NSTL国家科技图书文献中心

1. Time-varying Setpoint Tracking for Batch Process Control using Reinforcement Learning NSTL国家科技图书文献中心

M U Abuthahir | Nabil Magbool Jan - 《2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV 2024),Vol.2》 - International Conference on Control, Automation, Robotics and Vision - 2024, - 1148～1153 - 共6页

摘要：Batch processes are indispensable for the production of low-volume and high-value products. However, control of a batch process is challenging due to the inherent nonlinearity, and time-varying charac...

关键词： Tracking control | Batch process | Nonlinear model predictive control | Q learning | Reinforcement learning

2. Convex optimization approach to design sensor networks using information theoretic measures NSTL国家科技图书文献中心

Arjun M | Nabil Magbool Jan - 《AIChE Journal》 - 2024,70(2) - n/a～n/a - 共15页

摘要：Abstract Accurate and precise estimation of process variables is key to effective process monitoring. The estimation accuracy depends on the choice of the sensor network. Therefore, the article aims a...

关键词： data reconciliation | entropy | Kullback–Leibler divergence | sensor network design

3. Quality-driven Gaussian mixture variational probabilistic network for soft sensor application in PET/PA6 polymerization process NSTL国家科技图书文献中心

Ruimin Xie | Yuxiang Liu ... - 《Computers & Chemical Engineering: An International Journal of Computer Applications in Chemical Engineering》 - 2024,181(Feb.) - 108543.1～108543.11 - 共11页 - 被引量：2

摘要：Variational autoencoder has been widely used to build soft sensor models as a deep unsupervised feature extractor in recent years. However, the vanilla VAE employs a single diagonal Gaussian distribut...

关键词： Variational autoencoder (VAE) | Gaussian mixture variational probabilistic | network (QGMVPN) | Soft sensor | Non-Gaussian process data | Polymerization process

4. No-Delay Multimodal Process Monitoring Using Kullback-Leibler Divergence-Based Statistics in Probabilistic Mixture Models NSTL国家科技图书文献中心

Yue Cao | Nabil Magbool Jan ... - 《IEEE transactions on automation science and engineering: a publication of the IEEE Robotics and Automation Society》 - 2023,20(1) - 167～178 - 共12页 - 被引量：3

摘要：The primary goal of multimodal process monitoring is to detect abnormalities or occurrence of faults. However, the profound challenge in the multimodal monitoring problem is that it is difficult to qu...

关键词： Process monitoring | Principal component analysis | Bayes methods | Probabilistic logic | Delay effects | Sensitivity | Gaussian distribution

5. Data analytics for oil sands subcool prediction - a comparative study of machine learning algorithms NSTL国家科技图书文献中心

Chaoqun Li | Nabil Magbool Jan ... - 《10th IFAC International Symposium on Advanced Control of Chemical Processes: ADCHEM 2018, Shenyang, China, 25-27 July 2018》 - IFAC International Symposium on Advanced Control of Chemical Processes - 2018, - 886～891 - 共6页

摘要：Steam Assisted Gravity Drainage (SAGD) is an efficient and widely used technology to extract heavy oil from a reservoir. The accurate prediction of subcool plays a critical role in determining the eco...

关键词： Data analytics | Deep learning | Process application | Machine learning | SAGD | Subcool

NSTL主题词： Machine learning algorithms | Undercooling | Oilsands | Hydrocarbons

6. Stacking Approach for Soft Sensor Design in Steam Assisted Gravity Drainage Applications NSTL国家科技图书文献中心

Chaoqun Li | Nabil Magbool Jan ... - 《5th IFAC Workshop on Mining, Mineral and Metal Processing: MMM 2018, Shanghai, China, 23-25 August 2018》 - IFAC Workshop on Mining, Mineral and Metal Processing - 2018, - 30～35 - 共6页

摘要：This paper proposes a stacking soft sensor approach for the oil sands process, known as Steam Assisted Gravity Drainage (SAGD), which is an advanced underground bitumen extraction process. It involves...

关键词： Soft sensor | Stacking model | Machine learning | Oil sands | SAGD

NSTL主题词： Steam | Gravity drainage | Stacking | Water Vapor | Sensors

7. Robust soft sensor development using multi-rate measurements NSTL国家科技图书文献中心

Ouyang Wu | Hariprasad Kodamana ... - 《20th IFAC World Congress 2017: Toulouse, France, 9-14 July 2017, Part 16 of 24》 - IFAC World Congress - 2018, - 10190～10195 - 共6页

摘要：Two different types of measurements are often available for the key quality variables in process industries - (a) an accurate "slow-rate" laboratory measurements, and (b) a less accurate "fast-rate" o...

