Data-based modeling and control of nonlinear process systems using sparse identification: An overview of recent results

Process structure-based recurrent neural network modeling for predictive control: A comparative study

(2022) Mohammed S. Alhajeri et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

Identification of MIMO Wiener-type Koopman models for data-driven model reduction using deep learning

(2022) Jan C. Schulze et al.   COMPUTERS & CHEMICAL ENGINEERING

Physics-informed machine learning modeling for predictive control using noisy data

(2022) Mohammed S. Alhajeri et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

Modeling and Control of Nonlinear Processes Using Sparse Identification: Using Dropout to Handle Noisy Data

(2022) Fahim Abdullah et al.   Industrial & Engineering Chemistry Research

Machine learning modeling and predictive control of nonlinear processes using noisy data

(2021) Zhe Wu et al.   AICHE JOURNAL

Investigation and simulation of crystallization of high aspect ratio crystals with fragmentation

(2021) Ákos Borsos et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

Machine learning-based predictive control using noisy data: evaluating performance and robustness via a large-scale process simulator

(2021) Zhe Wu et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

Recent trends on hybrid modeling for Industry 4.0

(2021) Joel Sansana et al.   COMPUTERS & CHEMICAL ENGINEERING

Handling noisy data in sparse model identification using subsampling and co-teaching

(2021) Fahim Abdullah et al.   COMPUTERS & CHEMICAL ENGINEERING

Sparse-identification-based model predictive control of nonlinear two-time-scale processes

(2021) Fahim Abdullah et al.   COMPUTERS & CHEMICAL ENGINEERING

Operable adaptive sparse identification of systems (OASIS): application to chemical processes

(2020) Bhavana Bhadriraju et al.   AICHE JOURNAL

Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

(2020) Timur Bikmukhametov et al.   COMPUTERS & CHEMICAL ENGINEERING

SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics

(2020) Kadierdan Kaheman et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling

(2020) Dongheon Lee et al.   PLoS Computational Biology

Data-based reduced-order modeling of nonlinear two-time-scale processes

(2020) Fahim Abdullah et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations

(2020) Sheng Zhang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Overview of Surrogate Modeling in Chemical Process Engineering

(2019) Kevin McBride et al.   CHEMIE INGENIEUR TECHNIK

Reactive SINDy: Discovering governing reactions from concentration data

(2019) Moritz Hoffmann et al.   JOURNAL OF CHEMICAL PHYSICS

Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings

(2019) Kathleen P. Champion et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Real-Time Adaptive Machine-Learning-Based Predictive Control of Nonlinear Processes

(2019) Zhe Wu et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Deep learning of dynamics and signal-noise decomposition with time-stepping constraints

(2019) Samuel H. Rudy et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine learning-based adaptive model identification of systems: Application to a chemical process

(2019) Bhavana Bhadriraju et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

Deep hybrid modeling of chemical process: Application to hydraulic fracturing

(2019) Mohammed Saad Faizan Bangi et al.   COMPUTERS & CHEMICAL ENGINEERING

Integrating production scheduling and process control using latent variable dynamic models

(2019) Calvin Tsay et al.   CONTROL ENGINEERING PRACTICE

Sparse identification of nonlinear dynamics for rapid model recovery

(2018) Markus Quade et al.   CHAOS

Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review

(2018) Sohrab Zendehboudi et al.   APPLIED ENERGY

Data-driven identification of interpretable reduced-order models using sparse regression

(2018) Abhinav Narasingam et al.   COMPUTERS & CHEMICAL ENGINEERING

Robust data-driven discovery of governing physical laws with error bars

(2018) Sheng Zhang et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Fault-Tolerant Economic Model Predictive Control Using Error-Triggered Online Model Identification

(2017) Anas Alanqar et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

Error-triggered on-line model identification for model-based feedback control

(2016) Anas Alanqar et al.   AICHE JOURNAL

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Low-Dimensional Approach for Reconstruction of Airfoil Data via Compressive Sensing

(2015) Zhe Bai et al.   AIAA JOURNAL

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

(2013) Moritz von Stosch et al.   COMPUTERS & CHEMICAL ENGINEERING

Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models

(2007) Eleni Aggelogiannaki et al.   COMPUTERS & CHEMICAL ENGINEERING