卷帘型干式气柜在瓦斯回收系统中的应用

比较了几种不同形式气柜的优缺点,中国石化沧州分公司炼油厂选用了卷帘型干式气柜对瓦斯气进行回收和储存。该气柜主要借助于一种柔性的橡胶密封材料将气柜侧板和活塞密封连接。介绍了该气柜的设计参数、结构和运行原理,详述了瓦斯自动放散管的作用。讨论分析了气柜运行事故隐患,并提出相应的防范措施,最后针对气柜运行过程中存在的问题提出了整改措施。     

螺旋湿式气柜制造中的常见问题分析及处理办法

本文通过对两个详实的工程实例剖析,提出了螺旋湿式气柜制造、安装过程中的普通性问题,并通过分析,有针对性地提出了改进办法,为螺旋湿式气柜的制造、安装积累了一些经验。     

一种基于改进MPCA的间歇过程监控与故障诊断方法

针对基于不同展开方式的多向主元分析(MPCA)方法在线应用时各自存在的缺陷,提出一种改进的基于变量展开的MPCA方法,实现间歇过程的在线监控与故障诊断。该方法采用随时间更新的主元协方差代替固定的主元协方差进行T²统计量的计算,充分考虑了主元得分向量的动态特性;同时引入主元显著相关变量残差统计量,避免SPE统计量的保守性,且该统计量能提供更详细的过程变化信息,对正常工况改变或过程故障引起的T²监控图变化有一定的识别能力;最后提出一种随时间变化的贡献图计算方法用于在线故障诊断。该方法和MPCA方法的监控性能在一个青霉素发酵仿真系统上进行了比较。仿真结果表明：该方法具有较好的监控性能,能及时检测出过程存在的故障,且具有一定的故障识别和诊断能力。     

基于在线子空间辨识的自适应预测控制

针对实际工业工程中存在非线性、时变的特点,提出一种基于子空间辨识的自适应预测控制方法。利用滚动窗口在线更新R阵,得到新的预测模型参数矩阵,通过比较更新前和更新后的预测误差来决定是否更新预测模型。将此控制方法应用于2-CSTR过程控制的仿真试验,通过与自适应模糊控制、PID控制器的比较,说明了该方法的优越性。     

Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method

A four‐layer fuzzy neural network (FNN) model combining particle swarm optimization (PSO) algorithm and clustering method is proposed to predict the solubility of gases in polymers, hereafter called the CPSO‐FNN, which combined fuzzy theory's better adaptive ability, neural network's capability of nonlinear and PSO algorithm's global search ability. In this article, the CPSO‐FNN model has been employed to investigate solubility of CO₂ in polystyrene, N₂ in polystyrene, and CO₂ in polypropylene, respectively. Results obtained in this work indicate that the proposed CPSO‐FNN is an effective method for the prediction of gases solubility in polymers. Meanwhile, compared with traditional FNN, this method shows a better performance on predicting gases solubility in polymers. The values of average relative deviation, squared correlation coefficient (R²) and standard deviation are 0.135, 0.9936, and 0.0302, respectively. The statistical data demonstrate that the CPSO‐FNN has an outstanding prediction accuracy and an excellent correlation between prediction values and experimental data. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

基于KPLS模型的间歇过程产品质量控制

针对间歇过程所具有的非线性特性,提出了一种基于核偏最小二乘(KPLS)模型的最终产品质量控制策略。利用初始条件、批次展开后的过程数据以及最终产品质量建立了间歇过程的KPLS模型;采用基于主成分分析(PCA)映射的预估方法对未知的过程数据进行补充,实现了最终产品质量的在线预测。为了解决最终产品质量的控制,利用T2统计量确定KPLS模型的适用范围,并作为约束引入产品质量控制问题,提高控制策略的可行性;采用粒子群优化(PSO)算法实现了优化问题的高效求解。仿真结果表明,与基于偏最小二乘(PLS)模型的控制策略相比,所提出的方法具有更高的预测精度,且能有效解决产品质量控制中出现的各种问题。     

Globally stable control systems for processes with input multiplicities

A nonlinear process with input multiplicity has two or more input values for a given output at the steady state, and the process steady state gain changes its sign as the operating point changes. A control system with integral action will be unstable when both signs of the process gain and the controller integral gain are different, and its stability region will be limited to the boundary where the process steady state gain is zero. Unlike processes with output multiplicities, feedback controllers cannot be used to correct the sign changes of process gain. To remove such stability limitation, a simple control system with parallel compensator is proposed. The parallel compensator can be easily designed based on the process steady state gain information and tuned in the field. Using the two time scale method, the stability of proposed control systems for processes with input multiplicities can be checked.     

