A probabilistic self-validating soft-sensor with application to wastewater treatment

In the wastewater treatment plants (WWTPs), soft sensors are viewed as a simple signal estimator for hard-to-measure quantities. However, the presence of unreliable data, coupled with increasing demands for measurement quality assurance, has rendered inadequate such a simplistic view. In this paper, a probabilistic self-validating soft-sensor is proposed with the capability of performing self-diagnostics, self-reconstruction and online uncertainty measurement. In this framework, data collecting for soft-sensor modeling (easy-to-measure data) is validated by a Variational Bayesian Principal Component Analysis (VBPCA) model before performing a soft-sensor model construction. By integrating Relevant Vector Machine (RVM) as a predictive model, not only prediction values are obtained, but also the credibility of information for easy-to-measure and hard-to-measure quantities can be generated. The performance of the proposed soft-sensor is validated through two simulation studies of WWTPs with different process characteristics. The results suggest that the proposed strategy significantly improves the prediction performance.     

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.     

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.     

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     

Input variable scaling for statistical modeling

Input variable scaling is one of the most important steps in statistical modeling. However, it has not been actively investigated, and autoscaling is mostly used. This paper proposes two input variable scaling methods for improving the accuracy of soft sensors. One method statistically derives the input variable scaling factors; the other one uses spectroscopic data of a material whose content is estimated by the soft sensor. The proposed methods can determine the scales of the input variables based on their importance in output estimation. Thus, it can reduce the negative effects of input variables which are not related to an output variable. The effectiveness of the proposed methods was confirmed through a numerical example and industrial applications to a pharmaceutical and a distillation processes. In the industrial applications, the proposed methods improved the estimation accuracy by up to 63% compared to conventional methods such as autoscaling with input variable selection.     

Mixture semisupervised principal component regression model and soft sensor application

Traditionally, data‐based soft sensors are constructed upon the labeled historical dataset which contains equal numbers of input and output data samples. While it is easy to obtain input variables such as temperature, pressure, and flow rate in the chemical process, the output variables, which correspond to quality/key property variables, are much more difficult to obtain. Therefore, we may only have a small number of output data samples, and have much more input data samples. In this article, a mixture form of the semisupervised probabilistic principal component regression model is proposed for soft sensor application, which can efficiently incorporate the unlabeled data information from different operation modes. Compared to the total supervised method, both modeling efficiency and soft sensing performance are improved with the inclusion of additional unlabeled data samples. Two case studies are provided to evaluate the feasibility and efficiency of the new method. © 2013 American Institute of Chemical Engineers AIChE J 60: 533–545, 2014     

Applicability domain of soft sensor models based on one‐class support vector machine

Soft sensors are widely used to estimate process variables that are difficult to measure online. By using soft sensors, analyzer faults can be detected by estimation errors. However, it is difficult to detect abnormal data and determine the reasons because estimation errors increase not only due to analyzer faults but also due to variations caused by changes in the state of chemical plants. To separate those factors, we previously proposed to construct the relationships between distances to soft sensor models (DMs) and the accuracy of prediction of the models quantitatively and estimate the prediction accuracy of new data online. In this article, we used a one‐class support vector machine (OCSVM) to estimate data density and the output of an OCSVM as a DM. The proposed method was applied to real industrial data and the superiority of the proposed DM to the traditional ones was demonstrated by comparing their results. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2046–2050, 2013     

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.     

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.     

基于证据合成的高斯过程回归多模型软测量方法

针对生物发酵过程,提出了一种基于证据理论的高斯过程回归多模型软测量方法,其中多模型融合策略同时考虑了数据聚类特性和软测量子模型统计特性。首先,对聚类后的各子类建立高斯过程回归子模型;然后,基于聚类隶属度函数和高斯过程回归子模型后验概率分别设计子模型权值,并利用证据合成规则将两类权值进行证据合成得到融合权值;最后,将该融合权值作为加权因子对子模型进行融合。通过青霉素发酵过程仿真数据和红霉素发酵过程工业数据研究表明, 相比单一模型和传统多模型高斯过程回归软测量方法,本文所提方法具有较高的预测精度和较小的预测不确定度。     

Robust modeling of a microalgal heterotrophic fed-batch bioreactor

Microalgal feedstocks have shown potential for the production of biofuels and fine chemicals. Recently, an optimal experimental input profile for the identification of parameters of a microalgal bioreactor, containing 6 states and 12 unknown parameters has been proposed. In this work, the proposed design is implemented and parameters are estimated. It was found that the parameter estimation procedure can be made more computational efficient by the use of a novel iterative non-linear model reparameterization algorithm. By applying the proposed algorithm to experimental data, a good degree of output prediction was achieved along with bounds on the parameter values. The final, validated, model can be used for optimal control and process simulation.     

Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes

Soft sensor techniques have been widely used to estimate product quality or other key indices which cannot be measured online by hardware sensors. Unfortunately, their estimation performance would deteriorate under certain circumstances, e.g., the change of the process characteristics, especially for global learning approaches. Meanwhile, local learning methods always only utilize input information to select relevant instances, which may lead to a waste of output information and inaccurate sample selection. To overcome these disadvantages, a new local modeling algorithm, adaptive local kernel-based learning scheme (ALKL) is proposed. First, a new similarity measurement using both input and output information is proposed and utilized in a supervised locality preserving projection technique to select relevant samples. Second, an adaptive weighted least squares support vector regression (AW-LSSVR) is employed to establish a local model and predict output indices for each query data. In AW-LSSVR, instead of using traditional cross-validation methods, the trade-off parameters are adjusted iteratively and the local model is updated recursively, which reduces the computational complexity a lot. The proposed ALKL is applied to an online crude oil endpoint prediction in an industrial fluidized catalytic cracking unit (FCCU) process. The experimental results demonstrate the high precision of our ALKL 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.     

A multi-model approach for soft sensor development based on feature extraction using weighted kernel fisher criterion简

Multi-model approach can significantly improve the prediction performance of soft sensors in the proc- ess with multiple operational conditions. However, traditional clustering algorithms may result in overlapping phe- nomenon in subclasses, so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory. In order to solve these problems, a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy, in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses. Furthermore, the classified data are used to develop a multiple model based on support vector machine. The proposed method is applied to a bisphenol A production process for prediction of the quality index. The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.     

基于深度集成支持向量机的工业过程软测量方法

基于支持向量机（SVM）的软测量建模方法已经在工业过程控制领域得到广泛应用,然而传统支持向量机直接针对原始测量变量建立模型,未能充分挖掘数据的内在特征信息以提高预测精度。针对该问题,本文提出一种基于深度集成支持向量机（DESVM）的软测量建模方法。该方法首先利用深度置信网络（DBN）来对数据进行深层次的信息挖掘,提取出数据的内在特征,然后引入基于Bagging算法的集成学习策略,构建基于深度数据特征的集成支持向量机模型,以提升软测量预测模型的泛化能力。最后通过数值系统和真实工业数据对方法进行应用分析,结果表明本文提出的方法能够有效提升支持向量机软测量模型的预测精度,能够更好地预测过程质量指标的变化。     

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.     

选择性集成LTDGPR模型的自适应软测量建模方法

随着时间的增加,传统时间差（TD）模型会出现性能显著下降的问题。为了提高TD模型的可靠性和预测精度,同时考虑过程的时滞特征,基于一种选择性集成策略,提出一种局部时间差高斯过程回归（LTDGPR）模型的自适应软测量建模方法。首先,提取出数据库中的时滞动态信息,对建模数据进行重构;然后,采取局部化策略对差分后的重构样本进行统计划分,得到LTDGPR模型集。对于新来的输入样本,选择部分泛化能力强的LTDGPR模型进行集成,估计出含一定时间差的主导变量动态偏移值;最后,基于TD模型思想对当前时刻主导变量值进行在线预测。通过脱丁烷塔过程的数据建模仿真研究,验证了所提方法的有效性和精度。     

浮选工艺指标KPCA-ELM软测量模型及应用

精矿品位和尾矿品位是浮选过程重要的工艺技术指标,其难以实现在线检测,且与过程控制变量具有强非线性、不确定性等综合复杂特性,难以直接采用精确的数学模型描述,主要依靠人工化验分析。人工采样化验周期较长,难以满足控制要求,使得浮选精矿品位偏低,尾矿品位偏高,因此建立浮选品位指标的软测量方法受到工业界广泛关注。在分析浮选过程工艺指标相关影响因素的基础上,建立一种基于主元分析KPCA(kernel principal component analysis)和极限学习机ELM(extreme learning machine)的软测量模型。为了消除离群点对软测量模型精度的影响,采用基于稳健位置估计的方法识别离群点,利用核主元分析对软测量模型的输入数据进行降维,提取非线性主元,然后用极限学习机进行建模。该建模方法已成功应用于中国西北某选矿厂浮选车间,工业应用结果表明该方法有很高的预报精度,对生产有一定的指导意义。