Prediction of the Melt Index Based on the Relevance Vector Machine with Modified Particle Swarm Optimization

A novel chemical soft‐sensor approach for the prediction of the melt index (MI) in the propylene polymerization industry is presented. The MI is considered as one of the important variables of quality that determine the product specifications. Thus, a reliable estimation of the MI is crucial in quality control. An accurate optimal predictive model of MI values with the relevance vector machine (RVM) is proposed, where the RVM is employed to build the MI prediction model; a modified particle swarm optimization (MPSO) algorithm is then introduced to optimize the parameter of the RVM, and the MPSO‐RVM model is thereby developed. An online correcting strategy (OCS) is further carried out to update the modeling data and to revise the model's parameter self‐adaptively whenever model mismatch happens. Based on the data from a real polypropylene production plant, a detailed comparison is carried out among the least squares support vector machine (LS‐SVM), RVM, MPSO‐RVM, and OCS‐MPSO‐RVM models. The research results reveal the prediction accuracy and validity of the proposed approach.     

免疫PSO_WLSSVM最优聚丙烯熔融指数预报

熔融指数（MI）是聚丙烯生产的重要指标,建立可靠的熔融指数预报模型非常重要。针对标准粒子群算法(PSO)在迭代过程中易出现粒子过早收敛而陷入局部最优的缺陷,通过引入免疫系统的抗体选择机制,构造了一种基于免疫机制的免疫粒子群优化算法(ICPSO),来保持更新粒子的多样性,从而克服标准粒子群算法过早收敛的缺陷;然后利用ICPSO方法对鲁棒最小二乘支持向量机预报模型(WLSSVM)进行参数寻优,得到最优的ICPSO_WLSSVM预报模型。以实际聚丙烯生产的熔融指数预报作为实例进行研究,结果表明所提出的ICPSO_WLSSVM模型的有效性和良好的预报精度。     

Quality control in the polypropylene manufacturing process: An efficient,data‐driven approach

In the propylene polymerization process, the melt index (MI), as a critical quality variable in determining the product specification, cannot be measured in real time. What we already know is that MI is influenced by a large number of process variables, such as the process temperature, pressure, and level of liquid, and a large amount of their data are routinely recorded by the distributed control system. An alternative data‐driven model was explored to online predict the MI, where the least squares support vector machine was responsible for establishing the complicated nonlinear relationship between the difficult‐to‐measure quality variable MI and those easy‐to‐measure process variables, whereas the independent component analysis and particle swarm optimization technique were structurally integrated into the model to tune the best values of the model parameters. Furthermore, an online correction strategy was specially devised to update the modeling data and adjust the model configuration parameters via adaptive behavior. The effectiveness of the designed data‐driven approach was illustrated by the inference of the MI in a real polypropylene manufacturing plant, and we achieved a root mean square error of 0.0320 and a standard deviation of 0.0288 on the testing dataset. This proved the good prediction accuracy and validity of the proposed data‐driven approach. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41312.     

基于GMC(1,N)的聚丙烯熔融指数预报

引言聚丙烯是以丙烯单体为主聚合而成的一种合成树脂,是五大通用塑料之一,是塑料工业中的重要原料。世界丙烯的50%、我国丙烯的65%都用来生产聚丙烯。熔融指数(简称MI)是在一定温度、一定压力、一定负荷下,熔体在10min内通过标     

基于支持向量机的发酵过程生物量在线估计

Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine （SVM） for an on-line biomass estimation was analyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.     

Melt index prediction based on fuzzy neural networks and PSO algorithm with online correction strategy

A black‐box modeling scheme to predict melt index (MI) in the industrial propylene polymerization process is presented. MI is one of the most important quality variables determining product specification, and is influenced by a large number of process variables. Considering it is costly and time consuming to measure MI in laboratory, a much cheaper and faster statistical modeling method is presented here to predicting MI online, which involves technologies of fuzzy neural network, particle swarm optimization (PSO) algorithm, and online correction strategy (OCS). The learning efficiency and prediction precision of the proposed model are checked based on real plant history data, and the comparison between different learning algorithms is carried out in detail to reveal the advantage of the proposed best‐neighbor PSO (BNPSO) algorithm with OCS. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine

In propylene polymerization (PP) process, the melt index (MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of MI is essential for efficient and professional monitoring and control of practical PP processes. This paper presents a novel soft sensor based on extreme learning machine (ELM) and modified gravitational search algorithm (MGSA) to estimate MI from real PP process variables, where the MGSA algorithm is developed to find the best parameters of input weights and hidden biases for ELM. As the comparative basis, the models of ELM, APSO-ELM and GSAELM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. The research results show the accuracy and universality of the proposed model and it can be a powerful tool for online MI prediction.     

