In the realm of industrial operations, maintaining optimal boiler performance is a complex
challenge. Large variabilities in fuel and feed water quality significantly impact the efficiency and reliability of boiler systems, making it difficult to ensure consistent throughput. Estimating the critical contributing factors in real time has traditionally been a daunting task. However, the advent of machine learning (ML) has revolutionised this landscape, offering predictive models that can accurately forecast energy demand and enhance boiler operations.
By leveraging advanced techniques such as SHAP (Shapley Additive exPlanations) values, it's now possible to dynamically estimate and control the factors influencing boiler performance.
This innovative approach not only predicts energy demand but also provides actionable insights for better operational control. As a result, companies can achieve significant reductions in fuel consumption by approximately 6-8%, and improvements in productivity ranging from 12-15%.
These enhancements translate to substantial annual savings, estimated between $450,000 and $600,000.
