Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network

Zhao, Taoyan; Li, Ping; Cao, Jiangtao

doi:10.1016/j.isatra.2018.10.014

Cited by 37 publications

(5 citation statements)

References 32 publications

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“…These models serve to unite the best of the two methods that act strongly in solving complex problems [53]. Fuzzy neural networks work in issues in the area of the industry [11,54,55], controls, and actuation in robots [56][57][58][59], sectors of the economy [60][61][62], pulsar detection [63] and in the prediction of process failures [64][65][66][67]. Already in health, models have highlights in different performances.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Guimarães

Souza

Araújo

et al. 2019

BDCC

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Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.

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Section: Fuzzy Neural Networkmentioning

confidence: 99%

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Guimarães

Souza

Araújo

et al. 2019

BDCC

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“…The coking diagnostic process relies on data-driven soft sensor techniques that utilize accessible cracking process parameters as input variables for artificial intelligence algorithms. By employing feature extraction and modeling, a relationship with coking thickness is established [8]. However, within industrial production, cracker industrial data are marked by multimodality, nonlinearity, and non-Gaussian distribution patterns.…”

Section: Introductionmentioning

confidence: 99%

Study on a Soft Sensor for Coking Diagnosis Using Bayesian t-Distributed Mixed Regression Modeling

xia,

Peng,

Cui

et al. 2024

Preprint

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In the process of ethylene production by steam cracking, the coking diagnosis of the furnace tube of the cracking furnace is of great significance. Due to the existence of multimodal, nonlinear, non-Gaussian, and strong noise characteristics of the operating data of the cracking furnace, this paper proposes a soft sensor for coking diagnosis using bayesian t-distributed mixed regression modeling, which realizes the effective characterization of the multimodal, nonlinear, and non-Gaussian data through the hybrid model, and the model's parameter estimation is completed by the VBEM algorithm under the Bayesian framework to guarantee the anti-interference ability of the model. distribution to ensure the anti-interference ability of the model and the parameter estimation of the model is accomplished by the VBEM algorithm under the Bayesian framework. Finally, through simulation experiments and real industrial data experiments, as well as comparative analyses with PLSR, GMR, and GPR models, the model is verified to have good robustness, excellent prediction accuracy and robustness, which further confirms its potential application value in the diagnosis of furnace tube coking.

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“…Although mechanistic model-based approaches have been employed early on, their accuracy in coking inference is limited due to challenges in accurately obtaining parameters for crucial mechanistic models [9][10][11]. On the other hand, data-driven coking diagnosis offers a promising solution by utilizing available cracking process parameters as input variables for artificial intelligence algorithms, establishing relationships with coking thickness through feature extraction and modeling [12]. Among these techniques, artificial neural networks have been widely adopted for identifying operating conditions and developing a system model for the stochastic distribution of outlet temperatures, laying the foundation for advanced coke on temperature (COT) control during the ethylene cracking furnace coking process [13][14].…”

Section: Introductionmentioning

confidence: 99%

Concentration Diagnosis in Soft Sensing Based on Bayesian T-Distribution Mixture Regression

Xia,

Peng,

Cui

et al. 2023

Fuzzy Systems and Data Mining IX

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This study presents a cutting-edge soft sensing approach for coke-making diagnostics, aimed at tackling the challenges posed by multifaceted, nonlinear, non-Gaussian, and noisy operational data prevalent in coke-making ovens. Our proposed method leverages a Bayesian t-distributed mixed regression model, effectively capturing the intricate nature of multivariate, nonlinear, and non-Gaussian data. The utilization of the t-distribution ensures the model’s resilience to interference, with model parameter estimation achieved within a Bayesian framework. Conducting simulation experiments and real industrial experiments, as well as comparative analysis with PLSR, GMR, and GPR models, we demonstrate the model’s good robustness, excellent prediction accuracy, and robustness, further confirming its potential application in coking diagnosis.

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Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network

Cited by 37 publications

References 32 publications

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Study on a Soft Sensor for Coking Diagnosis Using Bayesian t-Distributed Mixed Regression Modeling

Concentration Diagnosis in Soft Sensing Based on Bayesian T-Distribution Mixture Regression

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