Modeling
M. Almaei; S. M. Nassiri; M. A. Nematollahi; D. Zare; M. Khorram
Abstract
IntroductionDrying shrimp is one of the storage methods that, while increasing the shelf life, leads to the production of a versatile product with various uses, from consumption as snacks to use as one of the main components of foods. Drying is preferred over other preservation methods because it offers ...
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IntroductionDrying shrimp is one of the storage methods that, while increasing the shelf life, leads to the production of a versatile product with various uses, from consumption as snacks to use as one of the main components of foods. Drying is preferred over other preservation methods because it offers numerous advantages, including extended shelf life, enhanced microbial stability, convenient consumption, reduced transportation costs, increased value, and product diversity.To accurately model these processes and thus obtain information on factors such as shelf life and energy consumption, it is necessary to determine the product’s initial and final temperatures, its geometry and dimensions, and its thermo-physical characteristics. Simulation of different drying processes requires accurate estimation of the effective moisture diffusion coefficient, which is highly dependent on temperature and humidity. Its dependence can be shown by an equation with an Arrhenius structure as an empirical function of humidity and temperature, or by considering the activation energy.It is necessary to have sufficient knowledge about heat and mass transfer characteristics, such as diffusion or penetration coefficient and the heat transfer coefficient to estimate the final temperature and drying time. This study investigated the drying process of peeled farmed shrimp (Litopenaeus vannamei) using a convective hot air dryer. Various parameters such as shrinkage and the effective moisture diffusion coefficient were examined.Materials and MethodsA drying device was built to conduct experimental studies on drying shrimp samples. The experiments were conducted on sliced shrimp meat samples at temperatures of 40, 50, and 60 degrees Celsius, with a constant air velocity of 1.5 m/s. The experimental drying models were based on diffusion theory. In these models, it is assumed that the resistance to moisture diffusion occurs from the outer layer of the food. In most cases, Fick's second law was used to describe the phenomenon of moisture penetration.The study used the standard method of immersion in toluene to measure volume changes in the samples. During the drying process, the volume of the samples was measured at 45-minute intervals, and their volume changes were calculated. To measure the moisture content of the samples, each test started by recording the initial weight of the samples using a digital scale with an accuracy of ±0.001 g. During the drying process, the samples were weighed each time their volume was measured.Shrinkage during the drying process is commonly modeled by finding a relationship between shrinkage and moisture, using linear and non-linear models. In most cases, effective permeability is defined as a function of humidity and temperature. For this purpose, curve-fitting methods were employed to analyze the data collected from experimental tests. The appropriate function was extracted by incorporating the Arrhenius equation, which is applicable to most food items.Results and DiscussionBased on the results of statistical indices, the linear model was the best model for depicting the relationship between shrinkage changes versus moisture ratio changes among the various experimental models evaluated for shrinkage and drying kinetics. Similarly, the Weibull distribution demonstrated superior performance in expressing variations in moisture ratio over time. A moisture dependent experimental model was used to express the variations in the apparent density of shrimp, resulting in a computed range of 1017-1117 kg m-3. Furthermore, an Arrhenius equation was derived to express the effect of moisture content and temperature on the effective diffusion coefficient of shrimp. According to the results, the effective diffusion coefficient of shrimp exhibited variations ranging from 0.08 ×10-9 m2 s-1 to 7.39×10-9 m2 s-1. When deriving the effective diffusion coefficient, the impact of the number of terms in Fick's second law on the variation of the moisture ratio was studied. The findings revealed that increasing the number of terms beyond 100 did not significantly affect the model’s outputs.ConclusionThe linear model had the highest coefficient of determination (R2) among the evaluated shrinkage models, as well as the lowest root mean square error and sum of square error (SSE). This makes it the most optimal model for interpreting shrinkage at the tested temperature levels. The Weibull distribution experimental model proved to be the most suitable for expressing changes in the moisture ratio of shrimp meat slices over time within the evaluated temperature range. The Arrhenius model accurately predicts changes in the effective diffusion coefficient of shrimp slices with respect to temperature and moisture content within the tested temperature range.
A. Nourmohamadi-Moghadami; D. Zare; Sh. Kamfiroozi; A. A. Jafari; M. A. Nematollahi; R. Kamali
Abstract
Introduction During filling a silo, granular material containing a range of particle sizes, the fine material accumulates under the filling point. The inclined surface of stationary bed particle which is formed in silos during filling process acts similar to a sieve through which the smaller particle ...
