Energy optimizing evaporating crystallizers through machine learning

Abstract

This study presents a machine learning-based approach to optimize energy use in batch-operated evaporating crystallizers within the sugar industry. Leveraging causal machine learning with LightGBM, the study introduces a method to balance high- and low-energy steam usage by dynamically adjusting setpoints in the crystallization process, thus minimizing energy consumption while maintaining optimal batch cycle times. The solution, implemented in collaboration with Nordic Sugar A/S Nakskov, demonstrated a 39.8% increase in lower-energy steam utilization, leading to significant energy savings, including an approximate reduction of 3000 L of diesel consumption per crystallizer per campaign. These findings support a pathway toward sustainable energy practices in industrial crystallization.