基于两层分块GMM-PRS的流程工业过程运行状态评价

过程运行状态评价旨在实时判断运行性能优劣程度,并追溯导致非优运行状态的原因,指导操作人员进行生产调整,保证企业经济效益.因此,对过程运行性能优劣评价的研究具有重要的理论和应用价值.本文针对定量、定性变量共存的流程工业过程运行状态评价问题,提出基于两层分块混合模型的评价方法.将流程工业过程根据其物理特性和管理方向划分子块,产生子块层和全流程层.在定量信息占主导地位的子块内,建立定量的高斯混合模型（Gaussian mixture model,GMM）.在定性信息占主导地位的子块内,建立定性概率粗糙集（Probabilistic rough set,PRS）模型.综合各子块运行状态信息,进一步判定全流程运行状态等级.针对非优运行状态等级,本文提出基于贡献率的非优原因追溯方法,在非优子块内进行原因追溯.最后,将所提方法应用于某黄金湿法冶炼生产过程,说明所提方法的可行性和有效性.     

基于分层分块结构的流程工业过程运行状态评价及非优原因追溯

过程运行状态评价是指在安全生产的前提下,对过程运行性能优劣程度做出评判,并对非优运行状态追溯原因,以指导操作人员进行生产调整.针对含不确定性的流程工业过程运行状态评价问题,本文提出分层分块评价结构.同时,考虑到实际生产过程中大量存在的不确定性信息,采用粗糙集对每个子模型进行建模,建立过程变量、工艺指标与综合评价指标间的关系.最后,将所提方法应用于一个金湿法冶金过程中,验证所提方法有效性.     

数据驱动的最优运行状态鲁棒评价方法及应用

在现代复杂工业生产过程中, 细致而稳健的运行状态评价及非优因素识别对指导工业生产具有十分重要的实际意义.考虑到复杂工业过程难以建立准确的数学模型和实际工业过程数据噪声及离群点污染比较严重的问题, 本文提出一种全潜鲁棒偏M估计的复杂工业过程最优状态的鲁棒评价方法.在建立离线评价模型时, 通过对过程数据主元和残差子空间的进一步分解, 提取出能够反映与原材料、生产消耗和产品质量等因素相关的经济指标的变化信息, 同时采用样本数据加权的方法消除离群点对评价模型的不利影响, 提高算法的鲁棒性; 在线评价时, 针对生产过程中存在不确定性因素, 引入在线数据窗口及相似度分析进行在线评价, 并给出在线评价的准则和流程, 提高评价结果的可靠性, 当评价结果非优时, 通过计算相应变量的贡献率识别非优因素.最后, 通过重介质选煤过程验证了所提方法的有效性.     

基于Fisher 判别分析的过程运行状态在线评价

为了实时掌握生产过程运行状态, 提出一种基于Fisher 判别分析(FDA) 的过程运行状态在线评价方法. 提出 离线数据分类与识别算法, 以识别不同稳定运行状态的建模数据及其对应的状态等级; 利用FDA提取各个稳定运行 状态的特征属性, 建立评价模型; 在线评价时, 通过“时间窗口”数据特征与各个状态等级的相似度, 实时评价过程运行状态. 将所提出的方法应用于某湿法冶金过程的仿真结果验证了该方法的有效性.

     

基于ISDAE模型的复杂工业过程运行状态评价方法及应用

工业过程的运行状态评价对保证产品质量及提升企业综合经济效益具有重要意义. 针对工业过程中存在强非线性、信息冗余以及不确定性因素影响而难以建立稳健可靠的运行状态评价模型问题, 提出一种基于综合经济指标驱动的稀疏降噪自编码器模型(Comprehensive economic index driven sparse denoising autoencoder, ISDAE)的复杂工业过程运行状态评价方法. 首先, 在SDAE (Sparse denoising autoencoder)模型中引入综合经济指标预测误差项, 迫使SDAE学习与综合经济指标相关的数据特征, 建立ISDAE特征提取模型. 其次, 将ISDAE模型所学特征作为输入训练运行状态识别模型, 级联特征提取模型和运行状态识别模型并通过微调网络结构参数获得运行状态评价模型. 另外, 针对非优状态, 提出一种基于自编码器贡献图算法的非优因素追溯方法, 通过计算变量的贡献率识别非优因素. 最后, 将所提方法应用于重介质选煤过程, 验证所提方法的有效性和实用性.     

基于PCA和RBF网络的减速箱运行状态诊断技术

针对减速箱运行状态和特征参数之间存在的复杂非线性关系,提出了基于主成分分析的RBF神经网络减速箱运行状态诊断方法.该方法用主成分分析方法将高维相关特征参数转化为低维相互独立的特征参数,在此基础上建立了RBF网络分类器,并用该网络对某汽轮机减速箱的运行状态进行识别.理论分析和实验结果表明,基于PCA和RBF网络方法的减速箱运行状态诊断技术具有模型简单,检测速度快等优点,可以在实际应用中发挥有效作用.     

