Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

Yuan, Xiaofeng; Li, Lin; Wang, Yalin

doi:10.1109/tii.2019.2902129

Cited by 393 publications

(131 citation statements)

References 27 publications

Supporting

Mentioning

130

Contrasting

Order By: Relevance

“…e early attempts to data-driven soft sensors for quality estimation mainly focus on global modeling techniques, such as multivariate statistical techniques [19,20], artificial neural networks [3,21], support vector regression [22], and Gaussian process regression [4,5]. Recently, deep learning methods have also been introduced to soft sensor applications [23].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

Jin

Wang

et al. 2020

Advances in Polymer Technology

View full text Add to dashboard Cite

e lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation-(MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. is method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. e proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

Jin

Wang

et al. 2020

Advances in Polymer Technology

View full text Add to dashboard Cite

show abstract

“…In addition, dynamic modeling of soft sensor is a significant problem that should be addressed. In recent years, process data dynamic modeling approaches have been used for soft sensor, such as linear dynamic systems and long‐short term memory . For the state estimation problem for nonlinear systems, the extended Kalman filter (EKF) is employed, which is much favorable in practical process engineering .…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, process data dynamic modeling approaches have been used for soft sensor, such as linear dynamic systems and long-short term memory. 42,43 For the state estimation problem for nonlinear systems, the extended Kalman filter (EKF) is employed, which is much favorable in practical process engineering. [44][45][46] In consideration of the errors resulting from linearization, a new robust design approach for a discrete-time EKF is proposed.…”

Section: Introductionmentioning

confidence: 99%

Online estimation for catalyst activity of acetylene hydrogenation reactor

Xie

Zhang

et al. 2020

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

In the ethylene industry, the high purity of the ethylene product depends on hydrogenation in acetylene hydrogenation reactor. Because the catalyst deactivation leads to the moving of the operating point, the operation scheme must be adjusted continually according to the catalyst activity. It is necessary to estimate the catalyst activity online. Based on the discrete dynamic model of the acetylene hydrogenation reactor, the extended Kalman filter (EKF) is used to build the soft sensor for catalyst activity. Considering that EKF involves the large computation costs, we propose a method that estimates the parameters of the time‐varying deactivation kinetics model for the tradeoff of accuracy and complexity. The method is effective to reduce computation complexity of estimation, and simultaneously, the accuracy satisfies the process requirement.

show abstract

“…Meel et al 29,30 developed a complete dynamic risk assessment methodology for process facilities, termed as dynamic failure assessment, which aimed at estimating the dynamic probabilities of accident sequences. Yuan et al 35,36 proposed a deep learningbased variable-wise weighted stacked autoencoder for hierarchical output-related feature representation layer by layer and developed a supervised long short-term memory network to learn quality-relevant hidden dynamics for soft sensor application. Recently, deep learning techniques have been developed and gained great success in industrial processes.…”

mentioning

confidence: 99%

“…Recently, deep learning techniques have been developed and gained great success in industrial processes. Yuan et al 35,36 proposed a deep learningbased variable-wise weighted stacked autoencoder for hierarchical output-related feature representation layer by layer and developed a supervised long short-term memory network to learn quality-relevant hidden dynamics for soft sensor application. Shang et al 37 exploited deep belief network to build soft sensor for a crude distillation unit and discussed the unique advantages of deep learning for industrial processes.…”

mentioning

confidence: 99%

Dynamic risk assessment approach of riser recoil control failure during production test of marine natural gas hydrate

Chang

Zhang

et al. 2019

Energy Science & Engineering

View full text Add to dashboard Cite

The marine natural gas hydrate is considered to have huge resource potential in the South China Sea, and a sixty‐day production test has been successfully conducted in 2017. As the crucial and most vulnerable connection of floating platform and subsea wellhead, the risers are of paramount importance in safe production test of the hydrate. Due to harsh environment and the possible drive off and drift off events induced by failure of dynamic positioning system of the platform, the unplanned emergency disconnect of risers is an integral preventive measure to avoid accidents and secure the well. Once the disconnect occurs, the huge potential energy stored in the riser column will release and consequently cause violent axial recoil, which will cause serious consequences if the recoil control fails. This paper presented a quantitative risk assessment approach on the basis of dynamic Bayesian networks to dynamically predict the risk of riser recoil control failure during production test of hydrate. By using the proposed method, the dynamic failure risk of the riser recoil control was figured out, and the most contributory factors were identified. The practical application of the proposed methodology was demonstrated through a case study. Results indicated that the probability of riser recoil control failure can be significantly decreased within one year through equipment repair, and the failure and repair rates of shutoff valves and main air isolation valves are the most contributory factors. Some corresponding safety control measures were proposed to further mitigate the risk of riser recoil control failure during the production test of hydrate.

show abstract

Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

Cited by 393 publications

References 27 publications

Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

Online estimation for catalyst activity of acetylene hydrogenation reactor

Dynamic risk assessment approach of riser recoil control failure during production test of marine natural gas hydrate

Contact Info

Product

Resources

About