Temperature and Air Velocity Simulation on Sago Starch Pneumatic Conveying Recirculated Dryer Using Ansys Fluent

Pneumatic Conveying Recirculated Dryer (PCRD) is one of many driers used for drying wet sago starch. The most important components of this PCRD machine are the vertical pipe and the u-bend. The vertical pipe and the u-bend are the primary drying spaces. They must have a good temperature distribution and air velocity dryer. To observe the process of temperature distribution and the air velocity dryer in the vertical pipe and u-bend, Computational Fluid Dynamics (CFD) analysis is required. The research was aimed to analyze the temperature distribution and the air velocity dryer flow in the recirculated pipe of PCRD machine by using CFD analysis. The analysis was based on the variance of the temperature, the air velocity, and the height of the vertical pipe in PCRD machine. The analysis was conducted using Ansys Workbench Fluid Flow ver. 15. This software was used to simulate the temperature and the air flow velocity in the vertical pipe and the u-bend. However, the flow characteristics and patterns of the wet sago starch were not included in the discussion. The turbulence model used in the simulation was the Reynold Stress Models (RSM). The result of the simulation showed that the temperature along the vertical pipe and the u-bend was distributed evenly. The error value between the result of the simulation and the observation was low (0.10–2.04%). The average test value with paired t-test showed that the simulation and observation result was not significantly different. This results indicated that the simulation fit well with the observation value or the real condition in the PCRD machine. The distribution of the temperature and the air velocity dryer in the vertical pipe and the u-bend were able to reduce the moisture content on sago starch from 31% (wb) to 9% (wb). Therefore, the vertical pipe and the u-bend design was appropriate to use in PCRD machine for drying wet sago starch

MODEL JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI KADAR AIR BAHAN PADA PNEUMATIC CONVEYING RECIRCULATED DRYER

Recirculation drying process ofmaterial on pneumatic conveying recirculated dryer (PCRD) are very complexand not linear, so it is very difficult to predict the final required moisture content.The purpose of this study was to develop a model of Artificial Neural Networks (ANN) to predict the final moisture content of the material on the PCRD machine. In this study, PCRD machine has been designed with variability in recirculation, and ANN Graphical User Interface (GUI) application using Neural Network in computer software. AAN  models have been designed using the structure of a network with 11 input neurons, hidden multilayers neurons, and one output neuron with backpropagation learning algorithm. Training and testing of models using 54 and 27 data set observations respectively. The validity test results of the model obtained the value of r2 trainning was 0.99 or 99%, and r2 of the testingwas 0.96 or 96%. This indicated that the models are very valid to predict the final moisture content of the materialon the PCRD machine. The results also revealed RMSE, MAE, MRE value of ANN optimization model was 0.118% wb, 0.056% wb, and 0.644% respectively. While the value of RMSE, MAE, MRE ofthe process of the model testing was 0.226% wb, 0.129 % wb, and 1.496% respectively.Keywords: prediction, moisture content, models, pneumatic recirculated conveying dryer, artificial neural networ

Model Matematis Pengeringan Pati Sagu pada Pneumatic Conveying Recirculated Dryer

