Mixed Student’s T-Distribution Regression Soft Measurement Model and Its Application Based on VI and MCMC
Bewaard in:
| Gepubliceerd in: | Processes vol. 13, no. 3 (2025), p. 861 |
|---|---|
| Hoofdauteur: | |
| Andere auteurs: | , , , |
| Gepubliceerd in: |
MDPI AG
|
| Onderwerpen: | |
| Online toegang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tags: |
Geen labels, Wees de eerste die dit record labelt!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3181727784 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2227-9717 | ||
| 024 | 7 | |a 10.3390/pr13030861 |2 doi | |
| 035 | |a 3181727784 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Li, Qirui |u Computer College, Guangdong University of Petrochemical Technology, Maoming 525000, China; <email>liqirui@gdupt.edu.cn</email> (Q.L.); <email>lcuix1819@gdupt.edu.cn</email> (C.L.); <email>jieguang@gdupt.edu.cn</email> (J.H.) | |
| 245 | 1 | |a Mixed Student’s T-Distribution Regression Soft Measurement Model and Its Application Based on VI and MCMC | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The conventional diagnostic techniques for ethylene cracker furnace tube coking rely on manual expertise, offline analysis and on-site inspection. However, these methods have inherent limitations, including prolonged inspection times, low accuracy and poor real-time performance. This makes it challenging to meet the requirements of chemical production. The necessity for high efficiency, high reliability and high safety, coupled with the inherent complexity of the production process, results in data that is characterized by multimodal, nonlinear, non-Gaussian and strong noise. This renders the traditional data processing and analysis methods ineffective. In order to address these issues, this paper puts forth a novel soft measurement approach, namely the ‘Mixed Student’s t-distribution regression soft measurement model based on Variational Inference (VI) and Markov Chain Monte Carlo (MCMC)’. The initial variational distribution is selected during the initialization step of VI. Subsequently, VI is employed to iteratively refine the distribution in order to more closely approximate the true posterior distribution. Subsequently, the outcomes of VI are employed to initiate the MCMC, which facilitates the placement of the iterative starting point of the MCMC in a region that more closely approximates the true posterior distribution. This approach allows the convergence process of MCMC to be accelerated, thereby enabling a more rapid approach to the true posterior distribution. The model integrates the efficiency of VI with the accuracy of the MCMC, thereby enhancing the precision of the posterior distribution approximation while preserving computational efficiency. The experimental results demonstrate that the model exhibits enhanced accuracy and robustness in the diagnosis of ethylene cracker tube coking compared to the conventional Partial Least Squares Regression (PLSR), Gaussian Process Regression (GPR), Gaussian Mixture Regression (GMR), Bayesian Student’s T-Distribution Mixture Regression (STMR) and Semi-supervised Bayesian T-Distribution Mixture Regression (SsSMM). This method provides a scientific basis for optimizing and maintaining the ethylene cracker, enhancing its production efficiency and reliability, and effectively addressing the multimodal, non-Gaussian distribution and uncertainty of the coking data of the ethylene cracker furnace tube. | |
| 653 | |a Accuracy | ||
| 653 | |a Data processing | ||
| 653 | |a Markov chains | ||
| 653 | |a Regression models | ||
| 653 | |a Ethylene | ||
| 653 | |a Normal distribution | ||
| 653 | |a Efficiency | ||
| 653 | |a Least squares method | ||
| 653 | |a Measurement techniques | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Chemical industry | ||
| 653 | |a Machine learning | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Inspection | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Carbon | ||
| 653 | |a Temperature | ||
| 653 | |a Reliability | ||
| 653 | |a Process controls | ||
| 653 | |a Variables | ||
| 653 | |a Gaussian process | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Mixtures | ||
| 653 | |a Real time | ||
| 653 | |a Coking | ||
| 653 | |a Probability distribution | ||
| 653 | |a Parameter estimation | ||
| 700 | 1 | |a Li, Cuixian |u Computer College, Guangdong University of Petrochemical Technology, Maoming 525000, China; <email>liqirui@gdupt.edu.cn</email> (Q.L.); <email>lcuix1819@gdupt.edu.cn</email> (C.L.); <email>jieguang@gdupt.edu.cn</email> (J.H.) | |
| 700 | 1 | |a Peng, Zhiping |u College of Information Engineering, Jiangmen Polytechnic College, Jiangmen 529090, China; <email>zhipingpeng@gdupt.edu.cn</email> | |
| 700 | 1 | |a Delong Cui |u Computer College, Guangdong University of Petrochemical Technology, Maoming 525000, China; <email>liqirui@gdupt.edu.cn</email> (Q.L.); <email>lcuix1819@gdupt.edu.cn</email> (C.L.); <email>jieguang@gdupt.edu.cn</email> (J.H.) | |
| 700 | 1 | |a He, Jieguang |u Computer College, Guangdong University of Petrochemical Technology, Maoming 525000, China; <email>liqirui@gdupt.edu.cn</email> (Q.L.); <email>lcuix1819@gdupt.edu.cn</email> (C.L.); <email>jieguang@gdupt.edu.cn</email> (J.H.) | |
| 773 | 0 | |t Processes |g vol. 13, no. 3 (2025), p. 861 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3181727784/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3181727784/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3181727784/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |