Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression (GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression (GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.     

Application of support vector regression for developing soft sensors for nonlinear processes

The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input–output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. The main feature of the algorithm is the use of a nonlinear kernel transformation to map the input variables into a feature space so that their relationship with the output variable becomes linear in the transformed space. This method has excellent generalisation capabilities to high‐dimensional nonlinear problems due to the use of functions such as the radial basis functions which have good approximation capabilities as kernels. Another attractive feature of the method is its convex optimization formulation which eradicates the problem of local minima while identifying the nonlinear models. In this work, we demonstrate the application of SVR as an efficient and easy‐to‐use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady‐state Melt Index soft sensor for an industrial scale ethylene vinyl acetate (EVA) polymer extrusion process using SVR. The SVR‐based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least‐square‐based soft sensor in terms of lower prediction errors. In the remaining two other case studies, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for two nonlinear processes: a simulated pH neutralisation process and a laboratory scale twin screw polymer extrusion process. A heuristic procedure is proposed for developing a dynamic nonlinear‐ARX model‐based soft sensor using SVR, in which the optimal delay and orders are automatically arrived at using the input–output data.     

Multi-layer features ensemble soft sensor regression model based on stacked autoencoder and vine copula

It is crucial in industrial processes to consider key variables to ensure safe operation and high product quality. Moreover, these variables are difficult to obtain using traditional measurement methods; hence, it makes sense to develop soft sensor regression models to process the variable prediction. However, there are numerous variables integrating noisy and redundant information in complex industrial processes. Using such variables in traditional regression models may result in reducing the model's efficiency and performance. Thus, this paper proposes a multi-layer feature ensemble soft sensor regression method using a stacked auto-encoder (SAE) and vine copula (ESAE–VCR) to address these problems. To do so, the number of neurons in the hidden layer of the SAE is determined by the principal component analysis (PCA). The multi-layer features of the process variables are extracted using a stacked AE, and the regression models are established for each feature layer. A linear regression ensemble method is used to combine the regression models with the multi-layer features to obtain the final predictive model that will estimate the values of the key variables. The effectiveness and practicality of the ESAE–VCR are validated by comparing them with several common soft measurement methods in two examples. In the numerical example, the ESAE–VCR yields an accuracy of prediction (R²) of 0.9898 and a root mean square error (RMSE) of 0.1804. In the industrial example, the ESAE–VCR yields an R² of 0.9908 and an RMSE of 0.1205.     

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.     

Dynamic nonlinear soft sensor modelling method using linear slow feature analysis and least squares support vector regression for batch processes

Data-driven soft sensor models have been extensively utilized in industrial processes. Batch processes are usually employed to manufacture low-volume and high value-added products in chemical, materials, and pharmaceutical industries. The most distinctive features of batch process lie in nonlinear, repetition, and slow time varying characteristics. In this paper, a data-driven soft sensor modelling method based on linear slow feature analysis (LSFA) and least squares support vector regression (LSSVR) is proposed. In this method, LSFA was used to effectively capture the driving force behind the data features that change as slowly as possible. Then, a LSSVR model was constructed between the extracted slow feature variables and quality variables. Finally, a numerical example, industrial penicillin fermentation processes, and cobalt oxalate synthesis process were utilized to confirm the prediction accuracy and model reliability of the proposed approach.     

Ensemble Correntropy‐Based Mooney Viscosity Prediction Model for an Industrial Rubber Mixing Process

The development of accurate soft sensors for online prediction of Mooney viscosities in industrial rubber mixing processes is a difficult task because the modeling dataset often contains various outliers. A correntropy kernel learning (CKL) method for robust soft sensor modeling of nonlinear industrial processes with outlier samples is proposed. Simultaneously, the candidate outliers can be identified once the CKL‐based soft sensor model is built. An index for describing the uncertainty of the CKL model is designed. Furthermore, to obtain more robust and accurate predictions, an ensemble CKL (ECKL) method is formulated by introducing the simple bagging strategy. Consequently, by detecting the outliers in a sequential manner, the database becomes more reliable for long‐term use. The application results for the industrial rubber mixing process demonstrate the superiority of ECKL in terms of better prediction performance.     

