Investigation of boiler energy consumption in the gas refinery units using RSM ANN and Aspen HYSYS DOI Creative Commons

Erfan Gholamzadeh,

Ahad Ghaemi,

Abolfazl Shokri

и другие.

Heliyon, Год журнала: 2024, Номер 11(1), С. e41450 - e41450

Опубликована: Дек. 25, 2024

In order to lower total energy consumption, this study focuses on optimizing use in refinery boilers. Using Aspen HYSYS simulations and modeling approaches like Artificial Neural Networks (ANNs) Response Surface Methodology (RSM), data from 579 days of boiler operation was gathered examined. Radial Basis Function (RBF) Multi-Layer Perceptron (MLP) techniques were used the ANN modeling. Under same operating circumstances, estimated an usage 1,355 m³, whereas actual consumption 986 m³. While R2 values for models 0.98 RBF model 0.99 MLP model, value derived using RSM 0.97. Furthermore, model's performance metrics 0.0034, 0.0018. The is best choice, according these findings. It that burning 26,000 m³ fuel with air supply 23 m³/h at 25.5 °C will result a steam flow 525.5 tons per day 10.5 barg 256.5 °C. According statistics, circumstances might prevent release 27 carbon dioxide by reducing over 10,000 hour. By combustion stack's supply, decrease accomplished.

Язык: Английский

Optimization of CO2 absorption into MDEA-PZ-sulfolane hybrid solution using machine learning algorithms and RSM DOI

Abolfazl Shokri,

Sepehr Aarabi Dahej,

Ahad Ghaemi

и другие.

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Язык: Английский

Процитировано

0
Exploring the Potential of Koh-Modified Mxene (Ti3c2tx) as a Novel Promising Adsorbent for Enhanced Co2 Capture DOI

karim mansouri,

Ahad Ghaemi

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Investigation of boiler energy consumption in the gas refinery units using RSM ANN and Aspen HYSYS DOI Creative Commons

Erfan Gholamzadeh,

Ahad Ghaemi,

Abolfazl Shokri

и другие.

Heliyon, Год журнала: 2024, Номер 11(1), С. e41450 - e41450

Опубликована: Дек. 25, 2024

In order to lower total energy consumption, this study focuses on optimizing use in refinery boilers. Using Aspen HYSYS simulations and modeling approaches like Artificial Neural Networks (ANNs) Response Surface Methodology (RSM), data from 579 days of boiler operation was gathered examined. Radial Basis Function (RBF) Multi-Layer Perceptron (MLP) techniques were used the ANN modeling. Under same operating circumstances, estimated an usage 1,355 m³, whereas actual consumption 986 m³. While R2 values for models 0.98 RBF model 0.99 MLP model, value derived using RSM 0.97. Furthermore, model's performance metrics 0.0034, 0.0018. The is best choice, according these findings. It that burning 26,000 m³ fuel with air supply 23 m³/h at 25.5 °C will result a steam flow 525.5 tons per day 10.5 barg 256.5 °C. According statistics, circumstances might prevent release 27 carbon dioxide by reducing over 10,000 hour. By combustion stack's supply, decrease accomplished.

Язык: Английский

Процитировано

1