| _version_ | 1866901192384184320 |
|---|---|
| author | Muhammad Arsalan, Muhammad Ayaz, Yousaf Ali, Uroosa Baig |
| author_facet | Muhammad Arsalan, Muhammad Ayaz, Yousaf Ali, Uroosa Baig |
| contents | <div> <div>Industrial power plants require highly efficient and adaptive control systems to manage complex, nonlinear, and time-sensitive processes. Traditional control algorithms, while effective in stable settings, struggle under dynamic load variations and unexpected operational conditions. Reinforcement Learning (RL), a subset of machine learning, has emerged as a powerful tool to enhance the intelligence of automated control systems through experience-based learning and real-time optimization. This paper investigates the application of RL in improving control precision, energy efficiency, fault resilience, and adaptive decision-making in industrial power plants. We present a modular RL-based control architecture, benchmark its performance against conventional PID and fuzzy logic controllers, and explore its implementation in scenarios such as boiler control, turbine optimization, and fault-tolerant systems. Experimental results demonstrate that RL controllers outperform baseline models in both stability and responsiveness. The study offers a framework for integrating RL into existing industrial automation systems while addressing deployment challenges and safety requirements.</div> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17926644 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Using Reinforcement Learning to Enhance Automated Control Systems in Industrial Power Plants Muhammad Arsalan, Muhammad Ayaz, Yousaf Ali, Uroosa Baig <div> <div>Industrial power plants require highly efficient and adaptive control systems to manage complex, nonlinear, and time-sensitive processes. Traditional control algorithms, while effective in stable settings, struggle under dynamic load variations and unexpected operational conditions. Reinforcement Learning (RL), a subset of machine learning, has emerged as a powerful tool to enhance the intelligence of automated control systems through experience-based learning and real-time optimization. This paper investigates the application of RL in improving control precision, energy efficiency, fault resilience, and adaptive decision-making in industrial power plants. We present a modular RL-based control architecture, benchmark its performance against conventional PID and fuzzy logic controllers, and explore its implementation in scenarios such as boiler control, turbine optimization, and fault-tolerant systems. Experimental results demonstrate that RL controllers outperform baseline models in both stability and responsiveness. The study offers a framework for integrating RL into existing industrial automation systems while addressing deployment challenges and safety requirements.</div> </div> |
| title | Using Reinforcement Learning to Enhance Automated Control Systems in Industrial Power Plants |
| url | https://doi.org/10.5281/zenodo.17926644 |