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 …
opportunity for both mitigating climate change and producing a valuable chemical feedstock …
Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework
Artificial intelligence (AI) is an emerging technology that has great potential in reducing
energy consumption, environmental burdens, and operational risks of chemical production …
energy consumption, environmental burdens, and operational risks of chemical production …
Artificial neural network and its application research progress in chemical process
L Sun, F Liang, W Cui - arXiv preprint arXiv:2110.09021, 2021 - arxiv.org
Most chemical processes, such as distillation, absorption, extraction, and catalytic reactions,
are extremely complex processes that are affected by multiple factors. The relationships …
are extremely complex processes that are affected by multiple factors. The relationships …
Energy-saving reduced-pressure extractive distillation with heat integration for separating the biazeotropic ternary mixture tetrahydrofuran–methanol–water
There is rich literature on the separation of binary azeotropic mixtures, whereas few studies
exist on the separation of biazeotropic ternary mixtures. In this work, we propose a …
exist on the separation of biazeotropic ternary mixtures. In this work, we propose a …
Application of artificial neural networks for testing long-term energy policy targets
DJ Đozić, BDG Urošević - Energy, 2019 - Elsevier
The paper analyses a model of the EU energy system by means of artificial neural networks.
This model is based on the prediction of CO 2 emissions until 2050 taking into account the …
This model is based on the prediction of CO 2 emissions until 2050 taking into account the …
A novel exergy-related fault detection and diagnosis framework with transformer-based conditional generative adversarial networks for hot strip mill process
C Zhang, K Peng, J Dong, R Jiao - Control Engineering Practice, 2024 - Elsevier
Fault detection and diagnosis (FDD) plays a crucial role in the iron and steel industry.
However, the iron and steel industry have unique complex characteristics such as high …
However, the iron and steel industry have unique complex characteristics such as high …
Comparison between artificial neural network and rigorous mathematical model in simulation of industrial heavy naphtha reforming process
In this study, an artificial neural network (ANN) model was developed and compared with a
rigorous mathematical model (RMM) to estimate the performance of an industrial heavy …
rigorous mathematical model (RMM) to estimate the performance of an industrial heavy …
Artificial Neural Network to model managerial timing decision: Non-linear evidence of deviation from target leverage
HI Hussain, F Kamarudin, HMT Thaker… - International Journal of …, 2019 - Springer
The current study highlights the utilization of a non-linear model to analyze an important
decision-making process in the study of corporate finance where managers are deciding on …
decision-making process in the study of corporate finance where managers are deciding on …
A framework for energy optimization of distillation process using machine learning‐based predictive model
The distillation process is one of the most common and energy‐intensive processes in the
chemical industry. Most chemical processes are nonlinear and complex, because of which, it …
chemical industry. Most chemical processes are nonlinear and complex, because of which, it …
Optimisation of methanol distillation using GA and neural network hybrid
Distillation is an energy-intensive non-stationary process represented using non-linear
model equations and involves multiple objectives. For such processes, data-based multi …
model equations and involves multiple objectives. For such processes, data-based multi …