Energetic and exergetic analysis of a convective drier: A case study of potato drying process

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 ºC) 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 of 79.9% wet basis were used. The influence of temperature and air velocity was investigated in terms of energy and exergy (energy utilization and energy utilization ratio, exergy losses and exergy efficiency). The calculations for energy and exergy were based on the 1st and 2nd laws of thermodynamics . Results indicated that energy utilization (EU), energy utilization ratio (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 ºC and 0.5 m/s, while minimum exergy efficiency was 57.07% at 40 ºC and 1.5 m/s. Moreover, the values of exergetic improvement potential rate (IP) changed between 0.0016-0.0046 kJ/s and the highest value occurred for drying at 70 ºC and 1.5 m/s, whereas the lowest value was for 70 ºC 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 reduction of energy consumption, irreversibilities and losses in the industrial convective drying processes.info:eu-repo/semantics/publishedVersio

Comparative study of the influence of microwave and hot air drying on mass transfer and qualitative aspects of pomegranate (Punica granatum L.) arils

Despite being an ancient method for food preservation, drying is still nowadays one of the most widely used techniques to extend shelf life of food products. There are numerous methods for drying, either isolated or in combination.1 In the case of microwave drying, the microwave energy penetrates the food and has the capacity to generate heat inside the sample. This quick energy can easily penetrate the inside layers, causing water elimination through an exterior flux of rapidly escaping vapour. Hence, microwave drying method can be faster and produce a higher quality final product, as compared, for example, with conventional hot air drying. 2 In this study, the drying time, effective moisture diffusivity, specific energy consumption, shrinkage, and color properties of the pomegranate arils were compared when dried by convective drying (CVD) and microwave drying (MW). The experiments were conducted at air temperatures of 50, 60 and 70°C and air velocities of 1 m/s for the convective dryer and at power levels of 270, 450 and 630 W for the microwave dryer. The results showed that increasing air temperature and microwave power increased the effective moisture diffusivity. The calculations demonstrate that the maximum effective moisture diffusivity value for pomegranate arils was achieved under microwave drying (630 W). Additionally, the analysis specifies that maximum specific energy consumption for pomegranate arils in the convective dryer was 145.12 kWh/kg whereas it was found to be 35.42 (kWh/kg) when using the microwave dryer. The lowest values for total color change and shrinkage observed were 14.77 and 66.5%, respectively, and they occurred for microwave drying. Comprehensive comparison of the various dryers (microwave and convective) revealed that microwave drying performed best for the drying of pomegranate arils, taking into consideration the drying time, effective moisture diffusion, specific energy consumption, color and shrinkage.info:eu-repo/semantics/publishedVersio

Modelling in Drying Technology of Food Products: A Comprehensive Survey.

Drying of foods has been used to preserve food and agricultural products since immemorial times. However, still nowadays it assumes a prominent place among food processing technologies applied industrially to extend shelf life of foods. Although having some important advantages, like the reduction in water activity and subsequent minimization of degradation reactions of biological, chemical or enzymatic nature, reduction in size for transportation and storage or avoidance of refrigeration systems during transportation and storage, it is also true that drying brings high energy costs and some possible undesirable changes in quality parameters. Hence, the optimization of drying processes is of the utmost importance to minimize energy costs and maximize quality. Mathematical modelling in food process engineering allows important savings, while also guaranteeing the safety of industrial plants and workers, and finally achieving ultimate quality of the dried foods. Because artificial neural networks (ANNs) have been gaining importance in the context of many problems in the fields of engineering, among others, this chapter aims to do a review of scientific literature about the use of artificial neural networks to modelling and optimization of food drying processes. Finally, opportunities and restrictions of the ANNs technique for drying process simulation, optimization, and control are achieved to guide future R&D in this area.info:eu-repo/semantics/publishedVersio

Phenolic Compounds and Antioxidant Activity Modeling in Strawberry by using Artificial Neural Networks (ANNs) Technique

