Kernel Partial Least Squares Model Research Articles

Practical Online Characterization of the Properties of Hydrocracking Bottom Oil via Near-Infrared Spectroscopy

Providing real-time information on the chemical properties of hydrocracking bottom oil (HBO) as the feedstock for ethylene cracker while minimizing processing time, is important to improve the real-time optimization of ethylene production. In this study, a novel approach for estimating the properties of HBO samples was developed on the basis of near-infrared (NIR) spectra. The main noise and extreme samples in the spectral data were removed by combining discrete wavelet transform with principal component analysis and Hotelling’s T2 test. Kernel partial least squares (KPLS) regression was utilized to account for the nonlinearities between NIR data and the chemical properties of HBO. Compared with the principal component regression, partial least squares regression, and artificial neural network, the KPLS model had a better performance of obtaining acceptable values of root mean square error of prediction (RMSEP) and mean absolute relative error (MARE). All RMSEP and MARE values of density, Bureau of Mines correlation index, paraffins, isoparaffins, and naphthenes were less than 1.0 and 3.0, respectively. The accuracy of the industrial NIR online measurement system during consecutive running periods in predicting the chemical properties of HBO was satisfactory. The yield of high value-added products increased by 0.26 percentage points and coil outlet temperature decreased by 0.25 °C, which promoted economic benefits of the ethylene cracking process and boosted industrial reform from automation to digitization and intelligence.

A Novel Three-Stage Quality Oriented Data-Driven Nonlinear Industrial Process Monitoring Strategy

It is well known that the partial least squares (PLS) and its nonlinear extension kernel PLS (KPLS) are efficient tools widely utilized in fault detection. Nonetheless, these two techniques project obliquely into the process variable space, limiting their ability to discriminate between the quality-related and quality-unrelated faults. Although various enhanced ways based on the basic PLS and KPLS, such as the total PLS and total KPLS methods, have been published to cope with this problem, these methods fail to reduce the false alarm rates of the quality-unrelated fault when the fault amplitude increases, and they need to monitor four statistics to determine the fault categories. To address this issue, this paper proposes a three-stage data-driven method for complicated nonlinear industrial quality-related process monitoring, consisting of three parts: data preprocessing, modeling, and decomposing. The kernel direct orthogonalization (KDO) approach is initially investigated in this strategy, which is based on the traditional direct orthogonalization methodology and the kernel theory. The unnecessary fluctuations in the process variable space are eliminated after the data is preprocessed using the KDO approach. A standard KPLS model is built for the filtered component. Then, a quality-related KPLS (QRKPLS) method is proposed as the decomposing part to further divide the filtered process mapping matrix into two orthogonal parts, namely, the quality-related part and the quality-unrelated part. Additionally, the corresponding test statistics are calculated for these two parts, and the diagnosis logic is also given in this paper. The effectiveness and superiority of the novel established KDO-QRKPLS method is investigated using a widely numerical simulation as well as an industrial simulation.

Developing new products with kernel partial least squares model inversion

In recent years, data-driven approaches (e.g., latent variable model) have excited the development of new products and the control of product quality. To derive an input space within which raw material properties or initial operating conditions can yield the required product quality, previous researchers developed a linear latent variable model and derived the solution of inputs through PLS model inversion. In this research, a novel method based on kernel partial least squares (KPLS) model inversion is proposed for product design of nonlinear processes and an input domain is derived. Constraints on the model inputs or outputs and on the KPLS model are discussed, and solutions are provided based on an optimization framework and the Monte Carlo sampling method. The effectiveness of the method is demonstrated by numerical simulation and a beer fermentation experiment, and the KPLS model inversion method outperforms the PLS model inversion method in terms of the precision.

Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

We suggest in this article a dynamic reduced algorithm in order to enhance the monitoring abilities of nonlinear processes. Dynamic fault detection using data-driven methods is among the key technologies, which shows its ability to improve the performance of dynamic systems. Among the data-driven techniques, we find the kernel partial least squares (KPLS) which is presented as an interesting method for fault detection and monitoring in industrial systems. The dynamic reduced KPLS method is proposed for the fault detection procedure in order to use the advantages of the reduced KPLS models in online mode. Furthermore, the suggested method is developed to monitor the time-varying dynamic system and also update the model of reduced reference. The reduced model is used to minimize the computational cost and time and also to choose a reduced set of kernel functions. Indeed, the dynamic reduced KPLS allows adaptation of the reduced model, observation by observation, without the risk of losing or deleting important information. For each observation, the update of the model is available if and only if a further normal observation that contains new pertinent information is present. The general principle is to take only the normal and the important new observation in the feature space. Then the reduced set is built for the fault detection in the online phase based on a quadratic prediction error chart. Thereafter, the Tennessee Eastman process and air quality are used to precise the performances of the suggested methods. The simulation results of the dynamic reduced KPLS method are compared with the standard one.