基于改进的快速搜索聚类算法和高斯过程回归的催化重整脱氯前氢气纯度多模型建模方法

氢气是催化重整反应的重要副产物之一,建立氢气纯度软测量模型有助于指导生产。针对催化重整过程工况复杂多变、单一软测量模型难以满足精度要求,提出了一种基于改进的快速搜索聚类算法和高斯过程回归的多模型软测量建模方法。首先,针对快速搜索聚类算法中截断距离是由人为设定的问题,提出了一种截断距离确定方法。并用该改进算法对历史数据进行自动分类,建立各个数据子集的高斯过程回归模型,使各子模型在最大程度上反映不同工况点。然后,针对聚类后得到的带有类别标签的历史数据,建立类别辨识模型,与各子模型相结合,形成开关模式的组合模型。最后,将该建模方法应用于连续催化重整装置,建立了脱氯前氢气纯度的在线计算模型。结果表明,该多模型建模方法具有较高的预测精度,优于传统的单一模型,有一定的实用价值。     

Application of online support vector regression for soft sensors

Soft sensors have been widely used in chemical plants to estimate process variables that are difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes in state of chemical plants. Characteristics of adaptive soft sensor models such as moving window models, just‐in‐time models and time difference models were previously discussed. The predictive accuracy of any traditional models decreases when sudden changes in processes occur. Therefore, a new soft sensor method based on online support vector regression (SVR) and the time variable was developed for constructing soft sensor models adaptive to rapid changes of relationships among process variables. A nonlinear SVR model with the time variable is updated with the most recent data. The proposed method was applied to simulation data and real industrial data, and achieved higher predictive accuracy than traditional ones even when time‐varying changes in process characteristics happen. © 2013 American Institute of Chemical Engineers AIChE J 60: 600–612, 2014     

应用多神经网络建立动态软测量模型

引　言由于神经网络具有强大的逼近非线性函数的能力 ,因此用神经网络来建立软测量模型是目前被广泛采用的一种方法 .应用最多的是多层前向传播网络 (MFNN)和径向基函数网络 (RBF) .这些网络是静态网络 ,建模所需样本是与时间无关的离散数据 ,这样获得的模型称为软测量静态模型     

基于智能优化算法的软测量模型建模样本优选及应用

根据目标工况合理选择训练样本,是建立软测量模型的关键。传统的训练集样本选择方法难以充分利用因变量信息,而且难以综合考虑样本对模型的影响。为了解决上述问题,本文提出一种基于智能优化算法的训练集样本选择模型,定义了损失函数和样本压缩率,通过权重因子将二者融合为多目标适应度函数,可调整优化算法的寻优方向,使算法能够同时对建模样本组合结构与样本数量寻优,因此极大提高了所选建模样本的质量。为了验证方法的有效性,以汽油调和过程中采集的汽油近红外光谱-研究法辛烷值数据以及柴油近红外基准数据为例,与偏最小二乘、局部权重偏最小二乘等多种方法进行了比较研究,并分析了建模样本对软测量模型的影响。结果表明,本文方法在大规模降低训练集样本规模的同时能够保证软测量模型的精度和泛化性,非常适合工业应用。     

基于改进随机梯度Boosting算法的软测量建模

在建立复杂化工过程软测量模型时,使用传统的随机梯度Boosting算法（SGB）建模若收缩参数v选取不当会明显降低算法收敛速度,且极易陷入过拟合,难以取得令人满意的泛化效果。为解决这一问题,提出了一种基于SGB集成学习的软测量建模方法,采用高斯过程回归作为基学习器,并针对SGB算法固有的不足,依据每一次迭代中弱学习机的反馈,自适应调整收缩参数v,改善了SGB算法的过度拟合,从而提高了集成模型的估计精度与学习效率。将该方法应用于某双酚A装置的软测量建模中,仿真结果表明,相比于传统SGB建模,该方法具有更高的泛化性能和学习效率。     

A neural network-based soft sensor for particle size distribution using image analysis

Many industrial processes require on-line measurement of particle size and particle size distribution for process monitoring and control. The available techniques for reliable on-line measurement are, however, limited. In this paper, based on the captured surface images of randomly disarranged ore particles, the image uniformity was characterized. Particle size distribution was then investigated by applying a neural network-based modeling with the obtained image uniformity. The proposed soft sensor provides an improved prediction model and can be used for real time measurement of particle size distribution in the industrial operations.     

