HTR-PM大范围变负荷的MA自适应优化算法

为应对电网负荷需求的变化,球床模块式高温气冷堆核电站示范工程(HTR-PM)在设计上具备大范围变负荷运行以满足电网负荷的能力。由于无法获得大范围变负荷精确适用的模型,使得基于模型的操作优化面临挑战。为处理模型与对象的失配,提出HTR-PM大范围变负荷的MA(modifier adaptation)自适应优化算法。MA自适应优化算法利用过程反馈信息修正优化模型,促进优化模型与对象优化命题的一致性,进而有助于基于模型的操作优化收敛至对象的最优操作。借助信赖域框架,MA自适应优化算法可基于模型评价自适应更新模型、调整修正的优化模型的应用范围,确保在合适操作空间内求解优化模型。而且,信赖域框架还降低了算法性能对算法参数的敏感性。MA自适应优化算法在HTR-PM双堆同步大范围变负荷中的应用验证了方法的有效性。     

300MW机组基于煤质变化的协调控制系统优化

对于300MW机组负荷-压力被控对象而言,燃料侧存在较大惯性和迟延,而调门侧对负荷和压力的控制都很迅速。采用优化自动控制策略、自适应在线参数调整等方法可以优化煤质变化引发的相应问题。提高了电厂变煤种工况下的自动控制系统调节品质,提高了响应电网负荷自动控制(AGC)指令的调节特性。     

基于信赖域算法的精馏塔优化

从结构优化角度建立精馏塔优化的混合整数非线性规划(MINLP)模型,为了消除整数变量,引入绕流效率将MINLP问题转化为非线性规划(NLP)问题。针对得到的NLP问题提出一种优化方法,在该方法中采用结构优化中常用的信赖域优化算法进行求解,并应用虚拟瞬态连续性方程辅助优化中的稳态模拟。采用提出的优化方法对3个精馏系统进行设计优化,以不同初始值开始,均可得到令人满意的优化结果,表明所提优化方法具有良好的稳健性,对于较复杂的部分热耦合精馏过程仍然可以有效优化求解;信赖域算法在精馏塔优化中也表现出良好的收敛性。     

基于自适应逆的温度控制系统

介绍了自适应逆控制方案,采用变步长LMS自适应滤波算法,用该算法建立对象的模型、对象逆模型,并设计了自适应逆控制器。将这种控制方案应用到含有大惯性、纯滞后的系统。仿真结果表明,此种方案鲁棒性及动态性能较好。     

一种自适应差分进化算法及其在精对苯二甲酸结晶生产过程优化中的应用

提出了一种自适应差分进化算法(adaptive differential evolutionary algorithm,ADE),并将其应用于精对苯二甲酸(PTA)生产中结晶过程的优化.由于该结晶过程为5级结晶器串联组成,并且存在较多杂质等的影响,使得该结晶过程很难用机理数学模型来精确表达.为此首先利用神经网络技术建立了该对象模型,并对各主要操作变量对关键产品指标的影响进行系统分析,以满足结晶机理先验知识.然后利用自适应差分进化算法对该结晶模型进行操作点的寻优.该ADE算法是一种基于实型数值编码的并行直接搜索算法.     

基于减法聚类的焦炉集气管压力操作模式提取及迁移重构

基于集气管压力控制特点和生产过程中产生的大量历史数据和以往操作经验的基础上，提出一种基于减法聚类的焦炉集气管压力控制的操作模式提取及迁移重构方法。基于减法聚类算法进行操作模式的提取，最终形成操作模式优化数据库，对压力设定值进行优化，采用基于模型迁移思想的模式重构策略实现操作模式的修正。工业应用试验验证了所提方法的有效性。     

基于弹性分析的空分变负荷操作优化

针对空分装置变负荷过程的优化控制问题,提出用弹性分析对不同控制精度下的操作变量进行优化的方法.建立等价超结构换热网络模型用以描述多股流换热器,进而建立可用于操作分析的空分装置机理模型以准确描述空分变负荷过程.引入弹性指数表示关键操作变量的控制范围和精度,用柱形代数分解方法求解关键操作变量弹性边界的显式表达式,将复杂多变...     