基于改进随机森林算法的工业过程运行状态评价

运行状态评价是指在过程正常生产的前提下, 进一步判断生产过程运行状态的优劣. 针对复杂工业过程定量信息与定性信息共存的情况, 本文提出了一种基于随机森林的工业过程运行状态评价方法. 针对随机森林中决策树信息存在冗余的问题, 基于互信息将传统随机森林中的决策树进行分组, 并选出每组中最优的决策树组成新的随机森林. 同时为了强化评价精度高的决策树和弱化评价精度低的决策树对最终评价结果的影响, 使用加权投票机制取代传统众数投票方法, 最终构成一种基于互信息的加权随机森林算法(Mutual information weighted random forest, MIWRF). 对于在线评价, 本文通过计算在线数据处于各个等级的概率, 并且结合提出的在线评价策略, 判定当前样本运行状态等级. 为了验证所提算法的有效性, 将所提方法应用于湿法冶金浸出过程, 实验结果表明, 相对于传统随机森林算法, MIWRF 降低了模型的复杂度, 同时提高了运行状态评价精度.     

复杂工业过程运行状态评价方法回顾与展望

准确感知和认知复杂工业过程的运行状态对于实现过程智能控制和优化决策至关重要,是当前实现工业人工智能需要解决的关键问题之一.传统过程监测理论系统已不能满足现代工业生产过程对过程运行状态认知的精细化及准确化的需求,因此,复杂工业过程运行状态评价技术应运而生,近几年受到学术界和工业界广泛关注并快速发展.对此,首先从复杂工业过程的主要特性以及数据提取过程中面临的问题出发,回顾基于数据驱动的相关工业过程运行状态评价方法;然后根据最优性评价结果总结导致状态非“优”的原因,并进一步给出相关非优因素追溯方法;最后对现有研究内容和这一领域中值得进一步研究的发展方向做出总结和展望.     

融合Cox回归与维纳过程的设备状态评估方法

针对机场自助行李托运设备运行状态评估存在数据利用率低和预测精度不高的问题,提出一种融合Cox回归与维纳过程的设备状态评估方法;首先基于事件型数据构建多风险因素影响下的设备状态突变模型,又基于关键子系统的状态型数据构建设备状态渐变模型,提出了复合退化指标并基于维纳过程建立设备性能退化模型,得到设备整体的健康状态值并提出相应维修决策;利用商用模块化航空推进系统仿真数据集和自助行李托运设备运行监测数据对所提方法进行实验验证,结果表明融合Cox回归与维纳过程的设备状态评估方法提高了设备数据利用率和预测精度.     

基于GMM和贝叶斯推理的多模态过程运行状态评价

为使综合经济效益最大化,生产过程应保持在最优运行状态等级.针对多模态过程运行状态等级优劣判断问题,提出一种运行状态等级评价方法.该方法对同一运行状态等级的多模态数据建立一个高斯混合模型(Gaussian mixture model,GMM),确保特征提取的准确性,避免模态划分问题.至于在线评价策略,本文采用贝叶斯推理,确定当前运行状态属于各等级的后验概率.并引入滑动窗口,判定当前运行状态等级,有效解决多模态过程运行状态在线评价问题.针对"非优"运行状态,本文提出一种基于变量偏导数的贡献计算方法,对导致过程运行状态等级"非优"的原因变量进行追溯.最后,通过田纳西–伊斯曼(Tennessee–Eastman,TE)过程验证所提方法的有效性.     

基于多时段机组组合的中长期市场安全校核方法

为解决电力中长期市场中调度机构安全校核缺乏有效技术方法的问题,基于多时段机组组合模型提出了一种面向电力中长期市场的安全校核方法。以发电企业中长期校核电量偏差最小化为优化目标,综合考虑电力平衡约束、网络传输能力约束、机组发电能力约束等多方面系统运行约束条件,构建了电力中长期市场机组组合模型。利用该机组组合模型,对电网调度过程进行运行模拟。并通过判定各发电企业电力中长期交易结果的完成率偏差,来判定电力中长期市场交易结果是否满足系统运行要求。最后,基于某省区电网实际数据构造的算例表明,与传统典型日运行模拟校核方法相比,本文所提出的校核方法能有效考虑机组启停时间约束等运行要求,更符合电网实际调度运行需要,能够给出更为准确的安全校核判定结果。     

Online process operating performance assessment and nonoptimal cause identification for industrial processes

Although industrial processes are usually operated at the optimal point in the early stage of the production, the operating performance may deteriorate with time due to process disturbances. In order to pursue optimal comprehensive economic benefit (CEB), online process operating performance assessment on optimality has become a key issue. However, a little work has been published in this research area. In this paper, a new online operating performance assessment and nonoptimal cause identification method for industrial process are proposed. The contributions of this paper can be summarized as follows: a novel performance-similarity-based online operating performance assessment method is proposed; total projection to latent structures (T-PLS) is applied to the area of process performance assessment for the first time; the online assessment results include not only the deterministic performance grades, but also the performance grade conversions which were not covered in the existing assessment method; when the assessment result is nonoptimal, a novel automatic nonoptimal cause identification strategy is developed based on variable contributions, which is meaningful for guiding the further production adjustment. Finally, the feasibility and efficiency of the proposed method are illustrated with a case of gold hydrometallurgical process.     

