Manipal Academy of Higher Education (MAHE) signed a Memorandum of Understanding (MoU) with GHCL Limited to develop an advanced Laser-Induced Breakdown Spectroscopy (LIBS)-based system for real-time monitoring of the Gross Calorific Value (GCV) of carbon-based fuels. The collaboration brings together spectroscopy, artificial intelligence (AI), and machine learning (ML) to create a rapid, on-site fuel quality assessment solution. The initiative aims to move beyond conventional laboratory-based testing and provide industries with faster, data-driven insights for operational decision-making.
MAHE-GHCL Collaboration to Strengthen Fuel Quality Monitoring
GHCL Limited, a leading Indian chemical manufacturer, extensively uses carbon-based fuels for energy generation and process heating. Therefore, maintaining consistent fuel quality is critical for improving operational efficiency, controlling costs, and ensuring reliable plant performance. Under the new partnership, MAHE and GHCL, along with the Manipal Institute of Applied Physics (MIAP), will work towards developing an intelligent monitoring system that can assess fuel quality at the point of receipt. Sanjay Gupta, Vice President (Commercial), GHCL Limited, participated in the discussions with the MAHE team and exchanged the signed MoU with Dr P. Giridhar Kini, Registrar, MAHE.
LIBS Technology Enables Real-Time Fuel Analysis
The proposed system will use Laser-Induced Breakdown Spectroscopy (LIBS) to analyse carbon-based fuels rapidly. By combining LIBS with AI and ML, the project seeks to develop a system capable of delivering immediate information on fuel characteristics, particularly its Gross Calorific Value (GCV). Unlike conventional testing methods, which typically require laboratory analysis and can take considerable time, the proposed solution is designed to provide rapid, on-site fuel quality information. As a result, industries could identify variations in fuel quality earlier and take corrective action without waiting for lengthy laboratory reports.
Improving Efficiency and Fuel Utilisation
Fuel quality directly affects the performance and economics of energy-intensive industrial operations. In conventional fuel-quality verification, delays in laboratory testing can limit the ability of plant operators to respond quickly to changes in fuel characteristics. The MAHE-GHCL project aims to address this challenge by developing a real-time fuel quality monitoring system that provides actionable information at the point of receipt. The technology could help industries improve fuel quality assurance, optimise fuel utilisation, strengthen process efficiency, reduce uncertainty associated with conventional testing, support faster operational decision-making and improve cost competitiveness.
Industry-Academia Collaboration for Industrial Innovation
Mayuresh Hede, Operations Head, Sutrapada Plant, GHCL Limited, highlighted the company’s focus on advanced technologies for operational excellence and sustainability. He noted that integrating real-time analytics into fuel quality management could strengthen process efficiency while reducing uncertainties associated with conventional testing methods. Dr Sharath K. Rao, Vice Chancellor, MAHE, emphasised the importance of connecting academic research with industrial requirements. According to him, combining LIBS technology with AI and ML offers significant scientific and commercial potential while creating opportunities for translational research across energy-intensive industries.
Project Led by Manipal Institute of Applied Physics
The MAHE side of the project will be led by Dr Unnikrishnan V. K., Manipal Institute of Applied Physics, as the Principal Investigator. Prof. Karunakar Kotegar, Pro Vice Chancellor – Technology and Science, has assured full institutional support for the initiative. Dr Sajan Daniel George, Director, Manipal Institute of Applied Physics, and Dr Harishkumar, Director, Corporate Relations, MAHE, were also present during the MoU-related discussions.
Potential for Scalable Fuel Monitoring Technology
Beyond developing a functional monitoring system, the collaboration also aims to generate intellectual property (IP) and create scalable technology with commercial potential. If successfully implemented, the LIBS-based fuel quality monitoring system could provide a replicable technology model for industries that depend on carbon-based fuels. As reported by bignewsnetwork.com, the project could strengthen industry-academia collaboration and demonstrate how advanced spectroscopy, AI, and machine learning can address practical challenges in energy-intensive industrial operations. Ultimately, the MAHE-GHCL initiative represents a step towards faster, smarter, and data-driven fuel quality management, with potential applications across multiple industrial sectors.
