Energy-efficient start-up optimization via digital twin for a vegetable broth sterilization process

The food industry is undergoing a digital transformation driven by the need for greater sustainability, efficiency, and data integration. This study presents a methodology for implementing a digital twin in an industrial food manufacturing process, using the vegetable broth production line as a case study. The workflow integrates process analysis, sensor data collection, and data reconciliation to improve the reliability of process variables and enable accurate simulation. The reconciled data were used to develop a dynamic model in commercial software, capable of simulating different operating conditions. Two start-up strategies, cold start-up and pre-heating, were compared, revealing that pre-heating reduces steam consumption by 62% and start-up time by 63%. These results demonstrate the potential of digital twins in optimizing operational efficiency and energy use in the food industry. Future developments may include real-time data acquisition, integration with control systems, and the use of AI for predictive maintenance and process optimization. • Presented a methodology for digital twin development for the food industry. • Digital twin applied to the vegetable broth sterilization process. • Data reconciliation improves input accuracy for simulation. • Energy-efficient start-up achieved via digital twin simulation.