温度感知的Linux多核调度算法研究

多核处理器温度升高会影响芯片的稳定性和性能的发挥,硬件层面的DTM(Dynamic Thermal Management)方法以牺牲处理器性能为代价来降低功耗,提出了在一种软件层面的温度感知调度算法,它可以在线实时获取处理器性能计数器的值并计算各个执行核温度,根据各执行核的温度状况在各个核上合理分配进程,给出了温度感知的启发式方法。基于ATMI温度仿真器的仿真表明,温度感知调度算法较无温度感知的算法可以创建更均匀的功率密度图,且带MST启发式方法的温度感知调度算法能明显减少进程的迁移次数。     

基于多块MICA-PCA的全流程过程监控方法

多块策略广泛应用于全流程过程监控领域,以解决变量关系复杂性较高的问题,但传统分块方法得到的子块数据存在高斯与非高斯混合分布问题,影响过程监控的效果.为此,提出一种基于多块MICA-PCA的过程监控方法.首先采用Jarque-Bera(J-B)检测方法对原始数据进行高斯与非高斯分块;然后利用Hellinger距离(HD)方法获得高斯与非高斯子块,通过对高斯与非高斯子块采用不同的建模和诊断方法,提高监控效果;最后将该方法应用于田纳西-伊斯曼(TE)过程的监控中,以验证所提出方法的有效性.     

Operating optimality assessment based on optimality related variations and nonoptimal cause identification for industrial processes

In this study, a novel online operating optimality assessment based on optimality related variations and nonoptimal cause identification method is proposed for industrial processes. The optimality related variations are extracted from each steady performance grade by analyzing the common and unique variations among steady performance grades, which avoids the time-consuming data alignment. When the optimality related variations are used in assessment, both the robustness and sensitivity of the assessment method are improved compared with the PCA-based assessment for its abilities in highlighting the process variations related to operating performance and excluding those unrelated variations. Based on the similarities between the optimality related variations of the online data and that of each steady performance grade, the process operating performance can be evaluated as the steady performance grade or the conversion process between performance grades, and this provides more information for the in-depth understanding of the process operating. For nonoptimal operating performance, the nonoptimal cause identification strategy is developed for further production adjustment and performance improvement. Finally, the efficiency of the proposed method is illustrated with a case of gold hydrometallurgical process.     

Grasshopper optimization algorithm for optimal load frequency control considering Predictive Functional Modified PID controller in restructured multi-resource multi-area power system with Redox Flow Battery units

Load frequency control (LFC) is a well-established issue in design and operation of power systems considering to the extension, restructuring, and complexity of the interconnected power systems and also the emergence utilization of renewable energy resources. This paper studies the frequency control of multi-area multi-source power system based on the importance of the LFC in the stability of the power system which includes various generation units of thermal, hydroelectric, wind, natural gas and diesel under the restructured environment. In this system, non-linear physical constraints, governor dead band (GDB) and generation rate constraint (GRC) are considered. In this paper, a new Predictive Functional Modified PID (PFMPID) controller is proposed that the effectiveness of this controller is verified compared to the traditional one. In order to optimize and demonstrate the superiority of the proposed control method, Grasshopper Optimization Algorithm (GOA) is proposed as a suitable solution. To further improve the performance of the under study system, the use of the Redox Flow Battery (RFB) energy storage unit has also been proposed. Since the operation evaluation of the proposed process is necessary in different system conditions, the performance of the proposed method is studied under various disturbances and simulation results are presented.     

Modelling of the whole process of a university campus CHP power plant and dynamic performance study

Combined heat and power (CHP) refers to a process/system designed to utilize the waste or residual heat from a power generation process. Thus, a CHP plant can produce both electricity and heat. The nature of such a combination makes the process more complex than any single power generation process or boiler heating system. The paper focuses on modelling study and analysis of energy efficiency of the University of Warwick micro-CHP power plant. In this CHP modelling study, a gas turbine module is built to provide driving power and methane is used as fuel gas. Heat recovery system and auxiliary boiler modules are developed for thermal power generation. All the sub-systems are validated by comparing the simulation results with the operating data collected from the CHP plant. The dynamic performance of the key CHP process outputs is studied with respect to the variation of the input syngas stream, including electricity generation, thermal power output and water output temperature. Simplified controllers are also applied to the gas engineheat recovery subsystem and auxiliary boiler. Simulation results with/without feedback control are both analyzed. The study has highlighted the key factors which influence the plant performance and suggested the strategy for potential energy efficiency improvement.