Supercritical water gasification thermodynamic study and hybrid modeling of machine learning with the ideal gas model: Application to gasification of microalgae …

ÍAM Zelioli, ACD Freitas, AP Mariano - Energy, 2024 - Elsevier
This study presents a hybrid modeling approach that combines a simplified
phenomenological model with machine learning techniques for predicting variables in the …

The forefront of chemical engineering research

L Torrente-Murciano, JB Dunn… - Nature Chemical …, 2024 - nature.com
The forefront of chemical engineering research | Nature Chemical Engineering Skip to main
content Thank you for visiting nature.com. You are using a browser version with limited support …

A tutorial review of machine learning-based model predictive control methods

Z Wu, PD Christofides, W Wu, Y Wang… - Reviews in Chemical …, 2024 - degruyter.com
This tutorial review provides a comprehensive overview of machine learning (ML)-based
model predictive control (MPC) methods, covering both theoretical and practical aspects. It …

[HTML][HTML] Model predictive control of an electrically-heated steam methane reformer

B Çıtmacı, X Cui, F Abdullah, D Richard… - Digital Chemical …, 2024 - Elsevier
Steam methane reforming (SMR) is one of the most widely used hydrogen (H 2) production
processes. In addition to its extensive utilization in industrial sectors, hydrogen is expanding …

Feedback control of an experimental electrically-heated steam methane reformer

B Çıtmacı, D Peters, X Cui, F Abdullah… - … Research and Design, 2024 - Elsevier
Steam methane reforming (SMR) is the most common industrial process to produce
hydrogen (H 2) from methane and water vapor. The SMR reactions are overall highly …

[HTML][HTML] Machine learning-based predictive control of an electrically-heated steam methane reforming process

Y Wang, X Cui, D Peters, B Çıtmacı, A Alnajdi… - Digital Chemical …, 2024 - Elsevier
Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient
energy carrier that significantly reduces greenhouse gas emissions. However, the primary …

[HTML][HTML] The enabling technologies for digitalization in the chemical process industry

M Pietrasik, A Wilbik, P Grefen - Digital Chemical Engineering, 2024 - Elsevier
In this paper, we provide an overview of the technologies that enable digitalization in the
chemical process industry and describe their applications to solve problems in industrial …

[HTML][HTML] Improved Fault Detection and Diagnosis Using Graph Auto Encoder and Attention-based Graph Convolution Networks

P Brahmbhatt, R Patel, A Maheshwari… - Digital Chemical …, 2024 - Elsevier
A powerful fault detection and diagnosis (FDD) system plays a pivotal role in achieving
operational excellence by maximizing system performance, optimizing maintenance …

High-throughput automated membrane reactor system: The case of CO2/bicarbonate electroreduction

AB Navarro, R Garcia-Valls, A Nogalska - Chemical Engineering and …, 2024 - Elsevier
Nowadays, CO 2 capture and valorization are vital centers of investigation to help mitigate
the effects of climate change. Considering that to achieve high conversion efficiencies a …

Learning-based Model Predictive Control of an Ammonia Synthesis Reactor

TO Cabral, A Bagheri… - 2024 American Control …, 2024 - ieeexplore.ieee.org
We investigate the application of state-of-the-art recurrent machine learning methods to
tackle the computational challenges associated with the real-time solvability of a packed …