段随机规划在不确定条件过程优化中占有重要的地位,分析和比较了前人的工作,并将蒙特卡罗积分策略与基于对偶理论的可行域限定条件相结合,提出了新的求解策略,不仅避免了求解一系列子问题进行可行域限定的情况以及计算负荷随不确定参数数目呈指数增加的不足,而且使可行域限定条件更加合理,算例证明了该算法的有效性.     

基于分解算法的功热交换网络多目标优化

功热网络设计问题指在流程设计中对变压和换热过程进行耦合优化设计的问题,以此来提高整体系统的能效并降低成本。前人工作中一般采用数学规划法对功热网络建模优化。然而,由于存在变压过程和换热器面积计算的非线性约束,以及换热匹配的二元变量,整体模型往往是一个高度非凸的混合整数非线性规划模型,难以求解。本文提出一种高效的功热网络优化方法。模型中分别用透平压缩机和换热器实现功热网络中轴功和热的交换。求解过程采用分解算法,主问题中用随机算法对关键变量优化,功和热两个子网络问题中用确定性算法求解。目标函数考虑了经济和环境影响。案例测试对比了不同优化目标得到的结果以及多目标Pareto曲线,验证了所提出方法的高效性。     

石化企业蒸汽动力系统优化设计建模及应用

在石化企业蒸汽动力系统(SPS)热力学分析与数学规划集成优化策略的指导下,建立了多周期设计与运行协同优化的混合整数线性规划模型(MILP)。通过热力学效率、电热供应比2个指标筛选组合子系统,采用设计负荷离散化的方法确定SPS初始超结构,大大降低了优化设计问题模型的规模和复杂度。案例分析部分采用提出的优化设计方法和建模求解策略,得到了最优设计和运行方案,同传统设计方法相比,模型求解时间大大降低,方案可操作性大大提高,证实了文中提出的优化设计策略、筛选规则和优化设计模型的可行性和有效性。     

基于控制器切换的模糊不确定网络化系统的稳定性

基于T-S模糊模型建立模糊网络化控制系统模型,研究了一类存在不确定参数的网络时滞系统在单包传输且没有丢包情况下的稳定性.为了增强其诱导时延的动态性能,假设存在有限个备选的控制增益己知的模糊状态反馈控制器,在每个备选的控制器均不能镇定系统的情况下,使用控制器切换技术及Lyapunov函数方法,设计切换律,得到了系统渐进稳定的一个充分条件,并且此条件可转化为求解线性矩阵不等式(LMIS)问题.最后仿真结果表明所设计策略的可行性和有效性.     

多流股换热器网络综合方法在原油预热系统中的应用

以原油常减压蒸馏装置原油预热网络为研究对象,进行了多流股换热器网络的实例综合. 基于超结构物理模型建立了改进的多流股换热器网络综合数学模型,提出将该网络综合问题由混合整数非线性规划问题转化为简单的非线性规划问题的求解策略,并利用改进的遗传/模拟退火新算法进行了原油预热网络的综合. 与Hextran软件的综合结果以及现场换热网络的对比表明,本模型和求解策略可以应用于工业规模的多流股换热器网络综合,有可能取得较好的经济效益.     

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。     

基于风险管理的石化工业供应链网络设计

提出了一种带有风险约束的石化工业供应链设计方法,能够应对需求不确定性带来的影响。供应链网络设计问题包括仓储和分销中心的布局结构及容量安排,以及各级之间的物流连接关系。首先建立了不确定条件下的供应链设计模型,并在此基础上加入了风险约束,最后将多目标优化问题转化为单目标优化问题,建立了确定性的两阶段随机规划模型。以某个化工集团供应链设计为例,分析了风险管理在建立供应链中的重要性。     

化工过程多目标优化适宜解的模糊确定

应用模糊优化原理求解化工多目标优化问题.基于相对优属度的多目标模糊优化法适合复杂系统的求解,而且在过程中充分运用了空间图形直现的特性,达到了问题求解的过程简约化.本文作者以合成氨凝液回收系统的多目标优化为例,说明化工过程多目标优化适宜解的模糊确定.     