基于LS-SVM的流向变换催化燃烧反应器的温度预测

用最小二乘支持向量机建模方法对神经网络建立的流向变换催化燃烧反应器拟定态温度分布的模型进行改进,克服了神经网络局部最小和过拟合的问题,同时最小二乘支持向量机在训练过程中所需的训练样本比神经网络大大减少,使得试验成本大大降低,工业化进程加快。仿真结果表明支持向量机建立的模型简单,精度高,满足建模的精度要求,比神经网络模型耗时少。     

Melt index prediction by RBF neural network optimized with an adaptive new ant colony optimization algorithm

Melt index (MI) is a crucial indicator in determining the product specifications and grades of polypropylene (PP). The prediction of MI, which is important in quality control of the PP polymerization process, is studied in this work. Based on RBF (radial basis function) neural network, a soft‐sensor model (RBF model) of the PP process is developed to infer the MI of PP from a bunch of process variables. Considering that the PP process is too complicated for the RBF neural network with a general set of parameters, a new ant colony optimization (ACO) algorithm, N‐ACO, and its adaptive version, A‐N‐ACO, which aim at continuous optimizing problems are proposed to optimize the structure parameters of the RBF neural network, respectively, and the structure‐best models, N‐ACO‐RBF model and A‐N‐ACO‐RBF model for the MI prediction of propylene polymerization process, are presented then. Based on the data from a real PP production plant, a detailed comparison research among the models is carried out. The research results confirm the prediction accuracy of the models and also prove the effectiveness of proposed N‐ACO and A‐N‐ACO optimization approaches in solving continuous optimizing problem. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

丙烯聚合反应动态模拟

从聚合反应动力学出发,根据反应系统内物料平衡和能量平衡原理,建立环管式丙烯聚合反应器动态数学模型,并采用机理模型与经验模型相结合的方法给出聚丙烯熔融指数推理模型。使用建立的数学模型,对反应器进行仿真试验,考察操作条件的变化对反应温度、浆液密度和产品熔融指数等产生的影响。采用该数学模型,可以准确地预测出操作条件的变化对产品质量的影响,对实际生产过程中产品质量的调整与控制具有指导意义。     

Intelligent fitting of minimum spout‐fluidised velocity in spout‐fluidised bed

The experiments were carried on to study the minimum spout‐fluidised velocity in the spout‐fluidised bed. It was found that the minimum spout‐fluidised velocity increased with the rise of static bed height, spout nozzle diameter, particle density, particle diameter, fluidised gas velocity but decreased with the rise of carrier gas density. Based on the experiments, least square support vector machine (LS‐SVM) was established to predict the minimum spout‐fluidised velocity, and adaptive genetic algorithm and cross‐validation algorithm were used to determine the parameters in LS‐SVM. The prediction performance of LS‐SVM is better than that of the empirical correlations and neural network.     

基于支持向量机MPLS的间歇过程故障诊断方法

1 INTRODUCTION In batch or fed-batch processes, raw materials are converted to products within a finite duration. In prac- tical production, the process commonly exhibits large variations from batch to batch due to such influencing factors as the quality fluctuation of raw materials, de- fect of equipments, contaminations, and other unpre- dicted disturbances. These variations may have an adverse effect on the final product quantity and quality. But it is generally difficult to discern th…     

Explosive Random‐Coefficient AR(1) Processes and Related Asymptotics for Least‐Squares Estimation

Abstract. Large sample properties of the least‐squares and weighted least‐squares estimates of the autoregressive parameter of the explosive random‐coefficient AR(1) process are discussed. It is shown that, contrary to the standard AR(1) case, the least‐squares estimator is inconsistent whereas the weighted least‐squares estimator is consistent and asymptotically normal even when the error process is not necessarily Gaussian. Conditional asymptotics on the event that a certain limiting random variable is non‐zero is also discussed.     

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.     

The sparse least square support vector regression for estimating illumination chromaticity

The fusion of estimating algorithms for illumination chromaticity is an important strategy in computational color constancy and it has attracted the much attention of domestic and foreign scholars. Some effective approaches have been proposed to build the framework of the fusion, such as Committee‐based Color Constancy, BP neural network, and machine learning regression. In this article, the framework of least square support vector regression (LS‐SVR) is applied to illumination estimation fusion and provides a novel sparse method of LS‐SVR. According to the experience of previous research, the complexity in training LS‐SVR is obviously lower than it in training SVR, but due to all of the characters in images are chosen as the support vectors, the computation in LS‐SVR takes more time. In order to improve accuracy and reduce time consumption, this study uses sparse strategy that only weighted samples that illumination chromaticity of image have higher weight than other images can be selected as the support vectors. Training via a part of the images with high‐weight, the sparse LS‐SVR can achieve a satisfactory result. Experiment with real images shows that this sparse LS‐SVR method performs better than SVR, LS‐SVR and current some other color constancy algorithms especially in the aspects of computing speed and accuracy.     