Flour drying can be done by using pneumatic conveying dryer (PCD) or flash dryer, but generally required a vertical pipe that is high enough. High vertical pipe can be replaced with a recirculation process to produce a required final moisture content of the material. This study had designed pneumatic conveying recirculated dryer (PCRD), and seek mathematical model of the relationship between the variables of drying process with final moisture content of the material. The purpose of this study is to develop a mathematical model of the relationship between the final moisture content of the material with variables drying process and recirculation continuously in the pneumatic conveying recirculated dryer (PCRD) using dimensional analysis. Buckingham Phi Theorem methods of dimensional analysis is used to find the relationship variables that affect the final moisture content of the material on the PCRD machine. The mathematical model generated in this study are  The coefficient of determination (R2) of the mathematical model is 0.95, or 95 %, this indicates that the model is valid is used to predict the moisture content of the materials in designing PCRD machines. While the sensitivity of the test results showed that the dimensionless product of the most influential are , , and . The model is applicable for drying wet sago starch or other starch material are identical to the physical properties of wet sago starch.ABSTRAKPengeringan bahan-bahan tepung dapat dilakukan dengan pneumatic conveying dryer (PCD) atau flash dryer, namun umumnya memerlukan pipa vertikal yang cukup tinggi. Pipa vertikal yang tinggi dapat diganti dengan proses resirkulasi untuk menghasilkan kadar air akhir bahan yang disyaratkan. Pada penelitian ini telah dirancang pneumatic conveying recirculated dryer (PCRD), serta dicari model hubungan matematis antara variabel-variabel proses pengeringan dengan kadar air akhir bahan. Tujuan penelitian ini adalah mengembangkan model matematis hubungan antara kadar air akhir bahan dengan variabel-variabel proses pengeringan resirkulasi secara kontinyu pada pneumatic conveying recirculated dryer (PCRD) menggunakan analisis dimensi. Metode Buckingham Phi Theorem dalam analisis dimensi digunakan untuk mencari hubungan variabel-variabel yang berpengaruh terhadap kadar air akhir bahan pada mesin PCRD. Nilai koefisien determinasi (R2) dari model matematis tersebut adalah 0,95 atau 95%, hal ini menunjukkan bahwa model tersebut valid digunakan untuk memprediksi kadar air bahan dalam merancang mesin PCRD. Sedangkan hasil uji sensitivitas menunjukkan bahwa dimensionless product yang paling berpengaruh adalah , , dan . Model tersebut berlaku untuk pengeringan pati sagu basah atau bahan-bahan tepung lainnya yang sifat fisiknya identik dengan pati sagu basah.Flour drying could be conducted by using pneumatic conveying dryer (PCD) or flash dryer, but generally it is required a high vertical pipe. The high of vertical pipe may be replaced with a recirculation process to produce a required final moisture content of the material. This study had designed pneumatic conveying recirculated dryer (PCRD) to dryi of wet sago starch. Later, the design was used to determine a mathematical model of the relationship between the variables of drying process with final moisture content of the material. The purpose of this study was to develop a mathematical model of the relationship between the final moisture content of wet sago starch  with variables drying process and recirculation continuously in the pneumatic conveying recirculated dryer (PCRD) using dimensional analysis. Buckingham Phi Theorem methods of dimensional analysis was used to find the relationship variables that affect the final moisture content of wet sago starch on the PCRD machine. The mathematical model generated in this study is      The coefficient of determination (R2) of the mathematical model was 0.948, or 94.8 %, indicated that the model was valid to predict the final moisture content of wet sago starch in designing PCRD machines. While the sensitivity of the test results showed that the dimensionless product of the most influential are , , and . The model was applicable for drying wet sago starch or other starch material which is similat to the physical properties of wet sago starch. ABSTRAKPengeringan bahan-bahan tepung dapat dilakukan dengan pneumatic conveying dryer (PCD) atau flash dryer, namun umumnya memerlukan pipa vertikal yang cukup tinggi. Pipa vertikal yang tinggi dapat diganti dengan proses resirkulasi untuk menghasilkan kadar air akhir bahan yang disyaratkan. Pada penelitian ini telah dirancang pneumatic conveying recirculated dryer (PCRD) untuk mengeringkan pati sagu basah, serta dicari model hubungan matematis antara variabel-variabel proses pengeringan dengan kadar air akhir. Tujuan penelitian ini adalah mengembangkan model matematis hubungan antara kadar air akhir pati sagu basah dengan variabel-variabel proses pengeringan resirkulasi secara kontinyu pada pneumatic conveying recirculated dryer (PCRD) menggunakan analisis dimensi. Metode Buckingham Phi Theorem dalam analisis dimensi digunakan untuk mencari hubungan variabel-variabel yang berpengaruh terhadap kadar air akhir pati sagu basah pada mesin PCRD. Model matematis yang dihasilkan pada penelitian ini adalah      Nilai koefisien determinasi (R2) dari model matematis tersebut adalah 0,948 atau 94,8 %, menunjukkan bahwa model tersebut valid digunakan untuk memprediksi kadar air akhir pati sagu basah dalam merancang mesin PCRD. Sedangkan hasil uji sensitivitas menunjukkan bahwa dimensionless product yang paling berpengaruh adalah , , dan . Model tersebut berlaku untuk pengeringan pati sagu basah atau bahan-bahan tepung lainnya yang sifat fisiknya identik dengan pati sagu basah

Temperature and Air Velocity Simulation on Sago Starch Pneumatic Conveying Recirculated Dryer Using Ansys Fluent

Pneumatic Conveying Recirculated Dryer (PCRD) is one of many driers used for drying wet sago starch. The most important components of this PCRD machine are the vertical pipe and the u-bend. The vertical pipe and the u-bend are the primary drying spaces. They must have a good temperature distribution and air velocity dryer. To observe the process of temperature distribution and the air velocity dryer in the vertical pipe and u-bend, Computational Fluid Dynamics (CFD) analysis is required. The research was aimed to analyze the temperature distribution and the air velocity dryer flow in the recirculated pipe of PCRD machine by using CFD analysis. The analysis was based on the variance of the temperature, the air velocity, and the height of the vertical pipe in PCRD machine. The analysis was conducted using Ansys Workbench Fluid Flow ver. 15. This software was used to simulate the temperature and the air flow velocity in the vertical pipe and the u-bend. However, the flow characteristics and patterns of the wet sago starch were not included in the discussion. The turbulence model used in the simulation was the Reynold Stress Models (RSM). The result of the simulation showed that the temperature along the vertical pipe and the u-bend was distributed evenly. The error value between the result of the simulation and the observation was low (0.10–2.04%). The average test value with paired t-test showed that the simulation and observation result was not significantly different. This results indicated that the simulation fit well with the observation value or the real condition in the PCRD machine. The distribution of the temperature and the air velocity dryer in the vertical pipe and the u-bend were able to reduce the moisture content on sago starch from 31% (wb) to 9% (wb). Therefore, the vertical pipe and the u-bend design was appropriate to use in PCRD machine for drying wet sago starch

Pemodelan Jaringan Syaraf Tiruan untuk Memprediksi Color Difference Tepung Sagu pada Pneumatic Conveying Recirculated Dryer

AbstractPneumatic conveying recirculate dryer (PCRD) is an artificial drying machine which is suitable for flour drying. Previous research has designed PCRD machine to dry the sago flour. The change of sago flour color in PCRD machine is very difficult to be directly measured during the drying process. The aim of this research was to develop an artificial neural network (ANN) model to predict the color difference (ΔE) between wet sago flour before drying and dried sago flour after drying by PCRD machine. The value of ΔE observation was obtained based on the sago color data calculation. The color of sago flour was measured using a color meter (TES 135A). The observation ