Estimating Economical Thermal Insulation Thickness Using Artificial Intelligence Approach

Baghban, Alireza; Kazemi, Mohammadreza

doi:10.22034/farayandno.2025.2045358.1976

Estimating Economical Thermal Insulation Thickness Using Artificial Intelligence Approach

Document Type : Review

Authors

alireza Baghban ¹

Mohammadreza Kazemi ²

¹ Production Management, Gas & Natural Gas Liquid Engineering Department, National Iranian South Oilfields Company (NISOC), Ahvaz, Iran

² Process Engineering Department, Bandar Imam Petrochemical Company (BIPC), Mahshahr, Iran

10.22034/farayandno.2025.2045358.1976

Abstract

Due to the complexities in industrial processes and the variety of environmental conditions, it is necessary to use advanced methods to determine the insulation thickness. This study examines the use of artificial neural networks to predict the thickness of thermal insulation. In this regard, first, the thermal insulation data available in Iranian Petroleum Standard (IPS) with the code IPS-E-TP-700, is collected, and then the thickness of the insulation is predicted as a function of the outer diameter, thermal conductivity of the insulation and surface temperature. The statistical results show the high accuracy of the method used in predicting the thickness of the insulation, and the value of the regression coefficient and the relative error percentage for the tested data are 1.000 and 0.19, respectively. This approach not only helps to optimize insulation processes, but can also lead to cost reduction and increased safety in industrial operations.

Keywords

Artificial neural network

Insulation

Machine learning

Oil and gas industry

Modeling

Subjects

modeling & simulation

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