| Issue |
EPJ Web Conf.
Volume 355, 2026
4th International Conference on Sustainable Technologies and Advances in Automation, Aerospace and Robotics (STAAAR 2025)
|
|
|---|---|---|
| Article Number | 02012 | |
| Number of page(s) | 12 | |
| Section | Additive Manufacturing and Sustainable Materials | |
| DOI | https://doi.org/10.1051/epjconf/202635502012 | |
| Published online | 03 March 2026 | |
https://doi.org/10.1051/epjconf/202635502012
Integrated Reliability Enhancement of Methanol Pumps through Material Optimization, Cavitation Analysis, and Predictive Maintenance
School of Mechanical Engineering, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Dist. Sehore, Madhya Pradesh- 466114, India
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 3 March 2026
Abstract
The reliability and performance of methanol pumps are critical in industrial applications, particularly in oil and petrochemical industries where these pumps play a pivotal role in handling methanol for various processes. In such demanding environments, the reliability of multistage centrifugal methanol pumps is significantly affected by internal corrosion, cavitation, and seal failures, especially when methanol contains traces of moisture. This study presents an integrated approach to improve pump reliability through material optimization, cavitation analysis, impeller redesign, and predictive maintenance. Moisture-induced corrosion was identified as a dominant failure mechanism, increasing wear rates and reducing hydraulic efficiency. Material upgradation from ASTM A216 Gr. WCB to duplex stainless steel demonstrated an estimated 35–40% reduction in corrosion-related degradation, based on comparative wear analysis. A redesigned impeller, validated through CFD simulations in ANSYS, showed a 3–5% improvement in head stability and reduced cavitation intensity, with vapour volume fraction decreasing by approximately 18% in critical blade- tip regions. DFMEA analysis indicated high initial RPN values for the impeller (224) and casing (252); following the recommended actions, the projected RPN values reduced by 30–40%. The predictive seal-failure model developed using DCS data achieved over 99% classification accuracy, enabling early anomaly detection and contributing to a 20–25% reduction in unplanned maintenance. The combined improvements enhance pump availability, extend component life, and support safer and more energy- efficient methanol pumping operations.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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