Work

Reshaping Industrial Maintenance with Machine Learning: Fouling Control Using Optimized Gaussian Process Regression

Year: 2025

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide Reliable maintenance scheduling is essential for complex industrial equipment, yet traditional condition-based strategies with static warning thresholds often fail in fouling-prone processes or when feedstock composition fluctuates. This paper presents a predictive maintenance strategy based on the automatic selection of optimal kernel combinations in Gaussian Process Regression (GPR) through a recursive algorithm. The approach is applied to a vacuum distillation column processing used oil, a fouling-prone waste stream with variable composition. The algorithm performs an automated search and optimization of models through recursive combination of kernels and operators, following a greedy search strategy. The algorithm’s predictive capabilities are validated on five distinct data sets representing the evolution of the column’s pressure differential, a key indicator of fouling. Results show significant improvements, with the strategy reducing suboptimal operating time by 30–40% and, in some cases, entirely avoiding such conditions. Automation of kernel search and optimization ensures general validity for the proposed method.

Source: Industrial & Engineering Chemistry Research

DOI: 10.1021/acs.iecr.4c04550

Authors: Francesco Negri, Andrea Galeazzi, Francesco Gallo, Flavio Manenti

Institutions: Politecnico di Milano, Imperial College London

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,500