Application of data-driven modeling approaches to industrial hydroprocessing units

Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS)

(2021) Bhavana Bhadriraju et al.   COMPUTERS & CHEMICAL ENGINEERING

Modeling the Hydrocracking Process with Deep Neural Networks

(2020) Wenjiang Song et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Subspace based model identification for missing data

(2020) Nikesh Patel et al.   AICHE JOURNAL

Application of Koopman operator for model-based control of fracture propagation and proppant transport in hydraulic fracturing operation

(2020) Abhinav Narasingam et al.   JOURNAL OF PROCESS CONTROL

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

Process Fault Prognosis Using Hidden Markov Model–Bayesian Networks Hybrid Model

(2019) Mihiran Galagedarage Don et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Regression on dynamic PLS structures for supervised learning of dynamic data

(2018) Yining Dong et al.   JOURNAL OF PROCESS CONTROL

Subspace identification for data-driven modeling and quality control of batch processes

(2016) Brandon Corbett 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

Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel

(2015) Saeid Shokri et al.   Petroleum Science

A dynamic non-isothermal model for a hydrocracking reactor: Model development by the method of continuous lumping and application to an industrial unit

(2012) Hasan Sildir et al.   JOURNAL OF PROCESS CONTROL

Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach

(2011) Gang Li et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS

Kinetic parameter estimation and simulation of trickle-bed reactor for hydrodesulfurization of crude oil

(2010) Aysar T. Jarullah et al.   CHEMICAL ENGINEERING SCIENCE

Inferential sensor for on-line monitoring of ammonium bisulfate formation temperature in coal-fired power plants

(2008) Fengqi Si et al.   FUEL PROCESSING TECHNOLOGY

Application of Principal Component Analysis for Monitoring and Disturbance Detection of a Hydrotreating Process

(2008) Stella Bezergianni et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Artificial neural network modeling techniques applied to the hydrodesulfurization process

(2008) Enrique Arce-Medina et al.   MATHEMATICAL AND COMPUTER MODELLING