Optimizing drying processes for agricultural products utilising non-invasive measurement and adaptive control systems

With increasing population and global warming, the need for functional yet nutritional food combined with energy efficient processes has risen significantly. The new “Industry 4.0” revolution acknowledging this need aims to develop and improve processes that are affordable as well as sustainable. For a process like drying this means a shift towards smart or intelligent drying systems using a multidisciplinary approach. This encompasses the use of advanced computational algorithms and control systems integrated with non-invasive measurement (NiM) methods to increase process and energy efficiency while ensuring high product quality. Considering this background, the current thesis aims on understanding the effect of different strategies and varying process parameters on agricultural product quality using NiM techniques and integrated measurement and control systems. To fulfill the aim, five studies with multiple objectives were conducted. Each study builds on the results and outcomes from the previous one, thus exhibiting a progression towards the improvement of process conditions and product quality during the drying process. The first study focused on assessing the influence of pre-drying storage time on hop quality. The results provide thorough information on the quality changes occurring both during storage and drying. The second study investigated the effect of varying bulk weights and hop size distributions on hops drying behaviour and their overall quality. Results indicate dried hop quality depends on multiple factors and thus, it is essential to define both bulk and process parameters as well as integrate NiM systems. The third study is a proof of concept for integration of NiM systems into small-scale commercial hop driers. The novel and innovative use of integrated visual sensors gives a deeper understanding of the different processes taking place during hops drying. To discern the effect of different drying strategies on the dynamic drying behaviour and quality of organic carrots, the fourth study integrated both NiM techniques and control system into a developed precision scale laboratory dryer. Different drying strategies showed to have significant influence on both the product quality as well as the overall process efficiency. The integrated systems were inferred to be sophisticated techniques for continuous monitoring and control of product quality. The final study assessed the effect of the different process strategies on the development of prediction models for five quality parameters. The results indicate potential development of one model across all process strategies and all investigated quality parameters. It also attests for feasible replacement of conventional laboratory methods with NiM. In summary, the current thesis presents extensive information on the dynamic changes occurring within the quality of agricultural produce during the drying process. It also emphasis the interdependency of process settings and product quality for optimisation process. The use of NiM techniques has been helpful in accurately presenting the changes in quality parameters while also showcasing the versatility of these techniques. The outcomes from this thesis will act as a foundational base for further development of intelligent dryer that will encompass real time monitoring and analysis of product quality.