ISSN Original Article Genetic Algorithm Optimizing Approach in Rosa Petals Hot Air Dryer

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1 Available online at International Journal of Agricultural and Food Science Universal Research Publications. All rights reserved ISSN Original Article Genetic Algorithm Optimizing Approach in Rosa Petals Hot Air Dryer Ahmad Banakar *, S. R. Karimi Akandi Mechanical Agriculture Department, Tarbiat Modares University, Tehran, Iran * : ah_banakar@modares.ac.ir Phone * : , (Mob) Fax * : Received 18 May 2012; accepted 06 July 2012 Abstract Damask rose (scientific name: Rosa Damascene) belongs to Rosaceae families and is valuable in medicine. In this study, the influence of air temperature and velocity are investigated on hot air dryer of the rose petals. Artificial drying of the agricultural products is one of the most common maintenance procedures. Suitable drying methods reduce the waste and damage during the storage and save the quality of the products. Optimization and control of the drying process is also based on the precise modeling. Here, moisture ratio was estimated by feed-forward neural network via temperature, air velocity, time period and moisture content to predict the next moisture content and therefore the drying rate. Later by using a genetic algorithm the optimum inputs condition is obtained. The results show that the drying procedure time is minimized perfectly by using the optimum conditions. The result show that neural network with one hidden layer and 29 hidden neurons can models the drying procedure very well with the performance of Applying the optimum condition decreases the drying time from 40 minutes to 15 minutes Universal Research Publications. All rights reserved Key words: Genetic Algorithm; Neural Network; Hot air dryer; Rosa petals; Moisture ratio Introduction Drying preserves the product by lowering the amount of moisture in the material while freezing preserves it by lowering its temperature below the freezing point of water. The drying technique permits early harvest, harvest season planning, lighter weight for transportation and less space for long time storage without deterioration [1]. Air drying is the most commonly employed commercial technique for drying food and chemical products [2, 3]. Hot air drying is one of the usual unit operations in food processing which decreases drying time and substantially preserves the quality of the dried product [4] which is strongly affected by the conditions of drying [5]. Physical and thermal properties of the agricultural products such as heat and mass transfer, moisture diffusion, activation energy and energy consumption are required for an ideal dryer design [6]. The most important aspect of drying technology is modeling of the drying processes and equipment. Therefore, modeling of drying is crucial for optimizing the operating parameters and performance improvements of the drying systems [7]. Furthermore, it allows design engineers to choose the most suitable operating conditions and then size of the drying equipment and chamber in order to gather desired operating conditions. Many studies have been conducted for the mathematical modeling of drying behavior and determining the drying kinetics of various vegetables and fruits such as kiwifruit [4], red pepper [8], Üryani plum [9], pear fruit [10] and rosehip [11, 12]. Most of these mathematical models have presented physical and thermal properties of the drying material as a function of time for any air temperature and air velocity; therefore, several models have been necessary. In particular, the solution of these equations must allow for the prediction of the process parameters as a function of input parameters, air temperature and air velocity at any time in the dryer equipment [13]. Due to the dynamic behavior and complexity of the drying process of the agricultural products, mathematical and regression approaches are considered faulty. Thus, a model should be 60

2 stable and precise enough in case uncertainty exists in the inputs. Artificial intelligence methods, such as neural networks, have gained momentum and are suitable for identifying plant and fruit responses [14-17]. Huang and Mujumdar developed a neural network model to predict the performance of an industrial paper dryer [18]. Thyagarajan et al. modeled an air heater plant for a dryer using a neural network [19]. Sreekanth et al. predicted psychometric air parameters using various neural network models [20]. On the other hand, Genetic Algorithm (GA) is one of the search methods and optimization techniques which aim at an optimal value of a complex objective function by simulating biological evolutionary processes based, as in genetics, on crossover and mutation. The principles of GA are based on natural competitions of beings for appropriating limited natural sources [21-22]. Nazghelichia et al. [23] used GA for finding the optimal topology of ANN, which predicts energy and exergy during the fluidized bed drying of carrot cubes. Morimoto, De Baerdemaeker and