关键词： Soft sensor | Static model | Multi-rate | Expectation Maximization algorithm | T-distribution | Flat-topped t-distribution

NSTL主题词： Rate measurement, NOS | Sensors | Robustness

8. Optimal selection of sensor network and backed-off operating point based on economics NSTL国家科技图书文献中心

Nabil Magbool Jan | Sigurd Skogestad ... - 《2013 European control conference: ECC 2013, Zurich, Switzerland, 17-19 July 2013, Pages 3839-4615, [v.6]》 - European Control Conference - 2013, - 4472～4477 - 共6页

摘要：This paper discusses the simultaneous selection of measurements and economic backed off operating point when the nominal optimal operating point is constrained. However, operation at this point become...

关键词： Process control | Sensor networks | Multivariable control | Convex optimization

NSTL主题词： optimal selection | Workstations | Sensor networks | economics

9. Control of van de Vusse Reactor using Deep Reinforcement Learning NSTL国家科技图书文献中心

Rahul Yadav Ankaluga... | M U Abuthahir ... - 《2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV 2024),Vol.2》 - International Conference on Control, Automation, Robotics and Vision - 2024, - 1154～1159 - 共6页

摘要：van de Vusse reaction system in a continuous stirred tank reactor is a benchmark example to study the control of nonlinear non-minimum phase systems. Although different control strategies exist to dea...

10. Robust Optimal Sensor Selection using Information Theoretic Measures NSTL国家科技图书文献中心

Manjay Kumar | Rahul Yadav Ankaluga...... - 《2024 American Control Conference (ACC 2024), Vol.5: Toronto, Ontario, Canada.10-12 July 2024》 - American Control Conference - 2024, - 3687～3692 - 共6页

摘要：In this paper, we address the problem of optimal sensor selection to ensure estimability in linear, steady-state processes in case of sensor failures. To this end, we follow the data reconciliation fr...

摘要：Batch processes are indispensable for the production of low-volume and high-value products. However, control of a batch process is challenging due to the inherent nonlinearity, and time-varying charac...

关键词： Tracking control | Batch process | Nonlinear model predictive control | Q learning | Reinforcement learning

摘要：Abstract Accurate and precise estimation of process variables is key to effective process monitoring. The estimation accuracy depends on the choice of the sensor network. Therefore, the article aims a...

关键词： data reconciliation | entropy | Kullback–Leibler divergence | sensor network design

摘要：Variational autoencoder has been widely used to build soft sensor models as a deep unsupervised feature extractor in recent years. However, the vanilla VAE employs a single diagonal Gaussian distribut...

关键词： Variational autoencoder (VAE) | Gaussian mixture variational probabilistic | network (QGMVPN) | Soft sensor | Non-Gaussian process data | Polymerization process

摘要：The primary goal of multimodal process monitoring is to detect abnormalities or occurrence of faults. However, the profound challenge in the multimodal monitoring problem is that it is difficult to qu...

关键词： Process monitoring | Principal component analysis | Bayes methods | Probabilistic logic | Delay effects | Sensitivity | Gaussian distribution

摘要：Steam Assisted Gravity Drainage (SAGD) is an efficient and widely used technology to extract heavy oil from a reservoir. The accurate prediction of subcool plays a critical role in determining the eco...

关键词： Data analytics | Deep learning | Process application | Machine learning | SAGD | Subcool

NSTL主题词： Machine learning algorithms | Undercooling | Oilsands | Hydrocarbons

摘要：This paper proposes a stacking soft sensor approach for the oil sands process, known as Steam Assisted Gravity Drainage (SAGD), which is an advanced underground bitumen extraction process. It involves...

关键词： Soft sensor | Stacking model | Machine learning | Oil sands | SAGD

NSTL主题词： Steam | Gravity drainage | Stacking | Water Vapor | Sensors

摘要：Two different types of measurements are often available for the key quality variables in process industries - (a) an accurate "slow-rate" laboratory measurements, and (b) a less accurate "fast-rate" o...

关键词： Soft sensor | Static model | Multi-rate | Expectation Maximization algorithm | T-distribution | Flat-topped t-distribution

NSTL主题词： Rate measurement, NOS | Sensors | Robustness

摘要：This paper discusses the simultaneous selection of measurements and economic backed off operating point when the nominal optimal operating point is constrained. However, operation at this point become...

关键词： Process control | Sensor networks | Multivariable control | Convex optimization

NSTL主题词： optimal selection | Workstations | Sensor networks | economics

摘要：van de Vusse reaction system in a continuous stirred tank reactor is a benchmark example to study the control of nonlinear non-minimum phase systems. Although different control strategies exist to dea...

摘要：In this paper, we address the problem of optimal sensor selection to ensure estimability in linear, steady-state processes in case of sensor failures. To this end, we follow the data reconciliation fr...

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