A semi-supervised soft sensor method based on vine copula regression and tri-training algorithm for complex chemical processes

Liu, Shisong; Li, Shaojun

doi:10.1016/j.jprocont.2022.11.004

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…By incorporating a substantial quantity of unlabeled samples lacking dominant variables into a limited number of labeled samples containing dominant variables, both the problem of multi-rate and the predictive accuracy of soft sensing models can be effectively addressed. This integration optimally utilizes the unlabeled samples, consequently resolving the challenge of multi-rate and enhancing the precision of prediction in soft sensing models [31,32]. In the selection of fusion weights, time is used as a variable, calculate its Pearson correlation coefficient, and the time correlation performance effectively reflects the degree of correlation between unlabeled and labeled samples.…”

Section: The Semi-supervised Learning Methods Based On Time Correlationmentioning

confidence: 99%

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

Liu,

Li,

Zhang

et al. 2024

Meas. Sci. Technol.

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To address the adverse effects of nonlinearity and dynamic time-varying in complex chemical processes on the accuracy of the soft sensor model, a local-semi-supervised ensemble learning for soft sensor modeling (LS-SEL-LSTM) method is proposed in this article. Firstly, a hierarchical clustering method incorporating spatiotemporal criteria is proposed to reduce the influence of nonlinearity in global model prediction accuracy. The method considers the dynamic time-varying characteristics of temporal data and generates multiple local datasets. Then, to address the issue of multi-rate between auxiliary variables and dominant variables, a semi-supervised weight fusion mechanism based on temporal correlation is proposed, which effectively utilizes auxiliary variables to reconstruct local semi-supervised datasets and establishes local soft sensing models using LSTM. Concurrently, the parameters of the established model were optimized using the Flower Pollination Algorithm (FPA). Subsequently, a selective ensemble learning method based on sub-model prediction accuracy and an adaptive combination weight calculation method for sub-models were proposed to improve the prediction accuracy. Finally, the effectiveness of the proposed method was verified through the actual dataset of the sulfur recovery process. The results indicate that LS-SEL-LSTM performs well in handling complex chemical processes with nonlinear and dynamic time-varying characteristics.

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Section: The Semi-supervised Learning Methods Based On Time Correlationmentioning

confidence: 99%

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

Liu,

Li,

Zhang

et al. 2024

Meas. Sci. Technol.

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“…There are different approaches to soft sensor development, but the complexity of the production process and uncertainties in determining the connection between laboratory values and signals that are measured in the process are reasons why soft sensors in the process industry are mainly based on black box or gray box models. The black box approach is successfully applied to different processes, from the cement industry [ 1 , 2 , 3 , 4 ] and chemical processes [ 5 , 6 , 7 ] to water treatment [ 8 , 9 , 10 ], energy production [ 11 ] and the oil industry [ 12 , 13 , 14 ]. Since it proved appropriate for the development of soft sensors for a variety of industrial processes, the black box approach has the potential to be used as a basis for a wide industrial implementation of soft sensors.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Soft Sensor Development for a Class of Processes with Distinct Operating Conditions

Stanišić,

Mejić,

Jorgovanović

et al. 2024

Sensors

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Soft sensors are increasingly being used to provide important information about production processes that is otherwise only available through off-line laboratory analysis. However, usually, they are developed for a specific application, for which thorough process analysis is performed to provide information for the appropriate selection of model type and model structure. Wide industrial application of soft sensors, however, requires a method for soft sensor development that has a high level of automatism and is applicable to a significant number of industrial processes. A class of processes that is very common in the industry are processes with distinct operating conditions. In this paper, an algorithm that is suitable for the development of soft sensors for this class of processes is presented. The algorithm possesses a high level of automatism, as it requires minimal user engagement regarding the structure of the model, which makes it suitable for implementation as a customary industrial solution. The algorithm is based on a radial basis function artificial neural network, and it enables the automatic selection of the model structure and the determination of model parameters, only based on the training data set. The testing of the presented algorithm is done on the cement production process, since it represents a process with distinct operating conditions. The results of the test show that, besides providing a high level of automatism in model development, the presented algorithm generates a soft sensor with high estimation performance.

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“…The former aims to provide process dynamics for controller design, while the latter is to estimate key performance indicators based on easy-to-measure process variables. However, due to the complexity of process operation, industrial process data are usually nonlinear and nonstationary, high dimensional with strong correlations, and multivariate with complex interactions among multiple outputs [2,3,4,5,6,7]. Although numerous studies have been devoted to address one or two of these issues, no research has addressed them all.…”

Section: Introductionmentioning

confidence: 99%

Efficient adaptive deep gradient RBF network for multi-output nonlinear and nonstationary industrial processes

Liu

Chen

Yang

et al. 2023

Journal of Process Control

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A semi-supervised soft sensor method based on vine copula regression and tri-training algorithm for complex chemical processes

Cited by 6 publications

References 51 publications

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

An Algorithm for Soft Sensor Development for a Class of Processes with Distinct Operating Conditions

Efficient adaptive deep gradient RBF network for multi-output nonlinear and nonstationary industrial processes

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