Probabilistic density-based regression model for soft sensing of nonlinear industrial processes

Yuan, Xiaofeng; Wang, Yalin; Yang, Chunhua; Gui, Weihua; Ye, Lingjian

doi:10.1016/j.jprocont.2017.06.002

Cited by 34 publications

(8 citation statements)

References 30 publications

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“…For example, GPR models output both the estimated y values and their standard deviations. Additionally, probabilistic models 19,20 also can handle prediction uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Estimating the reliability of predictions in locally weighted partial least‐squares modeling

Kaneko

2021

Journal of Chemometrics

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It is important to predict the reliability of the estimation results given by adaptive soft sensors. In this study, a locally weighted partial least-squares (LWPLS) method, which is a just-in-time-based adaptive soft sensor, is analyzed, and the reliability of LWPLS modeling is predicted as the standard deviation of the estimated values of an objective variable y. The relationship between the minimum of Euclidean distance (MinED) and the standard deviation of y errors (SDYE) is

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“…For example, GPR models output both the estimated y values and their standard deviations. Additionally, probabilistic models 19,20 also can handle prediction uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Estimating the reliability of predictions in locally weighted partial least‐squares modeling

Kaneko

2021

Journal of Chemometrics

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“…First, feature indices are presented for dealing with the enormous data from different procedures of the ternary cathode material manufacturing process. Second, combining various similarity measurement methods with the local weighting technique [23][24][25][26] and JITL technique, 27,28 an ensemble JITL 22 soft-sensor modeling method based on semisupervised local weighted probability principal component regression (SWPPCR) 21 is developed to fully deal with process nonlinearity and adequately use unlabeled data. In addition, an adaptive method based on moving window technique [29][30][31][32][33] is used to track the varying process characteristic and update the model parameters.…”

Section: Introductionmentioning

confidence: 99%

An ensemble just‐in‐time learning soft‐sensor model for residual lithium concentration prediction of ternary cathode materials

Chen

Gui

Dai

et al. 2020

Journal of Chemometrics

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The surface-free lithium content, which is a critical index that reflecting the quality of ternary cathode materials, usually cannot be monitored in real time due to technical restriction. To this end, soft sensor technique is applied to predict the surface-free lithium content by readily available process variables, making timely control on operation parameters. In this paper, a modeling method based on ensemble just-in-time learning (JITL) soft-sensor is developed. In the model, data feature indices are firstly designed to properly extract the real-time and short-time features between the batching and the loading procedure. Accordingly, ensemble JITL soft-sensor model is constructed with the semisupervised local weighted probability principal component regression.Moreover, considering varying working conditions, an adaptive moving window technique is adopted to improve the adaptability of the model. The validation and the flexibility of the developed modeling method are testified with the practical manufacturing data. K E Y W O R D Sjust-in-time learning, moving window, prediction of residual lithium content, semisupervised learning | INTRODUCTIONWith the growing demand for electric energy reliability in modern society, as well as the widespread use of intermittent new energy sources such as wind power and photovoltaics, electric energy storage technology has received extensive attention from all over the world. 1 Lithium-ion batteries, which have many merits such as portability, recyclability, high energy density, and long cycle life, are nowadays recognized as a key power storage material. In the process of manufacturing lithium-ion batteries, surface-free lithium content, namely, the content of lithium-ion on the surface of cathode materials, plays a decisive role in determining the characteristic of this material. Thus, high requirements are put forward for the real-time monitoring of surface-free lithium content. However, surface-free lithium content cannot be directly measured since the mensuration is inefficient, hysteresis, and costly. Therefore, it is a good choice to predict surface-free lithium content by using the soft-sensor model. 2,3 There are two most commonly used methods for building soft-sensor models, namely, mechanism modeling and data-driven modeling. The mechanism modeling method starts with the physical, chemical, and biological knowledge of the object process. Then, mathematical equations based on conservation laws such as mass conservation are formed to find the relationship between process variables and the key variables. The mechanism models have more application

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“…In modern industrial processes, it is important to guarantee process safety, achieve energy conservation, and improve product quality, all of which often largely depend on the timely monitoring and control of important quality variables . However, due to reasons like harsh measurement environment, cost of expensive instruments, and significant measurement delays, most of these quality variables are often difficult to measure online by hard sensors in actual production processes.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

Wang

et al. 2019

Can J Chem Eng

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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. K E Y W O R D Sattention, deep learning, long short-term memory, prediction, soft sensor

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Probabilistic density-based regression model for soft sensing of nonlinear industrial processes

Cited by 34 publications

References 30 publications

Estimating the reliability of predictions in locally weighted partial least‐squares modeling

Estimating the reliability of predictions in locally weighted partial least‐squares modeling

An ensemble just‐in‐time learning soft‐sensor model for residual lithium concentration prediction of ternary cathode materials

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

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