间歇过程动态优化方法综述

间歇过程的动态优化近年来引起了广泛关注.针对近期主要的研究成果,综述了间歇过程动态优化中的数学模型、求解方法及控制架构等问题,介绍了间歇过程目前主要的操作优化方法,具体分析了含不确定性间歇过程的动态优化策略,总结了间歇过程常用的优化模拟计算工具.最后探讨了这一领域中值得进一步研究的问题和可能的发展方向.     

甘油间歇生物歧化过程的动态优化

研究了甘油间歇生物歧化过程的动态优化。针对甘油间歇生物歧化过程的非线性过量动力学系统,首先在终端时刻是固定和可变两种情况下,分别以终端时刻产物1,3-丙二醇的浓度、终端时刻产物1,3-丙二醇的产率为目标函数,提出了三个动态优化模型;然后基于有限元配置法将甘油间歇生物歧化过程的状态方程转化为代数方程,从而得到动态优化模型的非线性规划形式。最后对提出的三个动态优化模型进行求解,并对所得优化结果进行了分析与讨论。与已有结果相比,获得了更高的1,3-丙二醇产率。     

非线性动态优化仿真在间歇过程中的应用

为实现求解大规模非线性系统最优控制问题的自动设计及算法自动生成问题,开发基于非线性规划的动态优化软件集成系统.首先自定义基于XML的标准化非线性动态优化标记语言NDOML,采用NDOML模型设计模型编辑器;结合联立法和多点打靶法对NDOML模型进行自动离散化处理;最后利用内点算法解题器IPOPT设计了解题器接口程序生成器,实现模型录入通用化、模型离散自动化及优化控制算法代码的自动生成等多种应用功能.最后通过一个间歇反应过程实例验证动态优化软件系统的有效性,结果显示,系统能够实现最优控制算法的自动生成,仿真结果为大型非线性系统求解优化提供了依据.     

基于免疫萤火虫算法的RFID仓储车辆动态调度

针对物流配送实时仓储车辆调度问题,提出了一种基于RFID技术的免疫萤火虫车辆动态调度框架。建立了基于配送成本的带约束条件车辆路径问题数学模型,运用免疫萤火虫优化算法求解该模型,免疫萤火虫优化算法将萤火虫优化及免疫克隆技术融合,采用多层进化模式,在低层萤火虫操作中及高层免疫操作中分别引入多态子种群自适应机制和全局极值筛选策略,以提高算法全局收敛效率,在此基础上设计了仓储车辆动态调度框架,将车辆动态调度过程分为车辆调度任务控制和路径优化两个阶段,给出了车辆动态调度任务处理流程。实验仿真表明,该车辆动态调度算法能够有效地解决大规模动态物流车辆调度问题。     

煮糖结晶过程的多模型动态矩阵控制研究

由于工业结晶过程存在非线性和滞后现象,因此采用常规的控制策略已经达不到工业生产的要求。针对这种情况,通过对煮糖结晶过程的机理分析建立了反映实际煮糖过程的动态模型,在此基础上采用一种先进的预测控制算法即多模型非线性动态矩阵控制(DMC)算法进行控制。仿真和对比研究有力地证明了所建立的模型是有效的,同时也证明该算法可以应用到工业结晶过程弗能够取得较好的控制品质。     

动态多项目选择计划管理及其免疫优化决策

针对资源受限情况下动态多项目选择计划管理问题,探讨其数学模型,并设计免疫优化算法对其求解。算法设计中,分别引入基因块的随机漂移与确定性漂移两种变异方式,以及个体的亲和选择与激励选择两种选择方案。最后,数值实验验证了模型的合理性以及各算法的有效性。     

列车节能运行决策问题的模拟优化研究

轨道交通运输耗能巨大,研究列车节能操作运行具有重要的理论意义和实用价值。从节能角度出发,分析列车运行过程中的能量转换机制,建立单列车耗能最低优化模型、多列车节能优化模型及列车延误多目标优化控制模型,针对模型本身及其约束条件的复杂性,提出基于改进布谷鸟优化算法与动态搜索方法的“模拟优化”求解方法,对列车节能运行决策问题进行求解,并通过与其他同类算法的比较,阐述了所提方法的优越性。得到列车在不同运行工况下的最优节能运行控制策略,确定各情况下列车运行的最优速度距离曲线,结果符合实际情况。改进算法的搜索效率更高,研究思路与模型对于列车节能操作运行具有一定的借鉴意义,所提出的针对复杂优化模型的求解方法合理有效,适用性强,有一定的参考价值。     

