Accurate Soft Sensors Research Articles

Nearest Correlation-Based Input Variable Weighting for Soft-Sensor Design.

In recent years, soft-sensors have been widely used for estimating product quality or other important variables when online analyzers are not available. In order to construct a highly accurate soft-sensor, appropriate data preprocessing is required. In particular, the selection of input variables or input features is one of the most important techniques for improving estimation performance. Fujiwara et al. proposed a variable selection method, in which variables are clustered into variable groups based on the correlation between variables by nearest correlation spectral clustering (NCSC), and each variable group is examined as to whether or not it should be used as input variables. This method is called NCSC-based variable selection (NCSC-VS). However, these NCSC-based methods have a lot of parameters to be tuned, and their joint optimization is burdensome. The present work proposes an effective input variable weighting method to be used instead of variable selection to conserve labor required for parameter tuning. The proposed method, referred to herein as NC-based variable weighting (NCVW), searches input variables that have the correlation with the output variable by using the NC method and calculates the correlation similarity between the input variables and output variable. The input variables are weighted based on the calculated correlation similarities, and the weighted input variables are used for model construction. There is only one parameter in the proposed NCVW since the NC method has one tuning parameter. Thus, it is easy for NCVW to develop a soft-sensor. The usefulness of the proposed NCVW is demonstrated through an application to calibration model design in a pharmaceutical process.

A variable selection method for soft sensor development through mixed integer quadratic programming

Soft sensors are widely employed in industry to predict quality variables, which are difficult to measure online, by using secondary variables. To build an accurate soft sensor, a proper variable selection is critical. In this project, a method of selecting the optimal secondary variables for a soft sensor model is proposed. It is formulated as a nested optimization problem. In each iteration, a mixed integer quadratic programming (MIQP) is conducted with the Bayesian information criterion (BIC) to estimate the prediction error. A warm start (WS) technique is developed to speed up the convergence. The proposed method is evaluated using a number of instances from the UCI Machine Learning Repository. The computational results demonstrate that this method is well suited for finding the best variable subsets. The method is successfully applied to build soft sensors for an industrial distillation column. The results show that the proposed method can effectively select feature variables that will improve the model prediction performance and reduce the model complexity. Comparisons with other methods, including the traditional partial least square technique, are also presented.

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

AbstractThe 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 robust localized soft sensor for particulate matter modeling in Seoul metro systems

Developing accurate soft sensors to predict and monitor the indoor air quality (IAQ) of hazardous pollutants that accumulate in underground metro systems is of key importance. The just-in-time (JIT) learning technique possesses a local feature that can track the variations in the dynamic process more effectively, which is different from the traditional soft sensor modeling methods, such as partial least squares (PLS), which models the process in an offline and global way. In this study, a robust soft sensor that combined the JIT learning technique with a least squares support vector regression (LSSVR) method, named JIT–LSSVR, was derived in order to improve the prediction performance of a PM2.5 soft sensor in a subway station. Additionally, in order to eliminate the adverse effects caused by the outliers in the process variables, an outlier detection step was integrated into the JIT–LSSVR modeling procedure. The performance evaluation results demonstrated that the proposed robust JIT–LSSVR soft sensor has the capability to model nonlinear and dynamic subway systems. The root mean square error of the JIT–LSSVR soft sensor was improved by 55% in comparison with that of the LSSVR soft sensor.

Efficient input variable selection for soft-senor design based on nearest correlation spectral clustering and group Lasso

Appropriate input variables have to be selected for building highly accurate soft sensor. A novel input variable selection method based on nearest correlation spectral clustering (NCSC) has been proposed, and it is referred to as NCSC-based variable selection (NCSC-VS). Although NCSC-VS can select appropriate input variables, a lot of parameters have to be tuned carefully for selecting proper variables. The present work proposes a new methodology for efficient input variable selection by integrating NCSC and group Lasso. The proposed NCSC-based group Lasso (NCSC-GL) can not only reduce the number of tuning parameters but also achieve almost the same performance as NCSC-VS. The usefulness of the proposed NCSC-GL is demonstrated through applications to soft sensor design for a pharmaceutical process and a chemical process.

Two-stage subspace identification for softsensor design and disturbance estimation

Softsensors or virtual sensors are key technologies in industry because important variables such as product quality are not always measured on-line. In the present work, two-stage subspace identification (SSID) is proposed to develop highly accurate softsensors that can take into account the influence of unmeasured disturbances on estimated key variables explicitly. The proposed two-stage SSID method can estimate unmeasured disturbances without the assumptions that the conventional Kalman filtering technique must make. Therefore, it can outperform the Kalman filtering technique when innovations are not Gaussian white noises or the characteristics of disturbances do not stay constant with time. The superiority of the proposed method over the conventional methods is demonstrated through numerical examples and application to an industrial ethylene fractionator.

IMPROVING ESTIMATION PERFORMANCE OF SOFTSENSORS THROUGH TWO-STAGE SUBSPACE IDENTIFICATION

Softsensors or virtual sensors are among the key technologies in industry, because important variables such as product quality are not always measured on-line. Therefore, to reduce off-specification products and enhance productivity, the development of an accurate softsensor is crucial. In the present work, two-stage subspace identification (SSID) is proposed to develop highly accurate softsensors that can take into account the influence of unmeasured disturbances on estimated key variables. The two-stage SSID procedure is as follows: 1) identify a state space model by using measured input and output variables, 2) estimate unmeasured disturbance variables from residual variables, and 3) identify a state space model to estimate key variables from the estimated disturbance variables and the other measured input variables. The proposed two-stage SSID can estimate unmeasured disturbances without the assumptions that the conventional Kalman filtering technique must make. Thus it can outperform the Kalman filtering technique when innovations are not Gaussian white noises or the characteristics of disturbances do not stay constant with time. The superiority of the proposed method over the conventional method is demonstrated through a numerical example.