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Advances and opportunities in machine learning for process data analytics

Computers & chemical engineering, 2019-07, Vol.126, p.465-473

2019

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Data-driven methods for batch data analysis – A critical overview and mapping on the complexity scale

Computers & chemical engineering, 2019-05, Vol.124 (C), p.1-13

2019

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Towards artificial intelligence at scale in the chemical industry

AIChE journal, 2022-06, Vol.68 (6), p.n/a

2022

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Monitoring Chemical Processes Using Judicious Fusion of Multi-Rate Sensor Data

Sensors (Basel, Switzerland), 2019-05, Vol.19 (10), p.2240

2019

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Diagnosis of multiple and unknown faults using the causal map and multivariate statistics

Journal of process control, 2015-04, Vol.28, p.27-39

2015

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Sensor Fusion with Irregular Sampling and Varying Measurement Delays

Industrial & engineering chemistry research, 2020-02, Vol.59 (6), p.2328-2340

2020

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Data‐driven plant‐model mismatch estimation for dynamic matrix control systems

International journal of robust and nonlinear control, 2020-11, Vol.30 (17), p.7103-7129

2020

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Plant–Model Mismatch Estimation from Closed-Loop Data for State-Space Model Predictive Control

Industrial & engineering chemistry research, 2018-03, Vol.57 (10), p.3732-3741

2018

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A Unifying and Integrated Framework for Feature Oriented Analysis of Batch Processes

Industrial & engineering chemistry research, 2017-08, Vol.56 (30), p.8590-8605

2017

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Data-Driven Optimization of an Industrial Batch Polymerization Process Using the Design of Dynamic Experiments Methodology

Industrial & engineering chemistry research, 2020-08, Vol.59 (33), p.14868-14880

2020

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A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part IAssessing Detection Strength

Industrial & engineering chemistry research, 2016-05, Vol.55 (18), p.5342-5358

2016

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Autocovariance-based MPC model mismatch estimation for systems with measurable disturbances

Journal of process control, 2017-07, Vol.55, p.42-54

2017

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Fault diagnosis based on Fisher discriminant analysis and support vector machines

Computers & chemical engineering, 2004-07, Vol.28 (8), p.1389-1401

2004

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Benchmark study of reinforcement learning in controlling and optimizing batch processes

Journal of advanced manufacturing and processing, 2022-04, Vol.4 (2), p.n/a

2022

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Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

Chemometrics and intelligent laboratory systems, 2000-03, Vol.50 (2), p.243-252

2000

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Big Data Analytics in Chemical Engineering

Annual review of chemical and biomolecular engineering, 2017-06, Vol.8 (1), p.63-85

2017

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Semi-supervised online soft sensor maintenance experiences in the chemical industry

Journal of process control, 2018-07, Vol.67, p.23-34

2018

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Investigation of transfer learning for image classification and impact on training sample size

Chemometrics and intelligent laboratory systems, 2021-04, Vol.211, p.104269, Article 104269

2021

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Recent trends on hybrid modeling for Industry 4.0

Computers & chemical engineering, 2021-08, Vol.151, p.107365, Article 107365

2021

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Industrial experiences with multivariate statistical analysis of batch process data

Chemometrics and intelligent laboratory systems, 2006-04, Vol.81 (2), p.109-119

2006

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Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis

Chemometrics and intelligent laboratory systems, 2000-05, Vol.51 (1), p.81-93

2000

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Profile-driven Features for Offline Quality Prediction in Batch Processes

Computer Aided Chemical Engineering, 2017-01, Vol.40, p.1501-1506

2017

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Dimensionality reduction for visualizing industrial chemical process data

Control engineering practice, 2019-12, Vol.93, p.104189, Article 104189

2019

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Hybrid modeling for improved extrapolation and transfer learning in the chemical processing industry

Chemical engineering science, 2024-12, Vol.300, p.120568, Article 120568

2024

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Process monitoring using causal map and multivariate statistics: fault detection and identification

Chemometrics and intelligent laboratory systems, 2003-02, Vol.65 (2), p.159-178

2003

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Advances and opportunities in machine learning for process data analytics

Data-driven methods for batch data analysis – A critical overview and mapping on the complexity scale

Towards artificial intelligence at scale in the chemical industry

Monitoring Chemical Processes Using Judicious Fusion of Multi-Rate Sensor Data

Diagnosis of multiple and unknown faults using the causal map and multivariate statistics

Sensor Fusion with Irregular Sampling and Varying Measurement Delays

Data‐driven plant‐model mismatch estimation for dynamic matrix control systems

Plant–Model Mismatch Estimation from Closed-Loop Data for State-Space Model Predictive Control

A Unifying and Integrated Framework for Feature Oriented Analysis of Batch Processes

Data-Driven Optimization of an Industrial Batch Polymerization Process Using the Design of Dynamic Experiments Methodology

A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part IAssessing Detection Strength

Autocovariance-based MPC model mismatch estimation for systems with measurable disturbances

Fault diagnosis based on Fisher discriminant analysis and support vector machines

Benchmark study of reinforcement learning in controlling and optimizing batch processes

Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

Big Data Analytics in Chemical Engineering

Semi-supervised online soft sensor maintenance experiences in the chemical industry

Investigation of transfer learning for image classification and impact on training sample size

Recent trends on hybrid modeling for Industry 4.0

Industrial experiences with multivariate statistical analysis of batch process data

Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis

Profile-driven Features for Offline Quality Prediction in Batch Processes

Dimensionality reduction for visualizing industrial chemical process data

Hybrid modeling for improved extrapolation and transfer learning in the chemical processing industry

Process monitoring using causal map and multivariate statistics: fault detection and identification

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