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Contamination of cereal grain, especially wheat, with fungal infections can cause significant economic impacts and it endangers the health of humans and livestock. This study aims to appraise the UV/VIS–NIR and digital color (RGB) imaging systems and spectroscopic methodology to detect wheat kernels infected by fungi such as Penicillium expansum and Fusarium graminearum. NIR spectra of 190–1100 nm at 10 nm intervals, visible color reflectance images and non‐visible reflectance images of wheat kernels in the ultraviolet and near‐infrared ranges were applied to develop the multi‐layer perceptron (MLP) artificial neural network model. The optimum wavelengths were selected by application of the principal component analysis (PCA) after preprocessing the raw spectra. A confusion matrix was used in the correlation feature selection method (CFS) for the decision tree classifier of selected features. The results showed that the four UV wavelengths of 310, 330, 400, and 410 nm were the best wavelengths using PCA to distinguish healthy and unhealthy wheat kernels. Considering the intensity of the wavelengths as the neural network inputs, samples were classified into healthy and unhealthy categories with an accuracy of 90.9%. Also, 18 features of color images in RGB, LAB, HSV, HSI, YCbCr, and YIQ spaces provided the highest average accuracy of 44.4% in classifying healthy and infected wheat kernels by using a CCD Proline camera in the ultraviolet range. In contrast, other cameras in the visible and invisible range showed low accuracy. Furthermore, the best classification accuracy of the healthy and infected samples by the use of the CFS method was obtained at 88.1%. Based on the findings, spectroscopic methodology proved to be highly effective for detecting, classifying and automatic cleaning of various agricultural seeds, with a particular emphasis on wheat kernals. [ABSTRACT FROM AUTHOR]

Estimating energy flows that affect temperature increases inside houses is crucial for optimizing building design and enhancing the comfort of living spaces. In this study, a thermal model has been developed to estimate the internal temperature of rural houses in Mexico using aerial thermography. The methodology used in this study considered three stages: (a) generating a semi-experimental thermal model of heat transfer through roofs for houses with high infiltration, (b) validating the model using contact thermometers in rural community houses, and (c) integrating the developed model using aerial thermography and Python 3.11.4 into user-friendly software. The results demonstrate that the thermal model is effective, as it was tested on two rural house configurations and achieved an error margin of less than 10% when predicting both maximum and minimum temperatures compared to actual measurements. The model consistently estimates the internal house temperatures using aerial thermography by measuring the roof temperatures. Experimental comparisons of internal temperatures in houses with concrete and asbestos roofs and the model's projections showed deviations of less than 3 °C. The developed software for this purpose relies solely on the fundamental thermal properties of the roofing materials, along with the maximum roof temperature and ambient temperature, making it both efficient and user-friendly for rural community management systems. Additionally, the model identified areas with comfortable temperatures within different sections of a rural community, demonstrating its effectiveness when integrated with aerial thermography. These findings suggest the potential to estimate comfortable temperature ranges in both rural and urban dwellings, while also encouraging the development of public policies aimed at improving rural housing. [ABSTRACT FROM AUTHOR]

This study examines the potential for the smart integration of waste and renewable energy sources to supply industrial heat at temperatures between 150 °C and 250 °C, aiming to decarbonize heat demand in European industry. This work is part of a European project (SUSHEAT) which focuses on developing a novel technology that integrates several innovative components: a Stirling cycle high-temperature heat pump (HTHP), a bio-inspired phase change material (PCM) thermal energy storage (TES) system, and a control and integration twin (CIT) system based on smart decision-making algorithms. The objective is to develop highly efficient industrial heat upgrading systems for industrial applications using renewable energy sources and waste heat recovery. To achieve this, the specific heat requirements of different European industries were analyzed. The findings indicate that industrial sectors such as food and beverages, plastics, desalination, textiles, ceramics, pulp and paper, wood products, canned food, agricultural products, mining, and chemicals, typically require process heat at temperatures below 250 °C under conditions well within the range of the SUSHEAT system. Moreover, two case studies, namely the Pelagia and Mandrekas companies, were conducted to validate the effectiveness of the system. An analysis of the annual European heat demand by sector and temperature demonstrated that the theoretical potential heat demand that could be met by the SUSHEAT system is 134.92 TWh annually. Furthermore, an environmental impact assessment estimated an annual significant reduction of 19.40 million tonnes of CO2 emissions. These findings underscore the significant potential of the SUSHEAT system to contribute to the decarbonization of European industry by efficiently meeting heat demand and substantially reducing carbon emissions. [ABSTRACT FROM AUTHOR]