基于知识管理的精馏塔智能化控制模型

为解决多变操作环境下精馏塔在线控制调优问题,分析了精馏塔传统的控制策略与缺陷,提出精馏塔基于知识管理的智能化控制框架.框架引入知识管理体系结构,解决了模型构建、表述、引用等更适应于工程化应用的信息化处理技术,采用单神经元自适应控制策略,确定不同条件下动态优化的馏出物组成,进而确定各控制点的控制参数.并研究精馏塔知识管理的模型表达方式,在面向对象建模方法基础上,采用面向智能体的建模方法,建立了客体(对象)类、主动实体(智能体)类和最优化模型类,通过知识推理将三个类关联形成精馏塔管控一体化的知识化智能平台,其中知识推理采用了基于人工神经网络的数据挖掘技术.实际应用证明该体系结构是可行的.     

一种参数辨识与模型匹配相结合的自适应控制

讨论了对象参数辨识与模型匹配控制(MMC)相结合的自适应控制方法。该方法是基于RIV 在线参数辨识求取时变对象参数,进而利用 MMC 方法不断修正反馈与前馈增益,使对象得到与预选模型相匹配的动态响应。实验室一模拟对象与一实际工业对象的数字仿真结果说明了该方法的可行性。该算法能被改进为一种实时在线的计算机控制策略而加以实施。     

基于综合关联度的注塑成型工艺参数多目标优化研究

针对注射成型过程中的两个品质指标(翘曲量和收缩率),提出了基于综合关联度、Kriging模型及自适应遗传-蚁群算法的注射成型工艺参数优化方法。首先给出了综合关联度的定义及计算方法,然后给出了Kriging模型的拟合方法和自适应遗传-蚁群的优化算法。在结合带凸沿杯子的注射成型具体实例研究中,首先给出了注射成型工艺参数的范围,其次给出了基于正交实验的设计变量确定方法,然后基于超拉丁立方实验及Kriging方法进行模型近似,最后利用自适应的遗传-蚁群算法进行优化求解。模流分析及实际注塑实验表明,注射成型工艺参数优化的结果可靠,具有较好的实际意义。     

基于鲸鱼优化算法的汽轮机热耗率模型预测

为了准确地建立汽轮机热耗率预测模型,提出了一种基于反向学习自适应的鲸鱼优化算法（AWOA）和快速学习网（FLN）综合建模的方法。首先将改进后的鲸鱼算法与经典改进的粒子群、差分进化算法和基本鲸鱼算法进行比较,结果证明其具有更高的收敛精度和更快的收敛速度;然后采用某热电厂600 MW超临界汽轮机组现场收集的运行数据建立汽轮机热耗率预测模型,并将改进后的鲸鱼算法优化的快速学习网模型的预测结果与基本快速学习网及经典改进的粒子群、差分进化算法和基本鲸鱼算法优化的快速学习网模型预测结果相比较。结果表明,AWOA-FLN预测模型具有更高的预测精度和更强的泛化能力,更能准确地预测汽轮机的热耗率。     

变负荷工况下NOx排放量预测控制

NO_x是火电厂排放的主要污染物之一,降低NO_x的排放是火电厂面临的主要问题。针对火电厂变负荷工况下的NO_x排放量最小化问题,本文提出了一种基于最小二乘支持向量机（LSSVM）的非线性模型预测控制算法。根据电站锅炉实际历史数据建立锅炉负荷预测模型和NO_x排放预测模型,并以交叉验证的方法优化模型参数,从而获得高精度模型。在此基础上以NO_x的排放量最小为优化目标,考虑锅炉负荷约束,构建锅炉燃烧优化模型。采用差分进化算法求解优化模型得到控制参数的最优设定值。为了验证本文提出算法的有效性,采用实际生产数据进行实验。实验结果表明本方法能够在变负荷工况下有效降低NO_x排放量,在不增加电厂改造成本上,为电厂提供了有效的控制手段,具有一定应用前景。     

Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty

A novel data‐driven adaptive robust optimization framework that leverages big data in process industries is proposed. A Bayesian nonparametric model—the Dirichlet process mixture model—is adopted and combined with a variational inference algorithm to extract the information embedded within uncertainty data. Further a data‐driven approach for defining uncertainty set is proposed. This machine‐learning model is seamlessly integrated with adaptive robust optimization approach through a novel four‐level optimization framework. This framework explicitly accounts for the correlation, asymmetry and multimode of uncertainty data, so it generates less conservative solutions. Additionally, the proposed framework is robust not only to parameter variations, but also to anomalous measurements. Because the resulting multilevel optimization problem cannot be solved directly by any off‐the‐shelf solvers, an efficient column‐and‐constraint generation algorithm is proposed to address the computational challenge. Two industrial applications on batch process scheduling and on process network planning are presented to demonstrate the advantages of the proposed modeling framework and effectiveness of the solution algorithm. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3790–3817, 2017     

Probabilistic Multiobjective Operation Management of MicroGrids With Hydrogen Storage and Polymer Exchange Fuel Cell Power Plants

This paper models and solves the operation management problem of MicroGrids (MGs) including cost and emissions minimization under uncertain environment. The proposed model emphasizes on fuel cells (FCs) as a prime mover of combined heat and power (CHP) systems. An electro‐chemical model of the proton exchange membrane fuel cell (PEMFC) is used and linked to the daily operating cost and emissions of the MGs. A reformer is considered to produce hydrogen for PEMFCs. Moreover, in high thermal load intervals, in order to make the MG more efficient, a part of produced hydrogen is stored in a hydrogen tank. The stored hydrogen can be reused by PEMFCs to generate electricity or be sold to other hydrogen consumers. A probabilistic optimization algorithm is devised which consists of 2m + 1 point estimate method to handle the uncertainty in input random variables (IRVs) and a multi‐objective Self‐adaptive Bee Swarm Optimization (SBSO) algorithm to minimize the cost and emissions simultaneously. Several techniques are proposed in the SBSO algorithm to make it a powerful black‐box optimization tool. The efficiency of the proposed approach is verified on a typical grid‐connected MG with several distributed energy sources.     

Robust Optimal Operation of Two‐Chamber Microbial Fuel Cell System Under Uncertainty: A Stochastic Simulation Based Multi‐Objective Genetic Algorithm Approach

This investigation is performed to study the optimal operation decision of two‐chamber microbial fuel cell (MFC) system under uncertainty. To gain insight into the mechanism of uncertainty propagation, a Quasi‐Monte Carlo method‐based stochastic analysis is conducted not only to elucidate the effect of each uncertain parameter on the variability of power density output, but also to illustrate the interactive effects of the all uncertain parameters on the performance of MFC. Moreover, a systematic stochastic simulation‐based multi‐objective genetic algorithm framework is proposed to identify a set of Pareto‐optimal robust operation strategies, which is helpful to provide an imperative insight into the relationship between the mean and standard deviation of output power density. The results indicate that (1) the coefficient of variance (COV) value of output power density has a linear relationship with the COV value of each uncertainty parameter as well as all interactive parameters; and (2) a significant performance improvement with respect to both mean and standard deviation of power density is observed by implementing the multi‐objective robust optimization. These results thus validate that the proposed uncertainty analysis and robust optimization framework provide a promising tool for robust optimal design and operation of fuel cell systems under uncertainty.     

装置蒸汽动力系统与热电厂运行同步优化

石化企业装置蒸汽动力系统通常独立设计和操作,忽视了与热电厂蒸汽动力系统的联系。热电厂蒸汽动力系统通常在固定的蒸汽和电力需求下进行优化,忽视了与装置蒸汽动力系统的联系。为实现石化企业蒸汽动力系统的全局优化,本文提出了用于装置蒸汽动力系统与热电厂运行同步优化的方法。首先使用热电厂透平和锅炉的设计及运行数据回归得到设备模型系数,依照现有结构建立热电厂蒸汽动力系统约束。然后以装置蒸汽动力系统设计和操作灵活性为区分,将装置分为三类：第一类装置蒸汽和电力需求无法调节;第二类装置可以通过减温减压调节蒸汽需求;第三类装置既可以通过减温减压调节蒸汽需求,也可以通过驱动选择调节热电需求。装置透平模型参数采用文献值,通过采集各类装置蒸汽和电力需求等数据建立装置蒸汽动力系统约束,最后通过热电厂与装置蒸汽和电力的连接关系建立耦合模型。耦合模型以年度费用为目标函数,其中包括热电厂运行费用以及装置透平和电机的年度投资费用,通过优化求解得到热电厂设备负荷分配方案以及装置蒸汽动力系统设计方案。通过算例论证了同步优化方法的可行性,与独立优化相比,同步优化降低年度费用451万美元。     

