Description
Gas cyclone separators are essential components in circulating fluidized bed (CFB) boilers, where their performance and reliability directly impact operational efficiency and maintenance costs. Computational Fluid Dynamics (CFD) simulations using Eulerian-Lagrangian modeling are employed to quantify the effects of inlet velocity, gas temperature, and particle mass flow rate on cyclone performance. This study develops a hybrid CFD-Artificial Neural Network (ANN)-Genetic Algorithm (GA) framework to optimize cyclone performance, focusing on separation efficiency (SE) and pressure drop (ΔP), while translating results into actionable asset-level metrics. High-fidelity CFD simulations provided datasets to train ANN surrogates, which accurately predicted SE and ΔP across a wide range of operating conditions, effectively capturing the complex nonlinear relationships between input parameters and output responses, with maximum prediction errors of 3.2% for SE and 3.1% for ΔP. The GA identifies Pareto-optimal configurations that maximize SE while minimizing ΔP, and these optimized solutions are further interpreted in terms of predicted maintenance interval and lifecycle cost reduction. Results show that optimized operating conditions can extend maintenance intervals and achieve significant cost savings without compromising separation efficiency. The proposed framework integrates technical performance with asset management, enabling predictive maintenance planning, proactive decision-making, and intelligent lifecycle optimization of gas cyclones in CFB boiler systems. This approach establishes a performance-reliability-economics paradigm, offering practical strategies for sustainable and cost-effective boiler operation.| Period | 10 Dec 2025 |
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| Event title | 6th International Conference on Maintenance and Intelligent Asset Management |
| Event type | Conference |
| Location | Victoria, AustraliaShow on map |
| Degree of Recognition | International |