Content
Pepole
CV
Implementing a model for absorbing the pollutant gas of the oil industry's smokestacks with the Simorgh supercomputer
| Post date: 2024/01/22 |
Implementing a model for absorbing the pollutant gas of the oil industry's smokestacks with the Simorgh supercomputer
Researchers at the Faculty of Chemical Engineering of Amirkabir University of Technology, with the help of the Simorgh supercomputer, have presented an artificial intelligence-based model for absorbing carbon dioxide from gas in the oil, gas, petrochemical, and power plant industries.
According to the public relations office of Amirkabir University of Technology, Mohsen Makari, one of the researchers of this project, stated: "Utilizing artificial intelligence methods, especially machine learning, helps us reduce time, cost, and safety and environmental risks in the design, construction, and operation of chemical industries.
He continued: "Therefore, in this research, with the help of machine learning, the design and increase in efficiency of carbon dioxide removal units were studied, and an artificial intelligence model was created.
Makari expanded on the implementation of this research, which involved developing artificial intelligence software for the design, performance evaluation, and optimization of carbon dioxide removal units with amines. He pointed out that the efficiency of the developed tool was tested, and the results demonstrated its potential use in the industry without the need for significant time or cost for designing new units or optimizing the performance of existing units.
He also mentioned the process of implementing this research project, stating that with the help of artificial intelligence knowledge and a substantial volume of computational, industrial, and laboratory data, the development of an artificial intelligence model for carbon dioxide absorption with amine solutions was executed using various machine learning algorithms, and the Simorgh supercomputer was utilized for its implementation.
This Amirkabir University of Technology graduate highlighted the primary areas of application of this achievement, including gas, oil, petrochemical, power plant, and metal industries, and added that the use of artificial intelligence in chemical engineering is a completely new and noteworthy field for both academia and industry. The feasibility of using artificial intelligence-based methods in process industries is an innovative aspect of these studies.
He considered the development of an intelligent model for the process of removing carbon dioxide from flue gas using amine solutions with machine learning algorithms as one of the advantages of this research project. He stated: "Optimizing the process of carbon dioxide removal from flue gas using amine solutions with various optimization algorithms and achieving minimum and maximum energy consumption and carbon dioxide removal in the unit, and providing a comprehensive and extensive model under different operational conditions as well as a wide range of input and output process variables are among the other achievements of this project.
This project, titled "Simulation and Optimization of Carbon Dioxide Absorption Unit from Flue Gas using Amine Solutions with Machine Learning Algorithms," was carried out under the guidance of Dr. Mohammad Rahmani, a faculty member of the Chemical Engineering Department at Amirkabir University of Technology.
Researchers at the Faculty of Chemical Engineering of Amirkabir University of Technology, with the help of the Simorgh supercomputer, have presented an artificial intelligence-based model for absorbing carbon dioxide from gas in the oil, gas, petrochemical, and power plant industries.
According to the public relations office of Amirkabir University of Technology, Mohsen Makari, one of the researchers of this project, stated: "Utilizing artificial intelligence methods, especially machine learning, helps us reduce time, cost, and safety and environmental risks in the design, construction, and operation of chemical industries.
He continued: "Therefore, in this research, with the help of machine learning, the design and increase in efficiency of carbon dioxide removal units were studied, and an artificial intelligence model was created.
Makari expanded on the implementation of this research, which involved developing artificial intelligence software for the design, performance evaluation, and optimization of carbon dioxide removal units with amines. He pointed out that the efficiency of the developed tool was tested, and the results demonstrated its potential use in the industry without the need for significant time or cost for designing new units or optimizing the performance of existing units.
He also mentioned the process of implementing this research project, stating that with the help of artificial intelligence knowledge and a substantial volume of computational, industrial, and laboratory data, the development of an artificial intelligence model for carbon dioxide absorption with amine solutions was executed using various machine learning algorithms, and the Simorgh supercomputer was utilized for its implementation.
This Amirkabir University of Technology graduate highlighted the primary areas of application of this achievement, including gas, oil, petrochemical, power plant, and metal industries, and added that the use of artificial intelligence in chemical engineering is a completely new and noteworthy field for both academia and industry. The feasibility of using artificial intelligence-based methods in process industries is an innovative aspect of these studies.
He considered the development of an intelligent model for the process of removing carbon dioxide from flue gas using amine solutions with machine learning algorithms as one of the advantages of this research project. He stated: "Optimizing the process of carbon dioxide removal from flue gas using amine solutions with various optimization algorithms and achieving minimum and maximum energy consumption and carbon dioxide removal in the unit, and providing a comprehensive and extensive model under different operational conditions as well as a wide range of input and output process variables are among the other achievements of this project.
This project, titled "Simulation and Optimization of Carbon Dioxide Absorption Unit from Flue Gas using Amine Solutions with Machine Learning Algorithms," was carried out under the guidance of Dr. Mohammad Rahmani, a faculty member of the Chemical Engineering Department at Amirkabir University of Technology.