Electric Drive Supervisor for Milling Process 4.0 Automation: A Process Analytical Approach with IIoT NIR Devices for Common Wheat

Measuring risk of renewable diesel production processes using a multi-criteria decision strategy

This study proposes measuring the risk of five alternative renewable diesel production technologies using a multi-criteria decision matrix strategy. Evaluated criteria include environmental, economic, technological, social, and process safety risks. The subjective Analytical Hierarchy Process (AHP) with stakeholder input provides criteria and sub-criteria weightings and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranks alternatives. Alternative renewable diesel options are Green Diesel from first, second, and third-generation feedstocks, Fischer-Tropsch Diesel from second-generation biomass, and the transesterification of vegetable oils (VO) to make biodiesel. This study is a response to an earlier work measuring the sustainability of the same renewable technologies. While the previous work indicated Fischer-Tropsch Diesel as the most sustainable, this current work indicated the process as the "most risky," suggesting that risk is a significant driver of decision making over sustainability, and newly developed decision tools should address both perspectives.

Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation

The flour milling industry-a vital component of global food production-is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters.