Assessment of a real time sensing technology for process monitoring and decision making

The smart food industry refers to the use of technology to make the food industry more efficient, sustainable, and profitable. Real-time monitoring is a key component of the smart food industry while involving the use of sensors, and data analytics to track and analyze various aspects of the food supply chain, such as temperature, humidity, location, and quality. Real-time monitoring can also help to improve food safety and traceability. For example, if a food product is found to be contaminated, real-time monitoring can help identify the source of the contamination and enable producers to take swift action to prevent further issues. This information can be used to optimize the supply chain, reduce waste, and ensure that food products are delivered to consumers in the best possible condition.
A crucial process in food industries is the adsorption. To model this process, traditional breakthrough models are currently used, such as the Bohart-Adams, Thomas, and Yoon-Nelson models. However, such models have limitations in describing asymmetric breakthrough curves and the change in adsorption rate during the process, requiring the estimation of several parameters via experimental data and long time to be developed. Therefore, implementing a simplified mathematical and empirical model (double exponential or logistic function), have the potential to provide a convenient description of the breakthrough curves (adsorbate concentration at the outlet of the column as a function of time) for different adsorbent-adsorbate systems.
For this purpose, this EngD project investigated how implementing a novel technology from Helia Biomonitoring for the real-time sensing of biomolecular substances, could help improving the efficiency and performance of a food industry process via decision making and process control. To validate this approach, two case studies with an adsorption process in common, are evaluated for two industrial partners: Dairy Company (DC, Company 1) and Potato Company (PC, Company 2).
The results of the project activities for both case studies, showed promising application of the Helia Biomonitoring technology towards addressing different issues regarding the adsorption process. By utilizing a simplified model, we have shown how a biosensor placed at the inlet of a column can be used to steer decisions when to stop the adsorption process of the lactoferrin (DC). On the other hand, it can also be used to design a feedback-feedforward control strategy to achieve a constant concentration at the outlet of a series of adsorption columns, which was explored for an extraction process of an anti-nutritional factor (PC). Recommendations for how the technology can be implemented in decision making and process monitoring, are provided in this report for each case.