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。     

Synthesis of robust water reuse networks for single-component retrofit problems using symmetric fuzzy linear programming

Water integration techniques can be used to minimize the utility water consumption and effluent generation of process plants through the implementation of reuse or recycle networks. There are a number of graphical and mathematical programming techniques available for the synthesis of such water reuse networks. However, effective use of these methods requires the availability of reliable process data, which in reality might be difficult to acquire. This paper describes a procedure for the synthesis of robust water reuse networks from imprecise data using symmetric fuzzy linear programming (SFLP). Two model variants, one based on mass exchange units and the other on source/sink allocation, are presented. Each variant is illustrated with a numerical example.     

Optimal water use and treatment allocation

Water consumption and emission can be decreased by optimal allocation of water re-use, regeneration and re-use. regeneration and recycle in water consuming mass exchange networks and by applying optimally distributed effluent treatment processes in case of several emission sources of different contamination levels. Several example problems taken from the literature have been reproduced and solved by GAMS/MINOS/CONOPT package. In most cases our optimal solution is identical to those of others; in some other cases, however, we found better solutions. We also found our method more flexible than those based on targeting and conceptual design. Some example problems involve constraints and/or specifications of those kinds the referred methods cannot deal with. Namely, the constant mass load specification is substituted by either mass load proportional to the water flow rate (involving constant concentration shift) or some other, even more realistic, non-linear functions. The optimal systems have different structures according to the applied mass load relations.     

换热网络全局优化的等平面切割方法的实现

换热网络全局优化属于混合整数非线性规划范畴,其全局优化难点是局部最小解遍布全局,求解过程中很容易陷入局部最小解,而不能获得全局最优解.文章针对换热网络全局最优化的难点,运用一种全新的全局最优化方法--等平面切割的方法,根据目标函数代表的性能进行等平面切割,反复交替地操作不断缩小全局最优解的区间;同时,基于切割平面法分析...     

Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances

Traditionally the design of supply chains has been based on economic objectives. However, as societal environment concerns grows, environmental aspects are also emerging at academic and industry levels as decisive factors within the supply chain management context. The investment towards logistics structures that considers both economic and environmental performances is nowadays an important and current research topic.This paper addresses the planning and design of supply chain structures for annual profit maximization, while considering environmental aspects. The latter are accounted for through the Eco-indicator methodology. Profit and environmental impacts are balanced using an optimization approach adapted from symmetric fuzzy linear programming (SFLP), while the supply chain is modelled as a mixed integer linear programming (MILP) optimization problem using the Resource-Task-Network (RTN) methodology. The obtained model applicability is validated through the solution of a set of supply chain problems.     

Nonlinear robust optimization for process design

A novel robust optimization framework is proposed to address general nonlinear problems in process design. Local linearization is taken with respect to the uncertain parameters around multiple realizations of the uncertainty, and an iterative algorithm is implemented to solve the problem. Furthermore, the proposed methodology can handle different categories of problems according to the complexity of the problems. First, inequality‐only constrained optimization problem as studied in most existing robust optimization methods can be addressed. Second, the proposed framework can deal with problems with equality constraint associated with uncertain parameters. In the final case, we investigate problems with operation variables which can be adjusted according to the realizations of uncertainty. A local affinely adjustable decision rule is adopted for the operation variables (i.e., an affine function of the uncertain parameter). Different applications corresponding to different classes of problems are used to demonstrate the effectiveness of the proposed nonlinear robust optimization framework. © 2017 American Institute of Chemical Engineers AIChE J, 64: 481–494, 2018     

A systematic framework for enterprise-wide optimization: Synthesis and design of processing networks under uncertainty

In this paper, a systematic framework for synthesis and design of processing networks under uncertainty is presented. Through the framework, an enterprise-wide optimization problem is formulated and solved under uncertain conditions, to identify the network (composed of raw materials, process technologies and product portfolio) which is feasible and have optimal performances over the entire uncertainty domain. Through the integration of different methods, tools, algorithms and databases, the framework guides the user in dealing with the mathematical complexity of the problems, allowing efficient formulation and solution of large and complex enterprise-wide optimization problem. Tools for the analysis of the uncertainty, of its consequences on the decision-making process and for the identification of strategies to mitigate its impact on network performances are integrated in the framework. A decomposition-based approach is employed to deal with the added complexity of the optimization under uncertainty. A network benchmarking problem is proposed as a benchmark for further development of methods, tools and solution approaches. To highlight the features of the framework, a large industrial case study dealing with soybean processing is formulated and solved.     

