Optimising energy usage of a semi-continuous fluidised bed dryer using digital twin technology and energy management strategies

Pharmaceutical manufacturers are under increasing pressure to reduce energy consumption and enhance sustainability to meet net-zero emission targets. Achieving these goals requires advanced methodologies capable of capturing complex process dynamics and driving realtime optimisation. Industry 4.0 technologies – particularly Digital Twins (DTs) – offer significant potential, yet their effectiveness can be limited by undetected process anomalies and subtle energy-performance deviations. This study presents a novel DT integrated framework that combines energy management techniques, statistical monitoring, and a newly defined Energy Performance Indicator (EnPI) to optimise a semicontinuous fluidised bed dryer (FBD) within the GEA Consigma25 line at the Diamond Pilot Plant, University of Sheffield. Realtime experimental data and DT outputs were analysed using CUSUM (Cumulative Sum) deviation analysis, enabling sensitive detection of energy-moisture performance shifts that the DT alone could not identify. Results highlight 60°C as the optimal drying air temperature, delivering superior energy efficiency across liquid-to-solid ratios of 0.18 and 0.30, with opportunities for further refinement within the 50–60°C range. By bridging gaps in realtime multi‑objective monitoring, this integrated approach provides actionable insights for energy-efficient, quality-driven process control and establishes a scalable pathway towards sustainable pharmaceutical manufacturing.