Work

Economic Analysis of Condition-Monitoring-Based Predictive Maintenance in Power Plants under Market Elasticity

Year: 2026

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide Condition-monitoring-based predictive maintenance can increase the power plant availability by preventing forced outages. However, the actual on-stream time depends on market elasticity and dynamics, which are affected by cost and penetration of other power generation technologies. This paper develops a systematic approach for the economic analysis of investment in condition monitoring technologies with due consideration of market elasticity. Focus is on corrosion monitoring in coal-fired power plants (CFFPs) since corrosion in high-temperature coal-fired boilers is a leading cause of equipment failure. Investment in sensor networks for measuring corrosion and operating conditions like metal temperature and concentrations of O 2 and SO 2 is investigated. The unscented Kalman filter is used to estimate corrosion in the waterwall section of the boiler under multiple sensor networks. Electricity produced by CFPPs in the future in the U.S. due to changes in availability under market elasticity is studied. Sensitivity of the incremental net present value to factors like the number, type, and cost of sensors is analyzed.

Source: Industrial & Engineering Chemistry Research

DOI: 10.1021/acs.iecr.5c04101

Authors: Chandra Sekhar Somayajula, Debangsu Bhattacharyya

Institutions: West Virginia University, Oak Ridge National Laboratory

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,500