Application of non-linear partial least squares analysis on prediction of biomass of maize plants using hyperspectral images

With the application of hyperspectral imaging systems on high-throughput plant phenotyping, multivariable spectral data modelling methods have been proposed to predict plant physiological features such as biomass. However, most presented modelling methods are linear models such as the regression model between projected leaf area and plant biomass, which cannot accurately reflect some non-linear relationships between hyperspectral imaging information and the phenomes to be predicted. Therefore, it is important to develop a prediction model for plant physiological features that fully utilises the information in plant hyperspectral images. In this paper, a non-linear modelling method known as kernel partial least squares (KPLS) was investigated to improve the prediction performance of maize (US corn) biomass. The main idea of KPLS is mapping the original input features into higher dimensional feature spaces before obtaining the principle components for a standard PLS model. In the new mapping spaces, the original, nonlinear inputs may exhibit linear patterns with different kernel functions. This method was tested in a greenhouse assay containing 102 maize plants, which were subjected to a combination of three water treatments and two nitrogen treatments at three different growth stages. KPLS methods were successfully applied on the collected hyperspectral images. The results showed that compared with conventional linear models, statistics including R 2 of KPLS with radial basis function (RBF) kernel had improved accuracy (R 2 = 0.924). We expect that besides plant biomass, this new non-linear KPLS model could improve the quality of estimation of other plant features as well. • Proposed KPLS model with RBF kernel more accurately predicts maize fresh weight. • Kernel functions map original features into higher dimensional feature spaces. • Hyperspectral image provides image-derived features for biomass prediction. • Performances of projected leaf area, linear PLS and KPLS models are investigated. • We conduct a greenhouse phenotyping assay to explore maize biomass prediction models.

Fault identification for quality monitoring of molten iron in blast furnace ironmaking based on KPLS with improved contribution rate

Blast furnace (BF) ironmaking is one of the most important production links in modern iron–steel making. The operation conditions of BF and the molten iron quality (MIQ) should be monitored and analyzed in real-time to realize high quality with low energy consumption. Aiming at the problems of strong nonlinearity and few fault samples in BF processes, a novel fault identification method for MIQ monitoring based on kernel partial least squares (KPLS) with improved contribution rate is proposed in this paper. First of all, a KPLS model is established with the actual historical data in order to detect the quality-related faults accurately. The T2 and SPE statistics are used to monitor the operation conditions of process from different aspects. Second, in view of the unclear physical meaning and complex computation of the existing fault identification methods based on KPLS with contribution rate, a scale factor vector is introduced into the new samples to calculate the T2 and SPE statistics, so as to construct the monitoring indicators functions. By performing Taylor approximation on these constructed functions near the scale factor with the value of 1, two new statistics are obtained from the absolute value of the first-order partial derivative representing the contribution rate of each variable. Finally, in order to further improve the effect of fault identification, the relative contribution rate of each variable is used to identify the final faulty variables. The tests of MIQ monitoring in BF ironmaking process using actual industrial data verify the validity and practicability of the proposed method.

Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS

Kernel partial least squares (KPLS) models are widely used as nonlinear data-driven methods for faults detection (FD) in industrial processes. However, KPLS models lead to irrelevant performance over long operation periods due to process parameters changes, errors and uncertainties associated with measurements. Therefore, in this paper, two different interval reduced KPLS (IRKPLS) models are developed for monitoring large scale nonlinear uncertain systems. The proposed IRKPLS models present an interval versions of the classical KPLS model. The two proposed IRKPLS models are based on the Euclidean distance between interval-valued observations as a dissimilarity metric to keep only the more relevant and informative samples. The first proposed IRKPLS technique uses the centers and ranges of intervals to estimate the interval model, while the second one is based on the upper and lower bounds of intervals for model identification. These obtained models are used to evaluate the monitored interval residuals. The aforementioned interval residuals are fed to the generalized likelihood ratio test (GLRT) chart to detect the faults. In addition to considering the uncertainties in the input-output systems, the new IRKPLS-based GLRT techniques aim to decrease the execution time when ensuring the fault detection performance. The developed IRKPLS-based GLRT approaches are evaluated across various faults of the well-known Tennessee Eastman (TE) process. The performance of the proposed IRKPLS-based GLRT methods is evaluated in terms of missed detection rate, false alarms rate, and execution time. The obtained results demonstrate the efficiency of the proposed approaches, compared with the classical interval KPLS.

