Strategic management of energy consumption and reduction of specific energy consumption using modern methods of artificial intelligence in an industrial plant

被引:3
|
作者
Sarvestani, Maryam Ebrahimzadeh [1 ]
Hoseiny, Saeed [2 ]
Tavana, Davood [2 ]
Di Maria, Francesco [1 ]
机构
[1] Univ Perugia, Dipartimento Ingn, LAR5 Lab, via G Duranti 93, I-06125 Perugia, Italy
[2] Middle East Kimiaye Pars Co MEKPCo, Asalouyeh, Bushehr, Iran
关键词
Energy; Gas production; Energy consumption index; Artificial intelligence; Sensitivity analysis; Management; SUPPORT VECTOR MACHINES; RANKINE-CYCLE ORC; PARAMETRIC OPTIMIZATION; FLUID SELECTION; PREDICTION; SVM; POWER; PERFORMANCE; SYSTEMS; ENGINE;
D O I
10.1016/j.energy.2023.129448
中图分类号
O414.1 [热力学];
学科分类号
摘要
Given the importance of energy and its role in sustainable gas production, paying special attention to its consumption indicators in all industries, especially oil & gas is very important. The energy consumption index parameter, which equals the ratio of energy consumption to gas production, is one of the most significant indicators of the gas refinery. Artificial intelligence can be used to evaluate the effects of different parameters and find the factors influencing energy performance. Important variables such as inlet gas from various sources, the energy consumption of each part, and the amount of waste refinery gas are collected on different days of the year, then using the least square support vector machine method and networking and sensitivity analysis of the impact of each variable separately. The role of each in this parameter is determined so that it can be used to provide appropriate solutions to reduce energy consumption and the energy consumption index parameter for sustainable gas production. According to the results of this study, the steam unit has a 14 % impact on the energy consumption index, and the gas sweetening unit has an 18 % impact on gas consumption. So, more research on the optimization of these two units is necessary.
引用
收藏
页数:7
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