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

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 11(1), P. e41450 - e41450

Published: Dec. 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.

Language: 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

et al.

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Language: Английский

Citations

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

karim mansouri,

Ahad Ghaemi

Published: Jan. 1, 2025

Language: Английский

Citations

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

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 11(1), P. e41450 - e41450

Published: Dec. 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.

Language: Английский

Citations

1