A Nonlinear Method for Component Separation of Dam Effect Quantities Using Kernel Partial Least Squares and Pseudosamples

Existing component separation methods fail to consider the complex nonlinear relationship between dam effect quantities and environmental variables. In this study, a novel nonlinear component separation method for the effect quantities is proposed by combining kernel partial least squares (KPLS) and pseudosamples. By this method, a nonlinear monitoring model is established based on KPLS, and the complicated nonlinear relationship between the effect quantities and environmental variables can be determined accurately through the model. Furthermore, special pseudosamples are constructed to separate independent components and coupling influence components of environmental factors from the KPLS model. These methods have been applied into a super‐high arch dam, and the separated displacement components conform to the general deformation law. The presented results indicate that it is more reliable than traditional multiple linear regression models.

Multilevel heterogeneous omics data integration with kernel fusion.

High-throughput omics data are generated almost with no limit nowadays. It becomes increasingly important to integrate different omics data types to disentangle the molecular machinery of complex diseases with the hope for better disease prevention and treatment. Since the relationship among different omics data features are typically unknown, a supervised learning model assuming a particular distribution with a specific structure will not serve the purpose to capture the underlying complex relationship between multiple features and a disease phenotype. In this work, we briefly reviewed methods for kernel fusion (KF) based on support vector machine and kernel partial least squares (KPLS) algorithms. We then proposed a fused KPLS (fKPLS) model for disease classification and prediction with multilevel omics data. The fused kernel can deal with effect heterogeneity in which different omic data types may have different effect contribution to the trait of interest, with the purpose to improve the prediction performance. We proposed to optimize the kernel parameters and kernel weights with the genetic algorithm (GA). The proposed GA-fKPLS model can substantially improve disease classification performance by integrating multiple omics data types, demonstrated via extensive simulations and real data analysis. With properly defined fitness functions during GA optimization, the proposed KF method can be extended to other kernel-based analyses such as in kernel association analysis with common or rare variants.

A Kernel Least Squares Based Approach for Nonlinear Quality-Related Fault Detection

In this paper, a nonlinear quality-related fault detection approach is proposed based on kernel least squares (KLS) model. The major novelty of the proposed method is that it utilizes KLS model to exploit the entire correlation between feature and output matrices. First, it uses a nonlinear projection function to map original process variables into feature space in which the correlation between feature and output matrices is realized by means of KLS. Then, the feature matrix is decomposed into two orthogonal parts by singular value decomposition and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with existing kernel partial least squares (KPLS) based approaches, the proposed new method has the following obvious advantages. 1) It extracts the full correlation information of feature matrix, while KPLS-based approaches only use the partial correlation of several selected latent variables; therefore, it is more stable than the existing ones. 2) It omits the iterative computation of KPLS model and the determination of the number of latent variables; therefore, it is more efficient in engineering implementation. 3) It only uses two statistics to determine the type of fault, while most of the KPLS-based approaches need four; therefore, it has a more simple diagnosis logic. For simulation verification, a widely used literature example and an industrial benchmark are utilized to evaluate the performance of the proposed method.

Kernel PLS-based GLRT method for fault detection of chemical processes

Fault detection is essential for proper and safe operation of various chemical processes, and it has recently become even more important than ever before. In this paper, we extended our previous work (Mansouri et al. (2016)), which addresses the problem of fault detection of chemical systems using kernel principal component analysis (KPCA)-based generalized likelihood ratio test (GLRT), to widen its applicability for processes represented by input-output models. Specifically, hypothesis testing fault detection technique that are based on linear and nonlinear partial least squares (PLS) models are developed. For nonlinear PLS models, a kernel PLS (KPLS) modeling framework is utilized. KPLS has been widely used to model various nonlinear processes, such as distillation columns and reactors. Thus, in the current work, a KPLS-based GLRT fault detection method is developed, in which KPLS is used as a modeling framework and the KPLS model generated residuals are evaluated using a GLRT statistic. The fault detection performance of the developed KPLS-based GLRT method is illustrated through a simulated example representing a continuously stirred tank reactor (CSTR). The simulation results show that the KPLS-based GLRT method outperforms its linear PLS-based version, and that both of the aforementioned techniques provide clear advantages over the conventional linear and nonlinear PLS based statistics, i.e., T2 and Q.