The studies on strategies for improving restoration times in electrical distribution systems are extensive. They have theoretically explored the application of mathematical models, the implementation of remotely controlled systems, and the use of digital simulators. This research aims to connect conceptual studies and the implementation of improvements and impact assessment in electrical distribution systems in developing countries, where distribution technologies vary widely, by employing a comprehensive methodology. The proposed research examines the restoration times for faults in substations within general distribution networks in the central–western region of Mexico. The study comprises these stages: (a) diagnosing the electrical supply, demand, and infrastructure; (b) analyzing the electrical restoration time and the restoration index of the substations; and (c) providing recommendations and implementing pilot tests for improvements in the identified critical substations. The results revealed 12 analysis zones, including 120 distribution substations, 150 power transformers, and 751 medium voltage circuits. Among the substations, 73% have ring connections, 15% have TAP connections, and 12% have radial connections. Additionally, 27% of the substations rely on only a single distribution line. The study identified areas with significant challenges in restoring electricity supply, particularly focusing on power transformers: 32 transformers with permanent power line failures requiring load transfer via medium voltage; 67 transformers requiring optimized restoration maneuvers due to specific characteristics; and 4 areas with opportunities to enhance the reliability of the power supply through remote-controlled link systems. The analysis resulted in the installation of 145 remote link systems, which improved restoration rates by over 40%. This approach is expected to be replicated throughout Mexico to identify improvements needed in the national distribution system. [ABSTRACT FROM AUTHOR]

In this study, the drying kinetics, effective moisture diffusivity (D eff), specific energy consumption (SEC), colour, and shrinkage (S b) of pomegranate arils were compared when dried by convective (CV) drying and microwave (MW) drying. The experiments were performed at air temperature of 50, 60, and 70°C and air velocity of 1 m/s for CV drying and 270, 450, and 630 W for MW drying. The results showed that increasing air temperature and MW power increased the D eff. The calculations demonstrated that the maximum D eff for pomegranate arils was obtained for MW drying (630 W). Maximum SEC for pomegranate arils in the CV dryer was 145.12 kWh/kg, whereas in the MW dryer was 35.42 kWh/kg. In MW dryer, the lowest values of colour change and shrinkage were 6.77 and 50.5%, respectively. Comprehensive comparison of the different drying methods (MW and CV) revealed that MW drying had best drying performance for pomegranate arils, considering the drying time, effective moisture diffusion, SEC, colour, and shrinkage.

This research work focused on the evaluation of energy and exergy in the convective drying of potato slices. Experiments were conducted at four air temperatures (40, 50, 60 and 70°C) and three air velocities (0.5, 1.0 and 1.5 m/s) in a convective dryer, with circulating heated air. Freshly harvested potatoes with initial moisture content (MC) of 79.9% wet basis were used in this research. The influence of temperature and air velocity was investigated in terms of energy and exergy (energy utilization [EU], energy utilization ratio [EUR], exergy losses and exergy efficiency). The calculations for energy and exergy were based on the first and second laws of thermodynamics. Results indicated that EU, EUR and exergy losses decreased along drying time, while exergy efficiency increased. The specific energy consumption (SEC) varied from 1.94 × 105 to 3.14 × 105 kJ/kg. The exergy loss varied in the range of 0.006 to 0.036 kJ/s and the maximum exergy efficiency obtained was 85.85% at 70°C and 0.5 m/s, while minimum exergy efficiency was 57.07% at 40°C and 1.5 m/s. Moreover, the values of exergetic improvement potential (IP) rate changed between 0.0016 and 0.0046 kJ/s and the highest value occurred for drying at 70°C and 1.5 m/s, whereas the lowest value was for 70°C and 0.5 m/s. As a result, this knowledge will allow the optimization of convective dryers, when operating for the drying of this food product or others, as well as choosing the most appropriate operating conditions that cause the reduction of energy consumption, irreversibilities and losses in the industrial convective drying processes.

In this research work, two intelligent methods, namely artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are applied to predict the moisture ratio (MR), energy utilization (EU), energy utilization ratio (EUR), exergy loss and exergy efficiency of onion slices drying process by a Multi-Stage Semi-Industrial Continuous Belt (MSSICB) dryer. The experiments are carried out for various temperature levels (40, 55, 70 °C), air velocities (0.5, 1, 1.5 mis) and belt linear speeds (2.5, 6.5 and 10.5 mm/s). The results demonstrate that by using the high levels of air temperature and velocity along with a low belt linear speed, the values of effective moisture diffusivity (Deff), color change (ΔE), EU, EUR, and exergy loss increase whereas the drying time, specific energy consumption (SEC) and exergy efficiency decrease. The highest values of Deff, SEC and ΔE are found to be 3.74 x 10- 11 m²/s, 484.54 MJ/kg and 23.65, respectively. EU and EUR vary between 0.0641 to 0.2451 kJ/s and 0.0848 to 0.5028, respectively. Moreover, exergy loss and efficiency vary in the ranges (0.0182- 0.0555] kJ/s and (62.33-89.25] %, respectively. Moreover, the results showed that the prediction accuracies of MR, EU, EUR, exergy loss and exergy efficiency by the use of ANNs model were 0.9995, 0.9957, 0.9984, 0.9960 and 0.9979, respectively so that the values of0.9998, 0.9972, 0.9991, 0.9962 and 0.9985, obtained by application of ANFIS model, respectively. Therefore, the selected ANN and ANFIS models can be used confidently to estimate the exergy efficient drying conditions for a sustainable drying process by MSSICB. [ABSTRACT FROM AUTHOR]