一种间歇反应过程迭代动态规划方法

随着工业过程对降低产品成本、改进产品质量、满足安全要求和环境规范,间歇反应过程的优化变得越来越重要.本文因此给出了一种有效的基于随机选点的间歇反应过程迭代动态规划算法,并给出了算法实现的详细步骤,能够有效实现间歇反应过程中温度、浓度等变量的动态优化问题.所述的迭代动态规划算法通过调节分段数P和离散点数(N和M)可以有效的避免计算量激增的问题,具有稳定可靠、易寻找到全局最优解的优点.以典型的间歇反应动态优化问题作为实例进行了研究,并与国际上公开报道结果进行了详细的比较研究,结果表明了所述方法的可靠有效性.     

基于微粒群优化算法的间歇过程迭代学习

在实际的间歇过程优化控制中面临着模型参数不确定性的问题,并且基于梯度的优化控制技术在处理具有多个局部极值的目标函数时,往往会陷入局部极值点.针对该问题,提出一种基于微粒群优化算法的间歇过程产品质量迭代学习策略.结合间歇过程产品质量控制问题的特点和各种约束条件,利用微粒群优化算法在非线性问题求解及大尺度空间搜索上的优势,...     

一种基于过程神经网络的动态系统控制信号求解模型和算法

针对非线性动态系统控制问题,提出了一种基于过程神经网络的控制信号求解模型和算法。利用过程神经网络对动态系统时变输入/输出信号的非线性映射机制和对系统过程模态特征的自适应提取能力,建立基于过程神经网络的辨识模型;然后根据所建立的辨识模型、系统控制结构和状态参数之间的关系,构建可满足系统信息传递约束关系的控制信号求解模型。分析了过程神经网络控制模型的信息处理机制,给出了基于GA与LMS相结合的优化求解算法,实验结果验证了模型和算法的有效性。     

全局优化视角下的有色冶金过程建模与控制

为提高生产效率、降低能源消耗、减少环境污染,需要对有色冶金过程进行建模,在系统模型的基础上,通过控制与优化技术,使过程系统运行在最优工况.本文以几个典型有色冶金过程为背景,阐述有色冶金过程建模、控制与优化三者之间的内在关联;从科学研究层次的角度上指出建模、控制与优化分属于不同层次的问题,且从方法论的角度指出建模、控制分两步进行:选择模型结构和估计模型参数、选择控制器结构和整定控制器参数,有色冶金过程系统模型、控制器的结构和参数确定问题均可以看成是非凸优化问题;探讨了全局优化视角下,建模、控制问题转化为优化问题以及在求解优化问题过程中存在的难点,提出解决这些难点的一些可行方案.     

煮糖结晶过程的动态矩阵控制研究

由于煮糖结晶过程本身受干扰的因素比较多，如糖浆的锤度、糖浆杂质、真空度以及温度的变化都会影响蔗糖结晶过程，非线性比较强。因此一般的控制策略（比如常规PID）难以凑效，根据煮糖结晶过程的物料平衡、能量平衡和生产过程的各种物性参数的机理解析关系（如晶体生长分散）建立描述被控对象的模型，并以该模型作为预测模型对煮糖结晶过程实施动态矩阵控制（DMC）．仿真结果表明该控制算法对煮糖过程的控制是有效的。     

