MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202611087039 A) filed by Adarsh Kumar Arya; Dr. K. Sankar; Dr. Abhimanyu Kisan Chandgude; Dr. Sandip Mane; Dr. M. Ismail Fathima; Dr. S. Saranya; Dr R Ramya; and Vasujadevi Midasala on July 16, 2026, for Ai-Driven Optimization Of Electrolytic Hydrogen Generation Using Ant Colony Algorithms.
Inventors include Adarsh Kumar Arya; Dr. K. Sankar; Dr. Abhimanyu Kisan Chandgude; Dr. Sandip Mane; Dr. M. Ismail Fathima; Dr. S. Saranya; Dr R Ramya; and Vasujadevi Midasala.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: The present invention relates to an AI-driven optimization framework for enhancing the efficiency, reliability, and sustainability of electrolytic hydrogen generation by integrating Ant Colony Optimization (ACO) algorithms with intelligent electrolysis control systems. Green hydrogen production through water electrolysis is a promising pathway for achieving carbon-neutral energy systems; however, conventional electrolyzers often experience reduced efficiency due to fluctuating renewable energy inputs, suboptimal operating conditions, electrode degradation, and increased energy consumption. The proposed invention introduces an adaptive optimization platform that continuously monitors critical electrolysis parameters, including voltage, current density, electrolyte concentration, temperature, pressure, water flow rate, electrode condition, and renewable power availability using a network of smart sensors. These real-time data are processed by an AI engine employing Ant Colony Optimization to identify the optimal combination of operating parameters that maximizes hydrogen production while minimizing electrical energy consumption, heat losses, and system degradation. The optimization model dynamically updates pheromone trails based on historical and real-time system performance, enabling rapid convergence toward globally efficient operating conditions under varying environmental and load scenarios. The platform further incorporates predictive maintenance, anomaly detection, digital twin simulation, cloud-based analytics, and edge computing to ensure continuous operational reliability and scalability for industrial hydrogen production facilities. The system supports integration with photovoltaic, wind, and hybrid renewable energy sources to improve energy utilization and reduce greenhouse gas emissions. Experimental validation demonstrates significant improvements in hydrogen yield, electrolyzer efficiency, operational stability, and equipment lifespan compared to conventional control methods. The invention offers an intelligent, autonomous, and economically viable solution for next-generation green hydrogen production, facilitating the widespread adoption of renewable energy technologies while contributing to global decarbonization, energy security, sustainable industrial development, and the advancement of hydrogen-based clean energy ecosystems.
Disclaimer: Curated by HT Syndication.
