Finite Element Analysis and Machine Learning-Based Prediction of Oil Tank Behavior Under Diverse Operating Conditions

Abstract

Ensuring the structural integrity and operational reliability of oil storage tanks is critical to preventing catastrophic failures, including environmental pollution and economic losses. This study integrates Finite Element Analysis (FEA) and Machine Learning (ML) to predict the behavior (structural) and useful life of oil tanks under diverse operating conditions. The methodology involves applying FEA simulation (using Abaqus) to model the strain, stress, and buckling behavior of the oil storage tank. Thereafter, fe-safe postprocessing software was used to post-process the FEA results to estimate the useful life of the tank. These FEA and fe-safe outputs were trained using Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to predict unknown tank operating scenarios. The study revealed that the filled tanks experienced higher stress (485.4 MPa) and reduced life expectancy (1429 h) compared to half-filled tanks (388.7 MPa and 3551 h). For the ML, ANFIS excelled in predicting stress and strain with R² values of 0.999, while ANN proved superior for useful life predictions with R² values of 0.998. This hybrid FEA-ML approach enables efficient and precise analysis, thereby facilitating design optimization and maintenance strategies for industrial applications.