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

Andreu Bonet Navarro,

Ricard Garcia‐Valls, Adrianna Nogalska

et al.

Chemical Engineering and Processing - Process Intensification, Journal Year: 2024, Volume and Issue: 198, P. 109723 - 109723

Published: Feb. 27, 2024

Language: Английский

The forefront of chemical engineering research DOI Open Access
Laura Torrente‐Murciano, Jennifer B. Dunn, Panagiotis D. Christofides

et al.

Nature Chemical Engineering, Journal Year: 2024, Volume and Issue: 1(1), P. 18 - 27

Published: Jan. 11, 2024

Language: Английский

Citations

14
Supercritical water gasification thermodynamic study and hybrid modeling of machine learning with the ideal gas model: Application to gasification of microalgae biomass DOI

J.M. Santos J,

Ícaro Augusto Maccari Zelioli,

En F

et al.

Energy, Journal Year: 2024, Volume and Issue: 291, P. 130287 - 130287

Published: Jan. 21, 2024

Language: Английский

Citations

13
The enabling technologies for digitalization in the chemical process industry DOI Creative Commons
Marcin Pietrasik, Anna Wilbik, Paul Grefen

et al.

Digital Chemical Engineering, Journal Year: 2024, Volume and Issue: 12, P. 100161 - 100161

Published: June 5, 2024

In this paper, we provide an overview of the technologies that enable digitalization in chemical process industry and describe their applications to solve problems industrial settings. This is done through identification categorization these technologies, thereby providing structure otherwise loosely connected basket casting a spotlight on state-of-the-art offer great potential but are still underutilized applications. Furthermore, identify problem domains characterize connect them development aspects lend themselves digital solutions. For each connections, select most essential bridging gap between solution. allows practitioners better understand relevancy provides starting point for further investigation The connections substantiated by reference successful applications, highlighting previous works have been published field. They verified experts brainstorm sessions, interviews, workshop.

Language: Английский

Citations

5
Efficient data-driven predictive control of nonlinear systems: A review and perspectives DOI Creative Commons
Xiaojie Li,

Meng Yan,

Xuewen Zhang

et al.

Digital Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 100219 - 100219

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review DOI Creative Commons
Wenlong Wang, Zhe Wu, Dominic Peters

et al.

Digital Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 100237 - 100237

Published: April 1, 2025

Language: Английский

Citations

0
Electrochemical and Chemometric Authentication of Panax notoginseng from Different Geographical Origins Using Graphene-Modified Screen-Printed Electrodes DOI Creative Commons
Shujing Wang, Jiafu Wang,

Wenjia Mi

et al.

International Journal of Electrochemical Science, Journal Year: 2025, Volume and Issue: unknown, P. 101050 - 101050

Published: April 1, 2025

Language: Английский

Citations

0
Feedback control of an experimental electrically-heated steam methane reformer DOI

Berkay Çıtmacı,

Dominic Peters, Xiaodong Cui

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 206, P. 469 - 488

Published: May 18, 2024

Language: Английский

Citations

3
A tutorial review of machine learning-based model predictive control methods DOI Creative Commons
Zhe Wu, Panagiotis D. Christofides, Wanlu Wu

et al.

Reviews in Chemical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It analysis closed-loop stability based on the generalization error ML models addresses challenges such as data scarcity, quality, curse dimensionality, uncertainty, computational efficiency, safety from modeling perspectives. The application these methods is demonstrated using nonlinear chemical process example, with open-source code available GitHub. paper concludes discussion future research directions in ML-based MPC.

Language: Английский

Citations

3
Model predictive control of an electrically-heated steam methane reformer DOI Creative Commons

Berkay Çıtmacı,

Xiaodong Cui, Fahim Abdullah

et al.

Digital Chemical Engineering, Journal Year: 2023, Volume and Issue: 10, P. 100138 - 100138

Published: Dec. 27, 2023

Steam methane reforming (SMR) is one of the most widely used hydrogen (H2) production processes. In addition to its extensive utilization in industrial sectors, expanding it share as a clean energy carrier, and more sustainable efficient H2 methods are continuously being explored developed. One method replaces conventional fossil fuel-based heating with electrical through flow electrons across reformer. At UCLA, an experimental setup was built electrically heated steam process. This paper describes system components, explains digitalization introduces for building first-principles-based dynamic process model using parameters estimated via data-driven from data. The modeling approach uses lumped parameter approximation employs algebraic equations solve gas-phase variables. reaction calculated steady-state data, temperature change modeled respect electric current first-order model. overall then computational predictive control (MPC) scheme drive new set-point under unperturbed flowrate disturbance cases. performance robustness proposed MPC compared ones classical proportional-integral (PI) controller demonstrated be superior terms closed-loop response, robustness, constraint handling.

Language: Английский

Citations

7
Improved fault detection and diagnosis using graph auto encoder and attention-based graph convolution networks DOI Creative Commons
Parth Brahmbhatt, Rahul Patel,

Abhilasha Maheshwari

et al.

Digital Chemical Engineering, Journal Year: 2024, Volume and Issue: 11, P. 100158 - 100158

Published: May 24, 2024

A powerful fault detection and diagnosis (FDD) system plays a pivotal role in achieving operational excellence by maximizing performance, optimizing maintenance strategies, ensuring the longevity resilience of process plants. In context FDD for multivariate sensor data, this study presents an improved approach using graph-based neural networks. This graph network uses adjacency matrix developed extracting expert domain knowledge topological information multi-sensor system. additional representation is incorporated along with data to capture spatial temporal networks efficiently. regard, we propose evaluate: 1) Graph Auto Encoder (GAE) based strategy 2) An Attention-based Spatial Temporal Convolution Network (ASTGCN) methodology. By leveraging form graphs, GAE captures complex relationships dependencies among sensors, enabling effective anomaly detection, which identifies abnormal patterns deviations from normal behavior, thus indicating potential faults The ASTGCN incorporates attention mechanisms selectively focus on relevant nodes their diagnosis. effectiveness proposed demonstrated benchmark Tennessee Eastman Process (TEP) problem. results show that approaches outperform traditional methods highlight importance systems.

Language: Английский

Citations

2