SINTEF, DENO, NXTECH, ILABS

The production process optimisation demonstrator installs IIoT-based sensors for visual analysis, temperature, humidity, and moisture throughout the preparation phase and correlates real-time data in those parameters using AI-based models. It is essential to have optimum moisture and temperature in the soybean processing after preparation and before extraction. If the moisture is too high and the temperature is too low, it will negatively affect the extraction's effectiveness. The moisture variation exists in the raw material, but there is no continuous measurement, and it is challenging to optimise the moisture content. It is essential to continuously correlate the moisture measurements with size, colour, fractions, and other parameters in the raw material. This could potentially optimise the conditioning and drying phase in the preparation plant and give real-time data to optimise the yields of the products.

Beyond state-of-the-art developments and impacts

The process optimisation solution offers key characteristics such as adaptive control, more careful handling, and reusability of stored knowledge in the soybean production process. The AI-based system integrates with the edge platform and improves the existing SCADA data acquisition system design. It integrates IIoT monitoring, wireless connectivity, and AI algorithms integrated into an edge processing platform to provide a means to develop AI models and algorithms and deploy them in the real-time process flow.