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An effective and innovative strategy for modeling and simulation of deep-bed solar greenhouse drying of biomaterials using TRNSYS software is presented in this article. Deep-bed solar greenhouse drying principles were written in Compaq Visual FORTRAN programming language through the combination of thin-layer drying principles and equilibrium drying model and the created new component was added to the software's library. Experimental data for deep-bed solar greenhouse drying of chamomile obtained at different specific loads of 15, 30, 45, and 60 kg/m2were used for model verification. In the newly developed component, thin-layer drying principles with the assumption that each bed composed of a series of thin layers was employed to simulate the drying process of chamomile. The moisture content and temperature profiles of the material as well as the temperature and humidity ratio profiles of drying air within bulk were computed using a rarely employed equilibrium model. There was good agreement between simulation results and experimental moisture content for deep-bed solar greenhouse drying of chamomile with root normalized mean square error lower than 9.3%. Findings of this study confirmed that TRNSYS is a powerful tool for optimization of the operation and design of solar dryers. The advantage of using the proposed strategy is that solar greenhouse drying systems can be effectively designed and developed for a wide range of biomaterials under different climate conditions without need for demonstration projects. [ABSTRACT FROM PUBLISHER]

In this study, the effects of drying medium temperature and velocity were surveyed on the image texture features of shrimp (Penaeusspp.) batches in a dryer equipped with a perpendicular dual-view computer vision system (CVS). This was carried out by applying an innovative rotation- and scale-invariant image texture processing approach with the capability of eliminating the effects of sample shrinkage on the visual textural features. Moreover, the variations in image texture parameters were investigated with moisture ratio, color, and geometrical characteristics of the shrimp samples. Drying experiments were conducted at hot air drying (HAD) temperatures of 50–90°C and superheated steam drying (SSD) temperatures of 110–120°C with drying medium velocities of 1–2 m/s. Several configurations of a multilayer perceptron artificial neural network (MLP-ANN) were also used to predict the moisture ratio and the geometrical characteristics of the shrimp batch using the image texture parameters. Generally, the image texture features were significantly affected by drying medium temperatures (p < 0.01), and the effects of drying medium velocities on the textural properties were nonsignificant (p > 0.05). Additionally, the higher drying temperatures generated products with uniform and regular texture patterns. The SSD produced samples with somewhat nonuniform and irregular texture patterns compared with HAD at 90°C. Finally, selected MLP-ANN topologies successfully predicted the moisture ratio and the geometrical characteristics of the shrimp batch using the textural properties with correlation coefficients higher than 0.99. [ABSTRACT FROM AUTHOR]