Inferential sensors for estimation of polymer quality parameters: Industrial application of a PLS-based soft sensor for a LDPE plant

Low-density polyethylene (LDPE) and ethylene vinyl acetate (EVA) copolymers are produced in free radical polymerization using reactors at extremely high pressure. The reactors require constant monitoring and control in order to minimize undesirable process excursions and meet stringent product specifications. In industrial settings, polymer quality is mainly specified in terms of melt flow index (MI) and density. These properties are difficult to measure and usually unavailable in real time, which leads to major difficulty in controlling product quality in polymerization processes. Researchers have attempted first principles modeling of polymerization processes to estimate end use properties. However, development of detailed first principles model for free radical polymerization is not a trivial task. The difficulties involved are the large number of complex and simultaneous reactions and the need to estimate a large number of kinetic parameters. To overcome these difficulties, some researchers considered empirical neural network models as an alternative. However, neural network models provide no physical insight about the underlying process. We consider data-based multivariate regression methods as alternative solution to the problem. In this paper, some recent developments in modeling polymer quality parameters are reviewed, with emphasis given to the free radical polymerization process. We present an application of PLS to build a soft-sensor to predict melt flow index using routinely measured process variables. Issues of data acquisition and preprocessing for real industrial data are discussed. The study was conducted using data collected form an industrial autoclave reactor, which produces LDPE and EVA copolymer using free radical polymerization. The results indicated that melt index (MI) can be successfully predicted using this relatively straightforward statistical tool.     

丙烯精馏塔智能控制系统设计及应用

针对乙烯生产装置丙烯精馏塔的工艺特征和操作特点,利用支持向量机在小样本数据建模中的优势,提出一种基于支持向量机丙烯浓度软测量技术,解决了塔釜建模数据样本少的问题,实现了塔釜丙烯浓度在线测量。在上述软测量系统的基础上,设计了丙烯浓度智能控制系统。该系统采用模糊PID作为丙烯浓度控制器,其输出量作为灵敏板温度控制器的设定值,与灵敏板温度控制构成串级调节系统,同时为了克服进料量对灵敏板温度造成的干扰,设计了进料流量前馈控制器。丙烯浓度智能控制系统对塔釜丙烯指标进行实时控制,提高了塔釜丙烯浓度的控制平稳度,解决了塔釜丙烯浓度超标问题。现场应用效果表明,该丙烯浓度软测量系统测量精度高,控制系统可以有效控制塔釜丙烯浓度,取得了良好的控制效果,满足了工业现场运行的需要。     

Implementation of soft computing approaches for prediction of physicochemical properties of ionic liquid mixtures

The main objective of this study was to develop soft computing approaches for prediction of physicochemical properties of IL mixtures including: density, heat capacity, thermal conductivity, and surface tension. The proposed models in this study are based on support vector machine (SVM), least square support vector machines (LSSVM), and group method of data handling type polynomial neural network (GMDH-PNN) systems. To find the LSSVM and SVM adjustable parameters, genetic algorithm (GA) as a meta-heuristic algorithm was utilized. The results showed that LSSVM is more robust and reliable for prediction of physicochemical properties of IL mixtures. The proposed GA-LSSVM model provides average absolute relative deviations of 0.38%, 0.18%, 0.77% and 1.18% for density, heat capacity, thermal conductivity, and surface tension, respectively, which demonstrates high accuracy of the model for prediction of physicochemical properties of IL mixtures.     

Modelling the strip thickness in hot steel rolling mills using least‐squares support vector machines

The development and implementation of better control strategies to improve the overall performance of a plant is often hampered by the lack of available measurements of key quality variables. One way to resolve this problem is to develop a soft sensor that is capable of providing process information as often as necessary for control. One potential area for implementation is in a hot steel rolling mill, where the final strip thickness is the most important variable to consider. Difficulties with this approach include the fact that the data may not be available when needed or that different conditions (operating points) will produce different process conditions. In this paper, a soft sensor is developed for the hot steel rolling mill process using least‐squares support vector machines and a properly designed bias update term. It is shown that the system can handle multiple different operating conditions (different strip thickness setpoints, and input conditions).     

A support vector clustering‐based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data

A new support vector clustering (SVC)‐based probabilistic approach is developed for unsupervised chemical process monitoring and fault classification in this article. The spherical centers and radii of different clusters corresponding to normal and various kinds of faulty operations are estimated in the kernel feature space. Then the geometric distance of the monitored samples to different cluster centers and boundary support vectors are computed so that the distance–ratio–based probabilistic‐like index can be further defined. Thus, the most probable clusters can be assigned to the monitored samples for fault detection and classification. The proposed SVC monitoring approach is applied to two test scenarios in the Tennessee Eastman Chemical process and its results are compared to those of the conventional K‐nearest neighbor Fisher discriminant analysis (KNN‐FDA) and K‐nearest neighbor support vector machine (KNN‐SVM) methods. The result comparison demonstrates the superiority of the SVC‐based probabilistic approach over the traditional KNN‐FDA and KNN‐SVM methods in terms of fault detection and classification accuracies. © 2012 American Institute of Chemical Engineers AIChE J, 59: 407–419, 2013