Sujata Jadhav
Department of Applied Sciences, Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

Amit Kumar
Department of Applied Sciences, Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

DOI https://doi.org/10.33889/IJMEMS.2026.11.3.060

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Abstract

Reliable and efficient functioning of thermal power plants is necessary for a steady power supply and economic viability. In the present research, the reliability and availability of a subsystem of a thermal power plant are modelled through a Continuous-Time Markov Process (CTMP), which reflects the stochastic change from working to failed states. Human error is incorporated as an additional failure factor to reflect practical operating conditions. To further improve performance, a Particle Swarm Optimization (PSO) algorithm, a nature-inspired metaheuristic approach, is used to maximize system availability. In addition to enhancing availability, the research uses PSO to maximize the expected profit of the system using a unified economic objective function. The Markov-based model yields an initial system availability of 0.9117. After applying Particle Swarm Optimization, the availability improves to 0.9199 with variation in population size and further increases to 0.9240 with variation in the number of iterations, representing an overall enhancement of approximately 1.23%. The optimized results also increase the expected profit. The outcomes demonstrate that the integration of Markov modelling with PSO ensures accurate reliability and availability analysis and provides a robust framework for economic optimization.

Keywords- Continuous-Time Markov Chain (CTMC), Availability modelling, Expected profit, Particle Swarm Optimization (PSO), Thermal power plant, Coal transportation system.

Citation

Jadhav, S., & Kumar, A. (2026). Enhancing Availability and Profitability of Thermal Power Plants using Markov Modelling and Particle Swarm Optimization. International Journal of Mathematical, Engineering and Management Sciences, 11(3), 1465-1486. https://doi.org/10.33889/IJMEMS.2026.11.3.060.