Hashimoto (1997) [24] combined an artificial neural network and genetic algorithm method for optimal control of the fruit-storage process. Morimoto, Purwanto, Suzuki and Hashimoto (1997) [25] used genetic algorithm for optimizing heat treatment for fruit during the storage. The aim of the present work is to determine the drying characteristic of carrot under different drying air conditions and to estimate its dynamical drying behavior using the neural network while a mathematical model of drying procedure is optimized using the genetic algorithm. Damask rose is widely used in perfume production, pharmaceutical and food industries. After harvesting the petals, their moisture content is high for optimal storage. Due to the high speed deteriorate of wet petals and suitable required environment for the growing of the living organisms such as yeast, molds and bacteria the drying agricultural products to appropriate moisture content is essential. On the other hand, in the recent years scientists realize the losses resulting by using chemical pesticides and preservation additives for storage and preparing of the essence and various diseases such as cancer has been associated with them. Damask rose (Rosa damascena), is an important species among the scented roses, yields a high valuable in rose water, rose essence and dried petals that are used in medicine, food and perfume industry, as well as make-up and health products. Damask rose is also used as an ornamental plant in parks, gardens and houses [26, 27]. Qualitative and quantitative essence and its economic justification depend on the suitable drying methods. Therefore, designing of an operating system to minimize the drying procedure time, seems to be very necessary for drying process. Moreover, one of the main objectives of the dryer design is to optimize energy and time, which needs an optimal controller to control the process online and also to minimize the required time and energy. So, the main objective of this work is to experimentally determine drying curves of damask roses as a function of dryer temperature, dryer air velocity and time based on the artificial neural network models. Through using these models and the genetic algorithm, an optimal online control is proposed to minimize time procedure. Theoretical considerations Mathematical model The Moisture Ratio (MR) and Drying Rate (DR) of Rosa petals were calculated using the following equations: M M e M. (1) R M 0 M e M t dt M e (2) DR dt In these equations M 0, M t and M e are the initial moisture content, moisture content at a specific time and equilibrium moisture content, respectively. M t+dt is the moisture content at t+dt, which dt is the time period. Artificial Neural Network Model Artificial neural networks were used in modeling complex and non-linear biological systems and technological faults. Several Neural Network (NN) topologies have been considered by different researchers for modeling the drying process. The selection of an appropriate NN topology is important in terms of model accuracy and simplicity. The choice of a topology depends on a careful selection of the input system variables and controlled output variables which are moisture contents in different layers of the material bed. Each topology consists of one or two hidden layers with different numbers of hidden neurons. Figure 1 shows the schematic of the feed-forward neural network with two hidden layers and multiple outputs. The Levenberg- Marquardt back-propagation algorithm was used to train the networks. Figure 1: Schematic model of feed-forward neural network Genetic Algorithm The genetic algorithm is a global search algorithm, which is designed to mimic the principles of biological evolution in natural genetic systems. There are various types of operators that are used in GA; however, selection, recombination and mutation are used quite often. In brief terms, a selection operator allocates greater survival to better individuals, evaluates the population according to fitness 61

3 function and chooses the best individuals. Weighted roulette wheels approach is opted for parent selection. After the selection, in the crossover step, two individuals are chosen randomly and are reproduced into two new individuals. Recombination is a genetic operator that combines bits and pieces of parental solutions to form new, and possibly better, offspring. By successive selection and reproduction of superior individuals, the population obtains more natural sources. The GA simulates this process and calculates optimum objective functions. In contrast to recombination which creates a new individual by combining the traits of two or more parents, mutation acts through simply modifying a single individual. Mutation operation consists of randomly altering the value of each element of the chromosome according to the mutation probability. Mutation enhances the GA ability by intermittently injecting a random point in order to better search the entire parameter space, which allows the GA to possibly escape from local optima. These