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The performance of industrial boilers is vital in the current dynamic environment of sustainable energy systems because they are the key to realizing reliable and efficient performance. This paper examines the corrosion and metal degradation of Carbon Steel Grade 200 Group A boiler tube in conditions of Reverse Osmosis (RO) water. Putting a prime focus on the interdisciplinary field of energy informatics, the study combines experimental, corrosion evaluation along with sophisticated methods of computational or machine intelligence known as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). These algorithms are used to optimize operating parameters and forecast the corrosion rates, resulting in a data-driven framework of decision support in the context of preventive maintenance of energy systems. The results are analyzed using informatics-based modeling with electrochemical cell testing, SEM microscopy, and mechanical property testing to determine the ideal combination of temperature (100 ℃), pH (8), and corrosion is minimized at 1.1 per year. The study is beneficial to the field of energy informatics because it makes it possible to realize smart monitoring of corrosion, predictive maintenance, and life extension pathways of crucial infrastructure within power production systems.

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Integrating Energy Informatics and Optimization Algorithms for Corrosion Mitigation in Carbon Steel Boiler Tubes Under Reverse Osmosis (RO) Water Conditions

  • 会议论文

  • 发表日期：

    2026年

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  • Shahad Mohammed Mahdi,
  • Safwan Nadweh,
  • Abdul Samad Bin Shibghatullah,
  • Reyad Omran Essa,
  • Ahmed Dheyaa Radhi,
  • Azmi Shawkat Abdulbaqi,
  • Ali Imad Naji,
  • Dima Haider Rasheed,
  • Salwan S. Hatif

摘要

The performance of industrial boilers is vital in the current dynamic environment of sustainable energy systems because they are the key to realizing reliable and efficient performance. This paper examines the corrosion and metal degradation of Carbon Steel Grade 200 Group A boiler tube in conditions of Reverse Osmosis (RO) water. Putting a prime focus on the interdisciplinary field of energy informatics, the study combines experimental, corrosion evaluation along with sophisticated methods of computational or machine intelligence known as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). These algorithms are used to optimize operating parameters and forecast the corrosion rates, resulting in a data-driven framework of decision support in the context of preventive maintenance of energy systems. The results are analyzed using informatics-based modeling with electrochemical cell testing, SEM microscopy, and mechanical property testing to determine the ideal combination of temperature (100 ℃), pH (8), and corrosion is minimized at 1.1 per year. The study is beneficial to the field of energy informatics because it makes it possible to realize smart monitoring of corrosion, predictive maintenance, and life extension pathways of crucial infrastructure within power production systems.