Ultrasonic spectrum for particle concentration measurement in multicomponent suspensions

This paper studies the feasibility of applying the ultrasonic spectrum technique to the measurement of particle concentrations in multicomponent suspensions. A combination of the kernel partial least squares (KPLS) model and the interval selection methods is implemented to build the relationship between the ultrasonic spectra of the first reflected pulses and the particle concentrations. First of all, the interval selection methods are used to select optimal spectral interval(s) from full spectra. Then, the KPLS models with optimal spectral interval(s) are tuned, built and evaluated to obtain the optimal model. Finally, the optimal KPLS model is employed to measure the particle concentrations in the mixing process and its online prediction ability is evaluated. In comparison with the linear partial least squares (PLS) models, the optimal KPLS model shows the best performance. The results demonstrate that particle concentrations in multicomponent suspensions can be measured online by the ultrasonic spectrum technique, and the KPLS model with optimal spectral interval(s) shows the superiority in model calibration.

Safety Monitoring of a Super-High Dam Using Optimal Kernel Partial Least Squares

Considering the characteristics of complex nonlinear and multiple response variables of a super-high dam, kernel partial least squares (KPLS) method, as a strongly nonlinear multivariate analysis method, is introduced into the field of dam safety monitoring for the first time. A universal unified optimization algorithm is designed to select the key parameters of the KPLS method and obtain the optimal kernel partial least squares (OKPLS). Then, OKPLS is used to establish a strongly nonlinear multivariate safety monitoring model to identify the abnormal behavior of a super-high dam via model multivariate fusion diagnosis. An analysis of deformation monitoring data of a super-high arch dam was undertaken as a case study. Compared to the multiple linear regression (MLR), partial least squares (PLS), and KPLS models, the OKPLS model displayed the best fitting accuracy and forecast precision, and the model multivariate fusion diagnosis reduced the number of false alarms compared to the traditional univariate diagnosis. Thus, OKPLS is a promising method in the application of super-high dam safety monitoring.

Performance modeling of centrifugal compressor using kernel partial least squares

The performance modeling of centrifugal (turbo) compressor was performed in this paper by applying kernel partial least squares (KPLS). Firstly, steady-state compressor data sets were collected from a real gas turbine power plant and a simulation study respectively. Then the two data sets were used to train the KPLS regression model for predicting the centrifugal compressor operating parameters such as pressure ratio and efficiency. The prediction performance of KPLS model was compared to that of a three-layer back-propagation (BP) neural network with validation data, and the KPLS model showed slightly better performance than the BP network. Furthermore, results showed that, with high accuracy, KPLS could be used to smoothly predict the compressor map, which was useful in the preliminary design phase of any centrifugal compression system.

A Multiway Gaussian Mixture Model based Adaptive Kernel Partial Least Squares Regression Approach for Inferential Quality Predictions of Batch Processes

Soft sensor technique has become increasingly important to provide reliable on-line measurements, facilitate advanced process control and improve product quality in process industries. The conventional soft sensors are normally single-model based and thus may not be appropriate for processes with shifting operating conditions or phases. In this study, a multiway Gaussian mixture model (MGMM) based kernel partial least squares (PLS) method is proposed to handle multiple operating phases in batch or semibatch processes. The measurement data are first projected onto high-dimensional kernel feature space to account for the process nonlinearity. Then the multiway Gaussian mixture model is estimated with multiple Gaussian clusters in the kernel space. Thus various localized PLS models can be built within each Gaussian cluster to characterize the dynamics in the particular operating phase. Using Bayesian inference strategy, the soft sensor models for all the test samples are adaptively selected from the multiple localized kernel PLS models representing different phases and further used for online quality predictions. The presented soft sensor method is applied to the multiphase penicillin fermentation process and the computational results demonstrate that its performance is superior to the conventional multiway kernel PLS model.