Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes

Liu, Yi; Yang, Chao; Gao, Zengliang; Yao, Yuan

doi:10.1016/j.chemolab.2018.01.008

Cited by 172 publications

(59 citation statements)

References 47 publications

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“…In the AI community, methods that combine kernel methods with deep learning are now being developed, such as neural kernel networks [58,59], deep neural kernel blocks [60], and deep kernel learning [61,62]. A soft sensor based on deep kernel learning was recently applied in a polymerization process [63]. Based on these recent advances, Wilson et al [62] has concluded that the relationship between kernel methods and deep ANNs must not be competing, but rather, complementary.…”

Section: Relationship Between Kernel Methods and Neural Networkmentioning

confidence: 99%

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.

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Section: Relationship Between Kernel Methods and Neural Networkmentioning

confidence: 99%

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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“…Alternatively, the representation of data at a deeper level reveals inherent features and becomes more attractive. Recently, increasing applications of deep neural networks (DNNs) have been reported, especially in the speech recognition and computer vision fields [21][22][23][24][25][26][27][28][29]. As a popular DNN, the deep brief network (DBN) comprises multiple layers for representing data with multilevel abstraction [22].…”

Section: Introductionmentioning

confidence: 99%

“…To describe the important trends in a combustion process, a multilayer DBN was constructed to obtain the nonlinear relationship between the flame images and the outlet oxygen content [25]. An ensemble deep kernel learning model was proposed for the melt index prediction and exhibited good predictions in an industrial polymerization process [26]. The process modeling results indicate that DNNs characterize nonlinear features better and enhance the automation level of industrial manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

Zheng

Liu

et al. 2020

Sensors

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Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR.

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“…Wang et al [23] provided a comprehensive survey of advanced DL methods and applications for smart manufacturing. Liu et al [24] proposed an ensemble deep kernel learning (EDKL) model to predict the melt index in industrial polymerization processes.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Analytics Method Based on Multistage Modeling for a BOF Steelmaking Process

Chang

Tang

Liu

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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This paper proposes a dynamic analytics method based on a least squares support vector machine with a hybrid kernel to address real-time prediction problems in the converter steelmaking process. The hybrid kernel function is used to enhance the performance of the existing kernels. To improve the model's accuracy, the internal parameters are optimized by a differential evolution algorithm. In light of the complex mechanisms of the converter steelmaking process, a multistage modeling strategy is designed instead of the traditional single-stage modeling method. Owing to the dynamic nature of the practical production process, great effort has been made to construct a dynamic model that uses the prediction error information based on the static model. The validity of the proposed method is verified through experiments on real-world data collected from a BOF steelmaking process. The results indicate that the proposed method can successfully solve dynamic prediction problems and outperforms other state-of-the-art methods in terms of prediction accuracy.Note to Practitioners-With the development of cyber-physical systems, abundant real-time data have been collected from the converter steelmaking process. These data provide an opportunity to solve product quality prediction problems using data-driven models. This paper proposes a dynamic analytics method based on a least squares support vector machine with a hybrid kernel to address this challenging issue. To improve the model's performance, a differential evolution (DE) algorithm is used to optimize its parameters. Because of the fierce physicochemical reaction in the converter furnace, it is difficult for a single-stage model to achieve accurate predictions. Thus, a multistage modeling strategy is proposed to address this difficulty, and a dynamic model based on feedback error is developed to realize real-time prediction. We verify the effectiveness of the proposed method using real data from a BOF steelmaking process. The computational results reveal that the proposed method has a higher prediction accuracy than other methods, making it helpful in guaranteeing the specified product quality and in maintaining stable BOF operation.Index Terms-Dynamic analytics, multistage modeling, least squares support vector machine, hybrid kernel function, basic oxygen furnace.

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Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes

Cited by 172 publications

References 47 publications

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

A Dynamic Analytics Method Based on Multistage Modeling for a BOF Steelmaking Process

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