Droplets diameter distribution using maximum entropy formulation combined with a new energy-based sub-model

The maximum entropy principle (MEP) is one of the first methods which have been used to predict droplet size and velocity distributions of liquid sprays. This method needs a mean droplets diameter as an input to predict the droplet size distribution. This paper presents a new sub-model based on the deterministic aspects of liquid atom-ization process independent of the experimental data to provide the mean droplets diameter for using in the maximum entropy formulation (MEF). For this purpose, a theoretical model based on the approach of energy conservation law entitled energy-based model (EBM) is presented. Based on this approach, atomization occurs due to the kinetic energy loss. Prediction of the combined model (MEF/EBM) is in good agreement with the avail-able experimental data. The energy-based model can be used as a fast and reliable enough model to obtain a good estimation of the mean droplets diameter of a spray and the combined model (MEF/EBM) can be used to wel predict the droplet size distribution at the primary breakup.     

基于FCMISVM的自适应软测量建模算法及其在PX吸附分离过程中的应用

To overcome the problem that soft sensor models cannot be updated with the process changes, a soft sensor modeling algorithm based on hybrid fuzzy c-means (FCM) algorithm and incremental support vector machines (ISVM) is proposed. This hybrid algorithm FCMISVM includes three parts: samples clustering based on FCM algorithm, learning algorithm based on ISVM, and heuristic sample displacement method. In the training process, the training samples are first clustered by the FCM algorithm, and then by training each clustering with the SVM algorithm, a sub-model is built to each clustering. In the predicting process, when an incremental sample that represents new operation information is introduced in the model, the fuzzy membership function of the sample to each clustering is first computed by the FCM algorithm. Then, a corresponding SVM sub-model of the clustering with the largest fuzzy membership function is used to predict and perform incremental learning so the model can be updated on-line. An old sample chosen by heuristic sample displacement method is then discarded from the sub-model to control the size of the working set. The proposed method is applied to predict the p-xylene (PX) purity in the adsorption separation process. Simulation results indicate that the proposed method actually increases the model’s adaptive abilities to various operation conditions and improves its generalization capability.     

A local and global statistics pattern analysis method and its application to process fault identification☆

Traditional principal component analysis (PCA) is a second-order method and lacks the ability to provide higher-order representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incor-porated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality pre-serving projections within the PCA, is proposed to utilize various statistics and preserve both local and global in-formation in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simula-tion results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.     

基于ARX-NNPLS模型的优化策略及在聚合物牌号切换过程中的应用(英文)

Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.     

基于模糊CMAC神经网络的4-CBA软测量

Soft sensor is attractive in dealing with online product quality measurement by virtue of other easily measured variables. In AMOCO PTA (purified terephthalic acid) production process, the unavailability of real-time measurement of 4-CBA makes it impossible for timely adjustment and thereby influences the product quality and the plant economy benefit. In this paper, a kind of FCMAC (fuzzy cerebellar model articulation controller) method is presented to solve the online measurement problem. Different from the conventional CMAC (cerebellar model articulation controller) networks, which has inferior smoothing ability because of its table look-up based technology. Integrating fuzzy model into CMAC networks, it becomes more accurate in functional mapping without weakening its generalization ability. Numerical example and industrial application results show the method proposed here is satisfactory and feasible.     

基于混合建模技术的复合肥养分含量MIMO软测量模型

In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.     

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.     

基于结合参数整定和特征选择策略的模糊支持向量机的故障诊断

This study describes a classification methodology based on support vector machines （SVMs）, which offer superior classification performance for fault diagnosis in chemical process engineering. The method incorporates an efficient parameter tuning procedure （based on minimization of radius/margin bound for SVM＇s leave-one-out errors） into a multi-class classification strategy using a fuzzy decision factor, which is named fuzzy support vector machine （FSVM）. The datasets generated from the Tennessee Eastman process （TEP） simulator were used to evaluate the classification performance. To decrease the negative influence of the auto-correlated and irrelevant variables, a key variable identification procedure using recursive feature elimination, based on the SVM is implemented, with time lags incorporated, before every classifier is trained, and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation. Performance comparisons are implemented among several kinds of multi-class decision machines, by which the effectiveness of the proposed approach is proved.     

A data-derived soft-sensor method for monitoring effluent total phosphorus

The effluent total phosphorus(ETP)is an important parameter to evaluate the performance of wastewater treatment process(WWTP).In this study,a novel method,using a data-derived soft-sensor method,is proposed to obtain the reliable values of ETP online.First,a partial least square(PLS)method is introduced to select the related secondary variables of ETP based on the experimental data.Second,a radial basis function neural network (RBFNN)is developed to identify the relationship between the related secondary variables and ETP.This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP.Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.     

