Integrating AI-Driven Analytics, Cyber security, and Heat Transfer Optimization: A Multidisciplinary Approach to Modern Healthcare, Risk Management, and Industrial Efficiency

Abstract

Through advanced technological combinations of dimensionless analysis with artificial intelligence as well as predictive analytics and machine learning organizations can optimize heat transfer processes in industry in addition to control operational risks and achieve enhanced efficiency operations. The development of energy and industrial thermal efficiency relies on dimensionless analysis because it uses essential dimensionless numbers to transform difficult heat transfer operations into simplified forms. AI systems when combined with predictive analytics tools create perfect heat transfer processes through their ability to observe data in real-time while doing automated predictive maintenance and adjusting system controls which deliver superior performance for sustainable industrial operations. Evidence of technological effectiveness exists in the sector where power plants combine with chemical plants and renewable energy facilities to show superior system performance and energy processing efficiency. Data quality issues and integration barriers create problems for the technology along with challenges against developing better model explanation functionalities. The resolution of research barriers for highly efficient industrial systems depends on both improved data acquisition technology and explainable AI models and reinforcement learning advancements. The research evaluates how thermal energy systems join big data platforms through technological alignment to optimize industrial heat transfer operations thus improving energy sustainability targets.