Novel Gray Orthogonal Echo State Network Integrating the Process Mechanism for Dynamic Soft Sensor Development

Zhang, Bailun; Han, Yongming; Li, Chengfei; Geng, Zhiqiang

doi:10.1021/acs.iecr.1c02380

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…In such a situation, a large measurement delay occurs, which is not beneficial to process quality control and optimization. Alternatively, soft sensing methods are developed to alleviate the problem. − The concerned hard-to-measure quality variables are estimated with the help of process variables. As known, adequate training samples are a key factor in reliable model construction.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction

et al. 2023

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Although recent transfer learning soft sensors show promising applications in multigrade chemical processes, good prediction performance mainly relies on available target domain data, which is difficult to achieve for a start-up grade. Additionally, only employing a single global model is inadequate to characterize the inner relationship of process variables. A just-in-time adversarial transfer learning (JATL) soft sensing method is developed to enhance multigrade process prediction performance. The distribution discrepancies of process variables between two different operating grades are first reduced by the ATL strategy. Subsequently, by applying the just-in-time learning approach, a similar data set is selected from the transferred source data for reliable model construction. Consequently, with the JATL-based soft sensor, quality prediction of a new target grade is implemented without its own labeled data. Experimental results on two multigrade chemical processes validate that the JATL method can give rise to the improvement of model performance.

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Section: Introductionmentioning

confidence: 99%

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction

et al. 2023

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“…Polypropylene (PP) is regarded as an important commercial material because of its processability, excellent mechanical properties and chemical resistance. It has been widely used in many application fields such as packaging, automotive and medical materials 1–3 . However, the application of PP is restricted by the performance requirement with rapid industrial development.…”

Section: Introductionmentioning

confidence: 99%

“…It has been widely used in many application fields such as packaging, automotive and medical materials. [1][2][3] However, the application of PP is restricted by the performance requirement with rapid industrial development. For example, the applications in the field of automobile and household appliances have requirements for high mechanical performance.…”

Section: Introductionmentioning

confidence: 99%

Effects of halloysite nanotubes modified by organic phosphate on the performance improvement for polypropylene

Deng

Meng

et al. 2023

J of Applied Polymer Sci

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The enhancement of halloysite nanotubes (HNT) for polymer mechanical performance is limited due to the poor compatibility with matrix. In this work, hydrophobic 2,2 0 -methylene-bis-(4,6-di-tert-butylphenyl) organic phosphate (MBP) with multiple methyl groups was firstly used for HNT compatibilization modification. The hydrophobicity transition of modified HNT (HNT-M) and the dispersibility difference in isotactic polypropylene (iPP) matrix were distinguished by water contact angle and SEM analyses, respectively. In addition, the binding energy evaluated the interfacial interaction and compatibility with matrix before and after modification by molecular dynamic simulation. Correspondingly, the iPP performance was optimized after addition of HNT-M. The onset decomposition temperature of system at 5% and 10% weight loss was increased by 7 and 11 C compared with neat iPP, respectively. Moreover, the flexural modulus and impact strength of iPP/HNT-M system were 18.9% and 9.6% higher than neat iPP showing simultaneous enhancement. In addition, iPP/HNT-M system crystallized begin at higher temperature showing enhanced crystallization ability. Furthermore, the faster crystallization process and more uniform spherulite size were obtained. As a result, the enhancement for iPP performance was obtained when the compatibility between HNT and matrix was improved through surface modification.

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“…Besides, some industrial processes have dynamic characteristics and complex mechanisms, and the static soft sensor models do not perform well in such a environment. Hence, in 2021, Zhang et al [29] introduced an improved orthogonal sparse echo network to build a grey dynamic soft sensor model. And achieved good prediction results in propylene polymerization examples.…”

Section: Introductionmentioning

confidence: 99%

Multi‐layer features ensemble soft sensor regression model based on stacked autoencoder and vine copula

Chen

2023

Can J Chem Eng

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It is crucial in industrial processes to consider key variables to ensure safe operation and high product quality. Moreover, these variables are difficult to obtain using traditional measurement methods; hence, it makes sense to develop soft sensor regression models to process the variable prediction. However, there are numerous variables integrating noisy and redundant information in complex industrial processes. Using such variables in traditional regression models may result in reducing the model's efficiency and performance. Thus, this paper proposes a multi-layer feature ensemble soft sensor regression method using a stacked auto-encoder (SAE) and vine copula (ESAE-VCR) to address these problems. To do so, the number of neurons in the hidden layer of the SAE is determined by the principal component analysis (PCA). The multi-layer features of the process variables are extracted using a stacked AE, and the regression models are established for each feature layer. A linear regression ensemble method is used to combine the regression models with the multi-layer features to obtain the final predictive model that will estimate the values of the key variables. The effectiveness and practicality of the ESAE-VCR are validated by comparing them with several common soft measurement methods in two examples. In the numerical example, the ESAE-VCR yields an accuracy of prediction (R 2 ) of 0.9898 and a root mean square error (RMSE) of 0.1804. In the industrial example, the ESAE-VCR yields an R 2 of 0.9908 and an RMSE of 0.1205.

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Novel Gray Orthogonal Echo State Network Integrating the Process Mechanism for Dynamic Soft Sensor Development

Cited by 6 publications

References 33 publications

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction

Adversarial Transferred Data-Assisted Soft Sensor for Enhanced Multigrade Quality Prediction

Effects of halloysite nanotubes modified by organic phosphate on the performance improvement for polypropylene

Multi‐layer features ensemble soft sensor regression model based on stacked autoencoder and vine copula

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