低压湿式煤气储气柜的防腐

分析了湿式煤气储气柜的腐蚀类型．提出采用环氧橡胶煤沥青玻璃鳞片涂料作为内壁涂料．采用环氧树脂高氯化聚乙烯涂料作为外壁涂料，对气柜进行涂层防护。     

Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is in-troduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to re-trieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.     

在线压缩KECA的自适应算法在故障检测中的应用

在复杂的大规模工业过程系统中,实时过程监视、优化计算时间和降低运行内存是实现最终产品质量的最关键和最具挑战性的任务,提出一种在线压缩核熵成分分析（online reduced kernel entropy component analysis, ORKECA）的自适应故障检测算法。首先计算训练样本的核矩阵,根据保留的特征值与特征向量选择有代表性的观测值,构造一个符合全局数据信息特征的压缩集,计算监测统计数据的平方预测误差（squared prediction error, SPE）,并利用核密度估计确定控制限。对于在线实时采集的数据,计算该数据的统计量并与压缩集的控制限比较,根据过程状态分析核熵成分分析（kernel entropy component analysis, KECA）模型是否需要进行更新,可以有效提高实时监测过程数据的性能。最后,以一个非线性数值案例及TE过程数据对该方法进行仿真数值分析。结果表明,所提的方法具有有效的可行性。     

Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models

Linear models can be inappropriate when dealing with nonlinear and multimode processes, leading to a soft sensor with poor performance. Due to time-varying process behaviour it is necessary to derive and implement some kind of adaptation mechanism in order to keep the soft sensor performance at a desired level. Therefore, an adaptation mechanism for a soft sensor based on a mixture of Gaussian process regression models is proposed in this paper. A procedure for input variable selection based on mutual information is also presented. This procedure selects the most important input variables for output variable prediction, thus simplifying model development and adaptation. Apart from online prediction of the difficult-to-measure variable, this soft sensor can be used for adaptive process monitoring. The efficiency of the proposed method is benchmarked with the commonly applied recursive PLS and recursive PCA method on the Tennessee Eastman process and two real industrial examples.     

Optimal control of batch processes using particle swam optimisation with stacked neural network models

An optimal control strategy for batch processes using particle swam optimisation (PSO) and stacked neural networks is presented in this paper. Stacked neural network models are developed form historical process operation data. Stacked neural networks are used to improve model generalisation capability, as well as provide model prediction confidence bounds. In order to improve the reliability of the calculated optimal control policy, an additional term is introduced in the optimisation objective function to penalize wide model prediction confidence bounds. The optimisation problem is solved using PSO, which can cope with multiple local minima and could generally find the global minimum. Application to a simulated fed-batch process demonstrates that the proposed technique is very effective.     

基于多尺度分析与主元分析的聚丙烯熔融指数神经元软测量预报模型

Prediction of melt index （MI）, the most important parameter in determining the product＇s grade and quality control of polypropylene produced in practical industrial processes, is studied. A novel soft-sensor model with principal component analysis （PCA）, radial basis function （RBF） networks, and multi-scale analysis （MSA） is proposed to infer the MI of manufactured products from real process variables, where PCA is carried out to select the most relevant process features and to eliminate the correlations of the input variables, MSA is introduced to a~quire much more information and to reduce the uncertainty of the system, and RBF networks are used to characterize the nonlinearity of the process. The research results show that the proposed method provides promising prediction reliability and accuracy, and supposed to have extensive application prospects in propylene polymerization processes.     

基于多尺度核JYMKPLS迁移模型的间歇过程产品质量的在线预测方法

针对过程数据不足,且具有强非线性和多尺度特性的新间歇过程,结合迁移学习方法与多尺度核学习方法的优势,提出了一种基于多尺度核JYMKPLS（Joint-Y multi-scale kernel partial least squares）迁移模型的间歇过程产品质量在线预测方法。该方法首先通过迁移学习利用相似源域的旧过程数据提高新间歇过程建模效率和质量预测的精度。然后,针对间歇过程数据的非线性和多尺度特性问题,引入了多尺度核函数以更好地拟合数据变化的趋势,从而提高模型的预测精度。此外,提出模型在线更新和数据剔除,通过在线持续改善迁移模型对新间歇过程的匹配程度,以消除相似过程间的差异性给迁移学习带来的不利影响,从而不断地提升预测精度。最后,通过仿真验证了所提方法的有效性,结果表明,与传统的数据驱动建模方法相比,本文所提方法能够有效提高建模效率和预测精度。     