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Introduction During filling a silo, granular material containing a range of particle sizes, the fine material accumulates under the filling point. The inclined surface of stationary bed particle which is formed in silos during filling process acts similar to a sieve through which the smaller particle fall. This effect is called sifting. As a result of the mentioned effect, much finer particles form a vertical cylindrical zone of high concentration at the silo center. For optimal design in industrial process such as aeration of stored products in silos, filling silos, and wherever granular materials are handled, it is necessary to survey the distribution of the fine materials depending on product and process properties. The objectives of present study were: (a) To study fine change as affected by substantial parameters, (b) To model fine changes at different conditions in silos. Materials and Methods In the present study, an experimental setup consist of a main container, elevator, trapezoidal container and experimental silo was designed and built. Fine content was defined by BCFM (broken corn and foreign material). By applying a new approach, sampling was performed in a radial and vertical direction. The position of each sampling point was determined with a scaled distance from center (R) and from bottom (Z). Local BCFM (BCL) was defined as the value of BCFM in each sampling point. Influential parameters namely, initial BCFM (BCI), volume flow rate (Q) and fill pipe diameter (DF) were considered as treatments. Non-linear regression technique was applied on the experimental data to predict the distribution pattern of fines into the pilot-scale silo. The most appropriate model in a try and error procedure was selected based on highest value of R2 and least value of χ2, RMSE and MRDM. Results and Discussion According to the results of ANOVA, it was found that the effects of all parameters were significant at 5% probability. BCL decreased nonlinearly with a concave down decreasing trend along radial direction due to sifting effect. As a result, most amount of fines remained in the sections closer to the center of the silo. Fine distribution became more uniform with decreasing Z and increasing BCI and DF. Also, the distribution of fine became more uniform with increasing Q. BCL was a nonlinear function of R and a linear function of Z, BCI, Q and DF. Although including more and complex terms increased the model complexity but in the present study considering BCL as an exponential function of R and as an implicit function of Z and R (ZR) improved the quality of the model significantly. The values of 0.94, 1.14, 1.06, 11.39 for R2, χ2, RMSE and MRDM, respectively, gave the best model. The results showed, considerable accumulation of fines occurred at the center of the silo which increased with increase of level of Z. Also, low concentration of fine occurred at the periphery of the silo especially at higher levels of Z. It means that maximum non-uniformity of fine distribution occurred at higher levels of Z. Conclusion The present study investigated distribution of fines during filling affected by main parameters namely, initial BCFM, volume flow rate and fill pipe diameter in a pilot scale silo. A new procedure was developed for measuring the fine material along radial and vertical directions. Distribution of fine was modeled using a developed equation considering the effects of main parameters. The results showed that distribution of fine becomes more uniform with decreasing height and increasing initial BCFM, volume flow rate and fill pipe diameter.
A. Bakhshipour Ziaratgahi; A. A. Jafari; Y. Emam; S. M. Nassiri; S. Kamgar; D. Zare
Abstract
Introduction Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important ...
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Introduction Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques. Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT) is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points) to a reference point to construct a pattern. By comparing this pattern with a set pattern, the desired shape is detected. The aim of this study was to identify the sugar beet plant from some common weeds in a field using the GHT. Materials and Methods Images required for this study were taken at the four-leaf stage of sugar beet as the beginning of the critical period of weed control. A shelter was used to avoid direct sunlight and prevent leaf shadows on each other. The obtained images were then introduced to the Image Processing Toolbox of MATLAB programming software for further processing. Green and Red color components were extracted from primary RGB images. In the first step, binary images were obtained by applying the optimal threshold on the G-R images. A comprehensive study of several sugar beet images revealed that there is a unique feature in sugar beet leaves which makes them differentiable from the weeds. The feature observed in all sugar beet plants at the four-leaf stage was a stretched S-shaped curve at the junction of the leaf and petiole. This unique shape characteristic was used as the pattern for sugar beet detection using GHT. To implement the Hough transform in the images, a 50-member group of samples was prepared from S-shaped curve to build appropriate patterns. Desired features for the Hough transformation were extracted from the patterns. In the next step, the attempts were made to find the images for the shapes similar to each of the patterns. Results and Discussion Plants were thoroughly separated from soil and residues. The accuracy of segmentation algorithm was achieved by almost 100%. The accuracy of the generalized Hough algorithm was evaluated in two stages. In the first stage, the algorithm accuracy was assessed in detecting patterns in the images. Results showed that the accuracy of the algorithm was 96.21%. In the second stage, the algorithm was evaluated for some other test images, whereas the algorithm achieved an overall accuracy of 91.65%. In some cases, the presence of a large overlap between objects in the image reduced the detection accuracy. This was because of two main reasons; 1) high interference and ambiguity in the object edges, so that Hough transform was not able to detect the predefined patterns in the objects and, 2) weeds highly overlapped with sugar beet plants and thereby they were wrongly detected as sugar beet. However, since there is no or little interference between plants at the four-leaf stage, this interference can be eliminated by morphological operations. Due to this fact, it can be said that the results of GHT algorithm are acceptable for the detection of sugar beet in the plants close to four-leaf stage. Conclusion A special feature in the shape of sugar beet leaves was used as a criterion to distinguish between sugar beet and weeds. The results showed that by quantifying this special feature, which is an S-shaped curve near the petioles connection of beet leaves, sugar beet can be discriminated from weeds with an accuracy of 91.65 %. Recalled that this feature is a shape characteristic, therefore, the generalized Hough algorithm must be applied prior to plant canopy development, which is consistent with the critical period of weed control in sugar beet fields.
