💡 SLIIT Research Advances Industrial Energy Management with AI

Researchers at SLIIT have developed an AI-powered framework to enhance power forecasting accuracy for biomass-fuelled steam turbines, aiming to improve industrial efficiency and reduce energy waste. • Core Innovation & Impact: • Developed an AI forecasting model based on an 8-year industrial dataset, achieving ~25% higher predictive accuracy over benchmark methods. • Focuses on palm oil manufacturing using agricultural by-products (fibres, shells, husks) to generate renewable power. • Prevents biomass waste and cuts reliance on the national grid, reducing overall operating costs. • Economic & National Context: • Strengthens local research capacity in applied energy innovation and sustainable resource management. • Directly supports Sri Lanka's broader industrial efficiency goals and the alignment of manufacturing productivity with clean energy transition. • Demonstrates practical cross-border collaboration across engineering and ICT/BPM domains, involving SLIIT, La Trobe University (Australia), and Kyungpook National University (South Korea).