Interval based MINLP superstructure synthesis of mass exchange networks

A new simultaneous synthesis approach for mass exchange networks (MENs) is presented. The technique is adapted from the interval based mixed integer non-linear program (MINLP) superstructure (IBMS) synthesis of heat exchanger networks (HENs). The superstructure interval boundary compositions are defined by the supply and target compositions of either the rich or lean set of streams. Each rich and lean stream in the superstructure has the potential of exchanging mass with streams of opposite kind in each interval based on mass transfer feasibility. The model harnesses the strengths of the stagewise superstructure and the pinch technology methods for mass exchange network synthesis (MENS). The IBMS approach simultaneously trades-off the capital and operating costs for MENs. The superstructure composition interval defining approach introduced in this paper enforces the mixing of split streams at equal compositions, hence there is no need to include mixing equations in the model. Fixing the interval boundaries helps to eliminate the complexities involved in initialisations, thus the region of search for the optimum solution is reduced. The IBMS model is applied to MENS problems involving continuous contact and staged columns. It is also extended to problems involving multiple mass separating agents (MSAs) and regeneration. The results obtained compare well with those in the literature.     

Mathematical programming approaches to the synthesis of chemical process systems

This paper presents a review of advances that have taken place in the mathematical programming approach to process design and synthesis. A review is first presented on the algorithms that are available for solving MINLP problems, and its most recent variant, Generalized Disjunctive Programming models. The formulation of superstructures, models and solution strategies is also discussed for the effective solution of the corresponding optimization problems. The rest of the paper is devoted to reviewing recent mathematical programming models for the synthesis of reactor networks, distillation sequences, heat exchanger networks, mass exchanger networks, utility plants, and total flowsheets. As will be seen from this review, the progress that has been achieved in this area over the last decade is very significant.     

Unified targeting algorithm for diverse process integration problems of resource conservation networks

A single algorithm is developed to establish minimum resource targets for diverse process integration problems including those of heat/mass exchange, water, hydrogen, carbon emission and material reuse networks. Previous algorithms such as the problem table algorithm for heat exchange networks and the composite table algorithm for resource allocation networks are special cases of the newly proposed unified targeting algorithm (UTA). The conversion of streams to equivalent inlet-outlet (demand-source) pairs is shown to be a key basis for the unified approach. The tabular data from the UTA may be plotted to obtain the grand composite curve (GCC) or the limiting composite curve (LCC). These provide graphical representations of the net load deficit/surplus at various levels for resource targeting and pinch identification. For allocation networks with system loss/gain, the UTA with increasing level sort order yields the Deficit LCC to target the minimum resource, whereas the UTA with decreasing level sort order provides the Surplus LCC to target the minimum waste/excess. A single UTA calculation along with the use of fundamental overall system balance equations is sufficient to establish complete targets for a problem. Six practical case studies from diverse domains are presented to illustrate the detailed steps of the UTA.     

Flexible mass transfer model for water minimization in batch plants

This paper presents the least constrained mass transfer mathematical formulation for freshwater minimization in multipurpose batch chemical processes with central reusable water storage. The mathematical formulation is an extension of the model developed by Majozi [T. Majozi, Wastewater minimization using central reusable water storage in batch processes, Computers and Chemical Engineering Journal 29 (7) (2005) 1631–1646]. In the latter model four scenarios were considered with various limitations or constraints. In the scenario presented in this paper only the mass load is fixed, whilst both the quantity of water used in a particular operation and outlet concentration are allowed to vary. In essence, fixing the mass load is more representative of the practical case. A solution procedure for the resultant nonconvex mixed integer nonlinear programming (MINLP) model is also presented. The solution procedure first involves reformulating the MINLP into a relaxed linear model (MILP). The MILP is first solved, the solution of which forms a feasible starting solution for the MINLP. Presented are two illustrative examples.