Deep learning for quality prediction of nonlinear dynamic processes with variable attention-based long short-term memory network

Industrial processes are often characterized with high nonlinearities and dynamics. For soft sensor modelling, it is important to model the nonlinear and dynamic relationship between input and output data. Thus, long short-term memory (LSTM) networks are suitable for quality prediction of soft sensor modelling. However, they do not consider the relevance of different input variables with the quality variable. To address this issue, a variable attention-based long short-term memory (VA-LSTM) network is proposed for soft sensing in this paper. In VA-LSTM, variable attention is designed to identify important input variables according to their relevance with quality prediction. After that, different attention weights are calculated and assigned to further obtain a weighted input sample at each time step. Finally, the LSTM network is exploited to capture the long-term dependencies of the weighted input time series to predict the quality variable. The performance of the proposed modelling method is validated on an industrial debutanizer column and a hydrocracking process.     

基于时间差分和局部加权偏最小二乘算法的过程自适应软测量建模

工业过程软测量模型常常因为过程的变量漂移、非线性和时变等问题而使得预测性能下降。因此,时间差分已被应用于解决过程变量漂移问题。但是,时间差分框架下的全局模型往往不能很好地描述过程非线性和时变等特性。为此,提出了一种融合时间差分模型和局部加权偏最小二乘算法的自适应软测量建模方法。时间差分模型可以大大减少过程变量漂移的影响,而局部加权偏最小二乘算法作为一种即时学习方法,可以有效解决过程非线性和时变问题。该方法的有效性在数值例子和工业过程实例中得到了有效验证。     

基于PLS快速剪枝法的RBF神经网络软测量模型建模方法和应用

提出一种从RBF神经网络隐含层的输出信息出发，通过PLS快速剪枝法，一次性剪去多余节点，生成最优规模的数学解析模型的方法。并用该方法建立了某化工企业精对苯二甲酸(PTA)晶体平均粒径的软测量模型，针对实际对象进行仿真研究，结果表明，该方法计算速度快，建立的模型精度高，适合实际工程应用的需求。     

PLS回归软测量方法在催化重整稳定油组分估计中的应用

提出扰动分类法和线性部分最小二乘（PLS）回归相结合的建立软测量模型的方法，并将它用于催化重整稳定油组分的估计中。仿真结果表明扰动分类法和PLS回归相结合建立的软测量模型简单、实用。     

A novel Bayesian inference soft sensor for real‐time statistic learning modeling for industrial polypropylene melt index prediction

A novel real‐time soft sensor based on a sparse Bayesian probabilistic inference framework is proposed for the prediction of melt index in industrial polypropylene process. The Bayesian framework consists of a relevance vector machine for predicting melt index and a particle filtering algorithm for soft sensor optimization. An online correcting strategy is also developed for improving the performance of real‐time melt index prediction. The method takes advantages of the probabilistic inference and using prior statistical knowledge of polymerization process. Developed soft sensors are validated with ten public databases from UCI machine learning repository and real data from industrial polypropylene process. Experimental results indicate the effectiveness of proposed method and show the improvement in both prediction precision and generalization capability compared with the reported models in literatures. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45384.     

高斯过程及其在软测量建模中的应用

结合工业萘初馏塔关键质量指标估计问题,提出了采用高斯过程（GP）建立复杂工业过程软测量方法。将自动相关确定（ARD）原理与GP模型结合进行软测量模型辅助变量选择,通过建立GP软测量模型,同时得到关键质量指标估计值和相应的预测不确定度,有效解决了现有软测量建模方法不能给出估计值的测量不确定度的问题。研究表明,GP软测量模型不仅能自动选择辅助变量,而且还具有较高的估计精度和较小的测量不确定度,能够更好地满足工业现场对测量可靠性的要求。     

Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples

Most traditional soft sensors are built upon the labeled dataset that contains equal numbers of input and output data samples. However, the output variables that correspond to quality variables and other important controlled variables are always difficult to obtain in chemical processes. Therefore, we may only obtain the output data for a small portion of the whole dataset and have much more input data samples. In this article, a semisupervised method is proposed for soft sensor modeling, which can successfully incorporate the unlabeled data information. To determine the effective dimensionality of the latent space, the Bayesian regularization method is introduced into the semisupervised model structure. Two industrial application case studies are provided to evaluate the feasibility and efficiency of the newly developed probabilistic soft sensor. © 2010 American Institute of Chemical Engineers AIChE J, 2011