发酵过程混合建模与优化控制新方法

针对当前微生物发酵过程存在因为生物传感器不具备足够的准确性和灵敏性,实验时的菌液和产物浓度等生化指标难以实时监测和控制等缺点,提出了采用量子粒子群优化算法(QPSO)优化最小二乘支持向量机(LSSVM)参数的QPSO-LSSVM混合建模新方法,并用于多粘菌素的发酵过程建模;同时,基于此模型,采用QPSO算法对pH值与溶解氧浓度Do控制轨线进行优化研究;首先,利用LSSVM进行发酵过程的建模,然后采用QPSO对LSSVM建模过程中的重要参数进行优化调整,形成QPSO-LSSVM混合建模与优化控制方法;仿真结果表明,该方法得到的模型能取得更好的预测效果,优化后的pH值与Do浓度控制轨线能够提高最终的产物浓度;该方法用于发酵过程的建模和重要参数的优化控制是可行的、有效的。     

Seven process modeling guidelines (7PMG)

Business process modeling is heavily applied in practice, but important quality issues have not been addressed thoroughly by research. A notorious problem is the low level of modeling competence that many casual modelers in process documentation projects have. Existing approaches towards model quality might be of benefit, but they suffer from at least one of the following problems. On the one hand, frameworks like SEQUAL and the Guidelines of Modeling are too abstract to be applicable for novices and non-experts in practice. On the other hand, there are collections of pragmatic hints that lack a sound research foundation. In this paper, we analyze existing research on relationships between model structure on the one hand and error probability and understanding on the other hand. As a synthesis we propose a set of seven process modeling guidelines (7PMG). Each of these guidelines builds on strong empirical insights, yet they are formulated to be intuitive to practitioners. Furthermore, we analyze how the guidelines are prioritized by industry experts. In this regard, the seven guidelines have the potential to serve as an important tool of knowledge transfer from academia into modeling practice.     

Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm

The use of inverse system model as a controller might be an efficient way in controlling non-linear systems. It is also a known fact that fuzzy logic modeling is a powerful tool in representing nonlinear systems. Therefore, inverse fuzzy model can be used as a controller for controlling nonlinear plants. In this context, firstly, a new fuzzy model based inverse controller design methodology is presented in this study. The design methodology introduced here is based on a recursive optimization procedure that searches for an optimal inverse model control signal at every sampling time. Since the task of optimization should be accomplished in between two sampling periods the use of a fast optimization algorithm becomes essential. For this reason, Big Bang-Big Crunch (BB-BC) optimization algorithm is used due to its low computational time and high global convergence properties. Even though, inverse model controllers may produce perfect control while operating in an open loop fashion, this open loop control would not be sufficient in the case of modeling mismatches or disturbances that might occur over the system. In order to overcome this problem, secondly, an on-line adaptation mechanism via BB-BC optimization algorithm is introduced in addition to BB-BC optimization based fuzzy model inverse controller. The adaptation mechanism is used to update the related parameters of the model while minimizing the absolute value of the instantaneous error between the system and model outputs. In this manner, the system output is somehow fed back, the overall control form can be considered as a closed-loop system. The new fuzzy model based inverse control scheme with the new online adaptation mechanism has been implemented and tested on the two real time processes; namely, heat transfer and pH processes and very satisfactory results has been reported.     

Low‐cost multiband compact branch‐line coupler design using response features and automated EM model fidelity adjustment

Design closure of compact microwave components is a challenging problem because of significant electromagnetic (EM) cross‐couplings in densely arranged layouts. A separate issue is a large number of designable parameters resulting from replacement of conventional transmission line sections by compact microstrip resonant cells. This increases complexity of the design optimization problem and requires employment of expensive high‐fidelity EM analysis for reliable performance evaluation of the structure at hand. Consequently, neither conventional numerical optimization algorithms nor interactive approaches (e.g., experience‐driven parameters sweeps) are capable of identifying optimum designs in reasonable timeframes. Here, we discuss application of feature‐based optimization for fast design optimization of dual‐ and multiband compact couplers. On one hand, design of such components is difficult because of multiple objectives (achieving equal power split and good matching and port isolation for all frequency bands of interest). On the other hand, because of well‐defined shapes of the S‐parameter responses for this class of components, feature‐based optimization seems to be well suited to control multiple figures of interest as demonstrated in this work. Two‐level EM modeling is used for further design cost reduction. More importantly, we develop a procedure for automated determination of the low‐fidelity EM model coarseness that allows us to find the fastest possible model that still ensures sufficient correlation with its high‐fidelity counterpart, which is critical for robustness of the optimization process. Our approach is illustrated using two dual‐band compact couplers. Experimental validation is also provided.     