基于IDPC-RVM的多模态间歇过程质量变量在线预测

间歇过程具有多模态特性,现有的间歇过程模态划分方法中过程数据高维特征和模态中心的选取直接影响模态划分结果的合理性,进而影响间歇过程质量变量在线预测的精度。为提高间歇过程质量变量在线预测的精度,提出了一种基于改进密度峰值聚类相关向量机(improved density peaks clustering-relevance vector machine,IDPC-RVM)的间歇过程质量变量在线预测方法。首先,在密度峰值聚类算法基础上,考虑过程数据的高维特征进行样本相似性度量,并通过样本密度不平衡下的模态中心选取策略准确获取间歇过程模态中心;其次,利用模态划分指标在无须先验知识的情况下获取间歇过程最优模态数目,并识别过渡模态完成间歇过程的模态划分;最后,建立各模态数据的RVM预测模型,实现间歇过程质量变量的在线预测。青霉素发酵过程的实验结果表明,与RVM、SCFCM-RVM和DPC-RVM方法相比,对青霉素浓度预测的均方根误差(RMSE)降低至0.0093,判定系数(R²)提升至0.9995,有效地提高了预测精度。     

大型系统控制结构设计的交互分析和分解原理简

Industrial processes are mostly large-scale systems with high order. They use fully centralized control strategy, the parameters of which are difficult to tune. In the design of large-scale systems, the decomposition according to the interaction between input and output variables is the first step and the basis for the selection of control structure. In this paper, the decomposition principle of processes in large-scale systems is proposed for the design of control structure. A new variable pairing method is presented, co_nsidering the steady-state information anddynamic response of large-scale system. By selecting threshold values, the related matrix can be transformed into the adjoining matrixes, which directly measure the couple among different loops. The optimal number of controllers can be obtained after decomposing the large-scale system. A practical example is used to demonstrate the validity and feasibility of the proposed interaction decomposition principle in process large-scale systems.     

基于IDPC-RVM的多模态间歇过程质量变量在线预测

间歇过程具有多模态特性,现有的间歇过程模态划分方法中过程数据高维特征和模态中心的选取直接影响模态划分结果的合理性,进而影响间歇过程质量变量在线预测的精度。为提高间歇过程质量变量在线预测的精度,提出了一种基于改进密度峰值聚类相关向量机(improved density peaks clustering-relevance vector machine,IDPC-RVM)的间歇过程质量变量在线预测方法。首先,在密度峰值聚类算法基础上,考虑过程数据的高维特征进行样本相似性度量,并通过样本密度不平衡下的模态中心选取策略准确获取间歇过程模态中心;其次,利用模态划分指标在无须先验知识的情况下获取间歇过程最优模态数目,并识别过渡模态完成间歇过程的模态划分;最后,建立各模态数据的RVM预测模型,实现间歇过程质量变量的在线预测。青霉素发酵过程的实验结果表明,与RVM、SCFCM-RVM和DPC-RVM方法相比,对青霉素浓度预测的均方根误差(RMSE)降低至0.0093,判定系数(R²)提升至0.9995,有效地提高了预测精度。     

基于时段过渡分析的多时段间歇过程质量预测(英文)

Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information. However, few of them consider phase transitions, though they exit widely in batch processes and have non-ignorable impacts on product qualities. In the present work, a phase-based partial least squares (PLS) method utilizing transition information is proposed to give both online and offline quality predictions. First, batch processes are divided into several phases using regression parameters other than prior process knowledge. Then both steady phases and transitions which have great influences on qualities are identified as critical-to-quality phases using statistical methods. Finally, based on the analysis of different characteristics of transitions and steady phases, an integrated algorithm is developed for quality prediction. The application to an injection molding process shows the effectiveness of the proposed algorithm in comparison with the traditional MPLS method and the phase-based PLS method.     

Batch-to-batch optimal control of a batch polymerisation process based on stacked neural network models

A neural network based batch-to-batch optimal control strategy is proposed in this paper. In order to overcome the difficulty in developing mechanistic models for batch processes, stacked neural network models are developed from process operational data. Stacked neural networks have enhanced model generalisation capability and can also provide model prediction confidence bounds. However, the optimal control policy calculated based on a neural network model may not be optimal when applied to the true process due to model plant mismatches and the presence of unknown disturbances. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch-to-batch optimal control strategy based on the linearisation of stacked neural network model is proposed in this paper. Applications to a simulated batch polymerisation reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model plant mismatches and unknown disturbances.     

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety. Small or incipient irregularities may lead to severe degradation in complex chemical processes, and the conventional process monitoring techniques cannot detect these irregularities. In this study to improve the performance of monitoring, an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis (MSPCA) with cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts. The new Hotelling's T² and square prediction error (SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data. The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods. The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults. Moreover, a comparative study shows that the SPE-EWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.