four operations are repeated until the desired convergence on the optimal or near-optimal solutions is achieved. In this paper, the one-point crossover with probability of 0.8 is used for the reproduction of offspring. The number of mutations in a solution is randomly selected with a very small value of probability, i.e., The technique of generational replacement without duplication is used for reproduction, and to test for a new solution. In this technique, all the solutions of one generation are replaced by the solutions of the next generation. Research Methodology Damask (Rosa damascene) used in drying experiments were provided from Tabriz, Iran and were harvested in July, They were quickly put in refrigerator and preserved at 4 C for later used. The petals were selected with the average physical properties of about 5-12 mm width, 7-15 mm length, mm 2 surface and g weight. The moisture content was determined by the Dean-Stark using toluene as described by Balladin et al. [26]. For drying, a laboratory scale hot air dryer was used (Figure 2) which was kept on until reaching a steady state. It essentially consisted of an adjustable centrifugal blower, air heating duct and drying chamber. The air supplied by the centrifugal blower was heated up to the required temperature in an air heating duct. The air velocity was changed by an inverter through changing the fan speed. It was measured directly in the drying chamber and its measurement range was 0.5 m/s. The air heating duct was constructed from galvanized metal sheets in the form of a cylinder which was 137 mm in diameter and 450 mm in length. The drying chamber s temperature was measured and controlled at ±2 C using a suitable controller. The weighing system was typed by an electronic balance which had an accuracy of g and was placed outside the drying chamber. The moisture content of petals was determined by the oven method (105 C for 2 h). The drying experiments were performed at four levels of drying air temperatures of 40, 50, 60, and 70 C along with Figure 2: Schematic diagram of the laboratory dryer. three levels of air flow velocities of 0.5, 1.0, and 1.5 m/s. Table I shows the range and conditions of experimental runs. To develop an optimal control system with the maximum drying rate which minimizes the drying time, the air velocity and temperature which maximize DR should be founded in any time. Therefore, a drying plant model is needed to predict the moisture ratio from the input element air velocity and temperature in any step of drying procedure for a special time period. For this purpose, an artificial NN with four inputs (air velocity, air temperature, time period and current moisture ratio) and one output (next moisture ratio) are used (Figure 1). After calculating the moisture ratio, the drying rate is calculated using (2). Five hundred fifty experimental data were ascertained. 70 percent of which were used in training the models and 30 percent were used in investigating the validation and evaluating the models. Optimization algorithm: To find an optimized control parameter, air velocity and temperature in any time and drying step, a learned NN was used with GA. In the beginning of the procedure, the moisture ratio was one. Using NN, the next moisture ratio (for example, after 5 minutes) was calculated. The control parameters, air and temperature, were changed by GA and drying rate is calculated using NN. The optimized parameter was founded by maximizing the drying rate as a fitness function. After each step, the moisture ratio was saved for optimizing the procedure in the next step. An optimized algorithm is shown in Figure 3. Results and discussion Figures 4 to 6 show the plot of drying curves of Rosa petals at the following temperatures and air velocities, respectively: 40, 50, 60, 70 C and 0.5, 1 and 1.5 m\s. All these curves reveal that decreasing the moisture content is in a non-linear relationship with time, indicating that the moisture movement is controlled by diffusion and diffusion depends on the moisture content of the samples, which confirms findings [28]. Improvement of drying rates was conducted rather differently at different drying temperatures. As shown in Figure 4, the rate of moisture decrease is faster 62

4 START t=0 ; MR=1 ; Set dt Initialize Population (T & V) Calculate MR(t+dt) by NN Evaluate DR of each population (Fitness) DR==(MR(t)-MR(t+dt))/dt Yes Target reached or g max? Figure 4: Relationship between air temperature and drying time at air velocity 0.5 m/s. Save Best Solution No t=t+dt Selection Crossove No MR<1? or t>80? Mutation Yes STOP Figure 3: Optimization Algorithm based on Genetic Algorithm TABLE I. RANGE AND CONDITIONS OF EXPERIMENTAL RUNS Investigated parameters Range Drying air temperature ( C) 40, 50, 60 and 70 (C) Air velocity (m/s) 0.5, 1 and 1.5 m\s Rosa petals condition without stem TABLE II. NEURAL NETWORK MODELING OF MOISTURE RATIO Model Neural Network RMSE No. Neuron Training Data Testing Data TABLE III. OPTIMIZATION BY USING GENETIC ALGORITHM dt t T V DR MR(t) MR(t+dt) Figure 5: