Abstract
Maintenance is critical for industrial plants to ensure operational reliability and worker safety. In process industries, fouling, the accumulation of solid residues in equipment, poses a significant challenge, causing inefficiencies and productivity losses. Effective modeling of fouling evolution over time is essential for maintenance planning to prevent equipment from operating under suboptimal conditions. Traditional approaches to fouling prediction include equation-based models, which offer high precision but may struggle with continuously changing process boundaries, and machine learning techniques, which are more adaptable but less effective at capturing rapidly evolving trends driven by complex underlying physics. This study introduces an innovative hybrid machine learning approach for predictive maintenance, combining the strengths of both methods. Pressure differential is modeled using an equation-based approach that links pressure data with fouling thickness, while the fouling growth rate is estimated through Gaussian Process Regression (GPR). This hybrid model generates pressure drop profiles with vertical asymptotes that closely mimic real-world behavior. Applied to a vacuum distillation column processing fouling-prone used oil, the proposed predictive method achieves a significant reduction of over 60% in suboptimal operating time compared to the current maintenance strategy, based on a fixed threshold.