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March 5, 20260 citations

Integrated Reliability Enhancement of Methanol Pumps through Material Optimization, Cavitation Analysis, and Predictive Maintenance

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KMKritika ModiPJPushpdant Jain

Key Points

  • The research aims to enhance the reliability and performance of methanol pumps by addressing key failure mechanisms.
  • Conducted material optimization using duplex stainless steel
  • Performed cavitation analysis and impeller redesign validated through CFD simulations
  • Developed a predictive maintenance model using DCS data
  • Executed DFMEA analysis for failure mode assessment
  • Material upgrade showed a 35–40% reduction in corrosion-related degradation
  • Impeller redesign improved head stability by 3–5% and cavitation intensity decreased by approximately 18%
  • Projected RPN values for the impeller and casing reduced by 30–40% after implementing recommendations
  • Predictive maintenance model achieved over 99% classification accuracy, reducing unplanned maintenance by 20–25%

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.

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Cite This Study

Modi et al. (2026) studied this question.

synapsesocial.com/papers/69a91dd2d6127c7a504c1043https://doi.org/10.1051/epjconf/202635502012/pdf
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