Relationship between air temperature and drying time at air velocity 1 m/s. at 40 C in comparison with 50 C. However, when the drying temperature shifts from 50 to 60 C, a slightly larger difference is observed in the moisture reduction rates. More capability of moisture removing at temperatures higher than 40 C can possibly be explained by a couple of reasons. First, Rosa petals morphology was different; large pores inside Rosa petals were formed at the temperature higher than 40 C. These large pores facilitated the transport of moisture. The acceleration of the movement of water molecules at higher temperatures also took part in a more rapid decrease of the moisture content [29]. By increasing the speed from 0.5 to 1 and 1.5 m\s, the distance between curves 40 and 50 C and curves 50 and 60 and 70 C was the most and the least, respectively. Typical drying rate curves at different drying temperatures are shown in Figures 7-9. The variation of moisture content with drying time was obtained at each drying temperature. From these results, drying rates were calculated and plotted against time. They were computed by a Lagrangian numeric differentiation technique which utilizes three data points. It was observed that the drying rate decreased continuously throughout the drying period. The constant rate period was not observed in any of the experimental runs for the entire duration. The drying of water chestnut slices took place in the falling rate period. These figures show that, in the beginning of the drying procedure, the drying rate of petals increases by increasing temperature in all air velocity. However, in the second step and in the air velocity of 0.5 m/s, the drying rates at 60 and 70 C is very close. In this step, in the air velocity of 1 m/s, the 63

5 Figure 6: Relationship between air temperature and drying time at air velocity 1.5 m/s. Figurei7: Drying Rate of Rosa petals in different air temperature at air velocity 0.5 m/s. Figurei8: Drying Rate of Rosa petals in different air temperature at air velocity 1 m/s. drying rate at 50 is more than 60 C which is already less than the drying rate of 70 C. In this step, in the air velocity of 1.5 m/s, the drying rate at 50 is more than 60 C and 70 c. This is evident in the following steps as well, which means that the relation of drying rate with air velocity and air temperature is non-linear and non-evident. Table II shows the NN modeling of the moisture ratio. In this table, several neural network models with one hidden layer and different numbers of hidden neurons are listed. Different topologies of multilayer perceptron neural networks with tangent-sigmoid and linear activation function, in hidden and output layers, respectively, were considered and trained with Levenberg-Marquardt learning algorithm. Finally, the best Figurei9: Drying Rate of Rosa petals in different temperature at air velocity 1.5 m/s. result with minimum RMSE values were presented here. Some models perform better with the training data but do not give a good result in the testing data. According to the result, it is obvious that increasing the complexity of network does not result in better results. Finally, the best model in the predictive result was selected, i.e. NN with one hidden layer and 29 hidden neurons with the performance of Table III shows the result of the optimization procedure. These results are for the time periods five and two minutes and shows that using optimum air velocity and air temperature decreases the drying time to a large extent. When the optimum condition is applied, the moisture content decreased below ten percent less than fifteen minutes; whereas this was 40 minutes for the temperature and air velocity of 70 0 C and 0.5 m/s in the best condition of the earlier experiments (Figure 5). By optimizing the drying procedure every 2 minutes, the optimum time for receiving the moisture content to ten percent was 22 minutes. This algorithm was checked several times and the result was very close to the presented result. The result for optimizing the procedure to less than 5 minutes seemed to be better than to 5 minutes. However, here it was not the case. The reason was mostly because the experimental data were for five minutes and using these data for less than this time period was an error. Conclusion Damask rose is widely used in perfume production and pharmaceutical and food industries. Its qualitative and quantitative essence and economic justification depend on the suitable drying methods. Therefore, in this paper, the drying process of damask roses was optimized using genetic algorithm and neural network model as a function of dryer temperature, air velocity, time period and moisture content. According to the result, feed-forward neural networks are able to model and predict the drying procedure. By developing a genetic algorithm, the results show that using an optimum input control in the drying procedure, the time can be minimized. Based on the achieved results, the application 64 air

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