Estimating Lifecycle Planning in Water Treatment Facilities from Semantic Asset Registers

Abstract

Modern water treatment facilities are complex, asset-intensive cyber-physical systems where structural, mechanical, and electrical assets operate under highly variable conditions. Accurate asset lifecycle planning is essential to prevent catastrophic failures, optimize operational expenditure, and guide capital investment strategies. However, asset information is often fragmented across heterogeneous legacy systems, such as computerized maintenance management systems, supervisory control and data acquisition systems, and building information models. This paper presents a novel framework that leverages semantic asset registers to unify these heterogeneous data sources into a standardized, machine-readable knowledge graph using the Web Ontology Language. By utilizing ontological relations and semantic inference, we enrich asset data with structural, functional, and spatial context. We formulate lifecycle planning as a multi-class classification and regression task, forecasting remaining useful life and scheduling proactive maintenance interventions. To evaluate this approach, we implement a comprehensive benchmarking framework comparing traditional tabular machine learning models with semantic-enriched predictive models on a real-world dataset from municipal water reclamation plants. The empirical results demonstrate that integrating semantic features yields significant improvements in predictive accuracy, with semantic-enriched models outperforming baseline algorithms in both classification and regression tasks. This research demonstrates how semantic web technologies can be systematically leveraged to transition water treatment asset management from reactive maintenance schedules to highly precise, data-driven lifecycle planning.