Machine learning applications in catalytic hydrogenation of carbon dioxide to methanol: A comprehensive review

EG Aklilu, T Bounahmidi - International Journal of Hydrogen Energy, 2024 - Elsevier
The catalytic hydrogenation of carbon dioxide (CO 2) to methanol presents a significant
opportunity for both mitigating climate change and producing a valuable chemical feedstock …

Dynamic real-time energy saving control of pressure-swing distillation based on artificial neural networks

H Li, W Wang, Y Wang, C Li, Y Wang, Z Zhu… - Chemical Engineering …, 2023 - Elsevier
Parameter variation can increase economic costs and energy consumption in distillation
process when disturbances occur. In this work, a dynamic optimization control model based …

Optimisation of methanol distillation using GA and neural network hybrid

AK Wolday, M Ramteke - Materials and Manufacturing Processes, 2023 - Taylor & Francis
Distillation is an energy-intensive non-stationary process represented using non-linear
model equations and involves multiple objectives. For such processes, data-based multi …

Accelerating active catalyst discovery: a probabilistic prediction-based screening methodology with applications in dry reforming of methane

H Park, J Roh, H Cho, I Ro, J Kim - Journal of Materials Chemistry A, 2024 - pubs.rsc.org
Dry reforming of methane (DRM) is a promising technology for syngas production from CH4
and CO2. However, discovering feasible and efficient catalysts remains challenging despite …

Grey‐box modelling for estimation of optimum cut point temperature of crude distillation column

J Shahzad, I Ahmad, M Ahsan, F Ahmad… - CAAI Transactions …, 2024 - Wiley Online Library
A grey‐box modelling framework was developed for the estimation of cut point temperature
of a crude distillation unit (CDU) under uncertainty in crude composition and process …

Novel natural gradient boosting-based probabilistic prediction of physical properties for polypropylene-based composite data

H Park, C Joo, J Lim, J Kim - Engineering Applications of Artificial …, 2024 - Elsevier
Accurately predicting the physical properties of polypropylene composites is challenging
because they are highly complex due to the numerous combinations of materials used in …

A genetic algorithm-based optimal selection and blending ratio of plastic waste for maximizing economic potential

C Joo, J Lee, J Lim, J Kim, H Cho - Process Safety and Environmental …, 2024 - Elsevier
Pyrolysis of plastic waste presents an innovative solution to convert plastics into valuable
fuels like oil and gas, thereby contributing to circular economy and sustainability. The …

Hyperparameter Optimization of the Machine Learning Model for Distillation Processes

KC Oh, H Kwon, SY Park, SJ Kim… - International Journal of …, 2024 - Wiley Online Library
This study was conducted to enhance the efficiency of chemical process systems and
address the limitations of conventional methods through hyperparameter optimization …

Development of AI-based process controller of sour water treatment unit using deep reinforcement learning

H Wang, Y Guo, L Li, S Li - Journal of the Taiwan Institute of Chemical …, 2024 - Elsevier
Background Due to the variability in the feedstock conditions and the nonlinearity of the sour
water strip** process, determining the optimal operating conditions for Sour Water …

Artificial intelligence models for yield efficiency optimization, prediction, and production scalability of essential oil extraction processes from citrus fruit exocarps

SE Fajardo Muñoz, AJ Freire Castro… - Frontiers in Chemical …, 2023 - frontiersin.org
Introduction: Excessive demand, environmental problems, and shortages in market-leader
countries have led the citrus (essential) oil market price to drift to unprecedented high levels …