Integrating Design Optimization and AI-Driven Maintenance in Centrifugal Pumps for Sustainable Industrial Operations

Centrifugal pumps are crucial in a variety of industrial sectors, but their performance is often affected by energy inefficiencies, wear-related failures, and suboptimal hydraulic characteristics. This research presents a comprehensive experimental study aimed at optimizing the hydraulic performance, energy consumption, and reliability of centrifugal pumps through design innovations, advanced manufacturing techniques, and predictive maintenance. Computational Fluid Dynamics (CFD) simulations, additive manufacturing, and Long Short-Term Memory (LSTM)-based predictive algorithms were employed. The findings revealed up to a 7.3% increase in hydraulic efficiency, a 75% reduction in failure incidents through predictive maintenance, and 8.2% savings in energy usage. The proposed framework offers a scalable, data-driven approach to improve centrifugal pump operations in modern industrial applications.