基于区间二型模糊神经网络的出水氨氮软测量

针对污水处理过程出水氨氮（ammonia nitrogen,NH₄-N）难以实时检测的问题,提出了一种基于区间二型模糊神经网络（interval type-2 fuzzy neural networks,IT2FNN）的软测量方法,建立了出水NH₄-N的软测量模型,实现了出水NH₄-N的实时检测。首先,采集和预处理相关过程变量的实际运行数据,通过主元分析法筛选出与出水NH₄-N相关性较强的过程变量。其次,利用IT2FNN建立所选变量与出水NH₄-N的软测量模型,通过梯度下降算法对模型相关参数进行修正。最后,将基于IT2FNN的出水NH₄-N软测量模型应用于实际污水处理过程。实验结果表明,提出的出水NH₄-N软测量方法不仅能够实现污水处理过程出水NH₄-N的实时检测,而且具有较高的检测精度。     

PX氧化反应器动态模型和4-CBA浓度的软测量

建立了PX氧化反应器的动态机理模型,模型能够较好地预测不同工艺条件下的4-CBA浓度。基于上述模型,对现有工艺过程进行了数值模拟,发现4-CBA浓度与尾气CO2浓度具有很强的相关性。通过数据回归得到了简化的4-CBA软测量模型,上述模型为现有PX氧化过程的4-CBA浓度实时控制提供了理论依据。     

PTA生产中4-CBA浓度的影响因素分析和软测量

根据济南化纤公司75 kt／a精对苯二甲酸(PTA)的生产工艺,建立了对二甲苯(PX)氧化反应器的数学模型,模型能够较好地预测不同工艺条件下的对羧基苯甲醛(4-CBA)浓度。在此模型基础上,对现有工艺过程进行了数值模拟,结果发现,4-CBA浓度与尾气CO_2浓度和单位液相体积耗氧速率具有很强的相关性。通过数据回归得到了4-CBA软测量模型,该软测量模型结果与工业运行实测结果基本一致     

基于证据框架的最小二乘支持向量机在精对苯二甲酸生产中的应用

支持向量机是一种基于统计学习理论的新型机器学习方法.本文给出一种考虑损失函数的噪声模型参数β的贝叶斯证据框架最小二乘支持向量机回归算法,通过贝叶斯证据框架自动调整正则化参数和核参数,更好地实现了最小化误差和模型复杂性之间的折中.将提出的算法用于精对苯二甲酸（purified terephthalic acid,PTA）生产过程中的关键指标对羧基苯甲醛（4-carboxybenzaldhyde,4-CBA）含量的预测中,能很好地跟踪4-CBA含量的变化趋势,泛化能力较强,为4-CBA含量的实时预测提供了很好的解决方案.     

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process (WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous (TP) and ammonia nitrogen (NH₄-N). In this intelligent monitoring system, a fuzzy neural network (FNN) is applied for designing the soft sensor model, and a principal component analysis (PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition (SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.     

基于模糊神经网络的污水处理生化除磷过程控制

针对污水处理生化除磷过程中出水总磷难以实时达标的问题,提出了一种基于模糊神经网络(fuzzy neural network,FNN)的出水总磷控制方法。首先,通过分析污水处理生化除磷机理,确定了控制器的操作变量为生化反应池第五分区外部碳源(external carbon, EC)与溶解氧(dissolved oxygen, DO)传递系数。其次,设计了一种基于FNN的出水总磷控制器,采用梯度下降算法更新控制器参数;最后,将基于FNN的出水总磷控制器应用于污水处理过程基准仿真平台(benchmark simulation model No.1,BSM1),实验结果表明,基于FNN的出水总磷控制器能够保证出水总磷的达标排放,具有较好的控制效果。     

基于D-FNN的聚合过程转化速率软测量建模及重构

引言以氯乙烯单体(VCM)为原料,采用悬浮法聚合工艺生产聚氯乙烯(PVC)树脂是一种典型的间歇式化工生产过程。VCM的转化率对PVC树脂产品质量有很大影响,不同转化率时对PVC     

基于区间二型模糊神经网络污水处理过程溶解氧浓度控制

针对城市污水处理过程溶解氧浓度难以精确控制的问题,提出了一种基于区间二型模糊神经网络（interval type-2 fuzzy neural networks,IT2FNN）的溶解氧浓度控制方法。先将IT2FNN应用在城市污水处理过程溶解氧浓度控制器的设计,获得了一种IT2FNN溶解氧浓度控制器。后采用自适应学习算法在线调整控制器的参数,提高了控制器的自适应能力。最后将提出的IT2FNN溶解氧浓度控制器应用于基准仿真2号模型（benchmark simulation model no.2,BSM2）平台,结果表明,IT2FNN控制器能够实现第5分区溶解氧浓度精确控制,具有较好的控制效果。