Extraction constitutes a vital procedure when attaining bioactive compounds from plant matrices. Conventional extraction using solvents is highly dependent on variables such as time, temperature, solid to liquid ratio or type of solvent, among others, leading to the need for optimising process variables in order to increase the yield.1,2 This research study focuses on the evaluation of total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberry fruits according to different experimental extraction conditions by application of Artificial Neural Networks (ANNs) technique. The experimental data was applied to train ANNs using feed and cascade forward back propagating models by Levenberg-Marquardt and Baysian regulation algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANNs inputs whereas the three variables of total phenolic compounds, DPPH and ABTS Antioxidant Activities were considered as ANNs outputs. The results demonstrated that the best neural network cascade and feed forward back-propagation topologies for the prediction of total phenolic compounds and DPPH and ABTS antioxidant activity factors were the 3-9-1, 3-4-4-1 and 3-13-10-1 structures with the training algorithm of trainlm, trainbr, trainlm and threshold functions of tansig-purelin, logsig-tansig-tansig and tansig-tansig-purelin, respectively. The best R2 value for the predication of total phenolic compounds and DPPH and ABTS antioxidant activity factors were 0.9806 (MSE=0.0047), 0.9651(MSE=0.0035) and 0.9756 (MSE=0.00286), respectively. According to the comparison of ANNs, the results showed that cascade forward back propagation network had better performance than feed forward back propagation network for the prediction of TPC as feed forward back propagation network in predicting the DPPH and ABTS antioxidant activity factors had more precision than cascade forward back propagation network. According to the obtained results, it was possible to predict TPC and AOA as a function of extraction time, volume/mass ratio, solvent concentration and volume.info:eu-repo/semantics/publishedVersio

Recognition of Paddy, Brown Rice and White Rice Cultivars Based on Textural Features of Images and Artificial Neural Network

Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify rice cultivars using of texture features with using image processing and back propagation artificial neural networks. To identify rice cultivars, five rice cultivars Fajr, Shiroodi, Neda, Tarom mahalli and Khazar were selected. Finally, 108 textural features were extracted from rice images using gray level co-occurrence matrix. Then cultivar identification was carried out using Back Propagation Artificial Neural Network. After evaluation of the network with one hidden layer using texture features, the highest classification accuracy for paddy cultivars, brown rice and white rice were obtained 92.2%, 97.8% and 98.9%, respectively. After evaluation of the network with two hidden layers, the average accuracy for classification of paddy cultivars was obtained to be 96.67%, for brown rice it was 97.78% and for white rice the classification accuracy was 98.88%. The highest mean classification accuracy acquired for paddy cultivars with 45 features was achieved to be 98.9%, for brown rice cultivars with 11 selected features it was 93.3% and it was 96.7% with 18 selected features for rice cultivars

Determination of drying kinetics, speci fi c energy consumption, shrinkage, and colour properties of pomegranate arils submitted to microwave and convective drying

In this study, the drying kinetics, e ff ective moisture di ff usivity (D e ff ) , speci fi c 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 per - formed 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 e ff. The cal - culations demonstrated that the maximum D e ff for pome - granate 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%, respec - tively. Comprehensive comparison of the di ff erent drying methods ( MW and CV ) revealed that MW drying had best drying performance for pomegranate arils, considering the drying time, e ff ective moisture di ff usion, SEC, colour, and shrinkageinfo:eu-repo/semantics/publishedVersio

Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)

his research study focuses on the evaluation of the total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberries according to different experimental extraction conditions by applying the Artificial Neural Networks (ANNs) technique. The experimental data were applied to train ANNs using feed- and cascade-forward backpropagation models with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANN inputs, whereas the three variables of total phenolic compounds, DPPH and ABTS antioxidant activities were considered as ANN outputs. The results demonstrate that the best cascade- and feed-forward backpropagation topologies of ANNs for the prediction of total phenolic compounds and DPPH and ABTS antioxidant activity factors were the 3-9-1, 3-4-4-1 and 3-13-10-1 structures, with the training algorithms of trainlm, trainbr, trainlm and threshold functions of tansig-purelin, tansig-tansig-tansig and purelin-tansig-tansig, respectively. The best R2 values for the predication of total phenolic compounds and DPPH and ABTS antioxidant activity factors were 0.9806 (MSE = 0.0047), 0.9651 (MSE = 0.0035) and 0.9756 (MSE = 0.00286), respectively. According to the comparison of ANNs, the results showed that the cascade-forward backpropagation network showed better performance than the feed-forward backpropagation network for predicting the TPC, and the FFBP network, in predicting the DPPH and ABTS antioxidant activity factors, had more precision than the cascade-forward backpropagation network. The ANN technique is a potential method for estimating targeted total phenolic compounds and the antioxidant activity of strawberries.info:eu-repo/semantics/publishedVersio