大型挤压油膜阻尼器非线性动力学分析与优化研究进展

挤压油膜阻尼器（Squeeze Film Dampers,SFDs）是旋转机械中常用的一类支承阻尼结构装置,能够改善转子系统的动力特性.当前工程实际中已经大量使用的两类不同结构形式的挤压油膜阻尼器,仍然存在着减振效果不稳定甚至会导致转子失稳,以及阻尼器动力学机制不清楚、建模和分析精度差、设计方法欠缺等理论技术难题.本文首先介绍两类典型SFD的结构形式和主要失效模式,然后详细叙述SFD动力学分析与优化设计方法在发展过程中的代表性研究成果,涉及SFD动力学特性、转子 SFD系统动力学特性研究、SFD动力学设计与优化等几个方面的研究,并对SFD的试验测试技术方面的成果进行评述.在此基础上,探讨目前先进航空发动机用大型挤压油膜阻尼器亟须开展的基础研究任务,特别强调了数据驱动与动力学解析模型融合的SFD动力学建模、分析与设计优化的发展方向.     

分布式约束优化方法研究进展

多agent系统作为分布式人工智能研究领域的重要分支,已被广泛应用于多个领域中复杂系统的建模.而分布式约束优化作为一种多agent系统求解的关键技术,已成为约束推理研究的热点.首先对其适用性进行分析,并基于对已有算法的研究,总结出采用该方法解决问题的基本流程,在此基础上,从解的质量保证、求解策略等角度对算法进行了完整的分类;其次,根据算法分类结果以及执行机制,对大量经典以及近年来的分布式约束优化算法进行了深入分析,并从通信、求解质量、求解效率等方面对典型算法进行了实验对比;最后,结合分布式约束优化技术的求解优势给出了分布式约束优化问题的实际应用特征,总结了目前存在的一些问题,并对下一步工作进行了展望.     

PKM capabilities and applications exploration in a collaborative virtual environment

This paper introduces an integrated validation system that consists of the following modular components: kinematic/dynamic analysis module, kinetostatic model, CAD module, FEM module, CAM module, optimization module and virtual environment for remote control. In this paper, authors focus mainly on the modules of kinetostatic modeling, dynamic modeling, PKM design optimization and remote control realization. The prototype of a 3-dof Parallel Kinematic Machine (PKM) developed at the Integrated Manufacturing Technologies Institute of National Research Council of Canada (NRC-IMTI) is used as an example throughout this paper.     

Data-driven,using design of dynamic experiments,versus model-driven optimization of batch crystallization processes

A new data-driven experimental design methodology, design of dynamic experiments (DoDE), is proposed as a means of developing a response surface model that can be used to effectively optimize batch crystallization processes. This data-driven approach is especially useful for complex processes for which it is difficult or impossible to develop a knowledge-driven model in a timely fashion for the optimization of an industrial process. Design of dynamic experiments [1] generalizes the formulation of time-invariant design variables from design of experiments, allowing for consideration of time-variant design variables in the experimental design. When combined with response surface modeling and an appropriate optimization algorithm, a data-driven optimization methodology is produced, which we call DoDE optimization. The method is used here to determine the optimal cooling rate profile, which integrates to give the optimum temperature profile, for a batch crystallization process. To examine the effectiveness of the DoDE optimization method, the data-driven optimum temperature profile is compared to the optimum temperature profile obtained using a model-based optimization technique for the potassium nitrate–water batch crystallization model developed by Miller and Rawlings [2]. The temperature profiles calculated using DoDE optimization yield response values within a few percent of the true model-based optimum values. A sensitivity analysis is performed on one case study to evaluate the distribution of the response variable from each method in the presence of parameter and initial seed distribution variability. It is demonstrated that there is partial overlap in the distributions when only variability in the model parameters is evaluated and there is substantial overlap when variability is included in both the model and initial seed distribution parameters. From this evidence, it can be concluded that the DoDE optimization method has the potential to be a useful data-driven optimization tool for batch crystallization processes where a first-principles model is not available or cannot be developed due to time and/or cost constraints.