Optimization of sub-ambient separation systems with embedded cubic equation of state thermodynamic models and complementarity constraints

A previously developed equation-based flowsheet optimization framework is extended and applied to design sub-ambient separation systems for oxy-fired coal power systems with carbon capture. Unlike most commercial flowsheet design and optimization tools, the proposed methods use exact derivatives and large-scale nonlinear programming algorithms to solve large flowsheet design problems with many degrees of freedom, including the simultaneous design of air separation units (ASUs) and their accompanying multistream heat exchangers. Emphasis is placed on additional model improvements regarding thermodynamic calculations. In order to maintain differentiability, complementarity constraints are used to model switches, including vanishing and reappearing phases. Nevertheless, these complementarity constraints may construct trivial phase equilibrium solutions, and a procedure based on embedded bubble and dew points calculations is proposed to avoid them. Furthermore, additional complementarity constraints for the cubic equation of state model are proposed to ensure correct phase identification in the supercritical region. Finally, the efficacy of these new models are demonstrated by optimization of the CO₂ processing unit and compression train for an oxy-fired power plant.     

Using surrogate models for efficient optimization of simulated moving bed chromatography

A new approach of using computationally cheap surrogate models for efficient optimization of simulated moving bed (SMB) chromatography is presented. Two different types of surrogate models are developed to replace the detailed but expensive full-order SMB model for optimization purposes. The first type of surrogate is built through a coarse spatial discretization of the first-principles process model. The second one falls into the category of reduced-order modeling. The proper orthogonal decomposition (POD) method is employed to derive cost-efficient reduced-order models (ROMs) for the SMB process. The trust-region optimization framework is proposed to implement an efficient and reliable management of both types of surrogates. The framework restricts the amount of optimization performed with one surrogate and provides an adaptive model update mechanism during the course of optimization. The convergence to an optimum of the original optimization problem can be guaranteed with the help of this model management method. The potential of the new surrogate-based solution algorithm is evaluated by examining a separation problem characterized by nonlinear bi-Langmuir adsorption isotherms. By addressing the feed throughput maximization problem, the performance of each surrogate is compared to that of the standard full-order model based approach in terms of solution accuracy, CPU time and number of iterations. The quantitative results prove that the proposed scheme not only converges to the optimum obtained with the full-order system, but also provides significant computational advantages.     

考虑周期划分特性和储能电池生命周期费用的混合供电系统优化

在风/柴/储混合供电系统中,风力发电的不确定性和用户负载的波动性对混合供电系统及其储能电池系统的优化设计和运行具有重要影响。针对混合供电系统,采用k-均值周期划分法考虑风力发电和用户负载的波动性,建立了混合供电系统及其储能电池系统的优化模型。在优化模型中将储能电池生命周期费用作为惩罚函数,将循环次数作为约束,以标准化能源费用为目标对混合供电系统进行优化,分析了风力发电不确定性对混合供电系统优化设计的影响。以能量需求为793 kW·h·d^-1的风/柴/储混合供电系统为例,研究了混合供电系统优化设计方法。研究表明,考虑风力发电和用户负载的周期划分后,储能电池系统循环次数显著降低,有利于储能电池使用寿命的延长。按风速特性划分周期时,储能电池系统的年循环次数最少,混合供电系统的标准化能源费用最低。当储能电池在其生命周期内的最大循环次数由2000次提升到10000次时,混合供电系统的标准化能源费用降低幅度可达8.3%~16.6%。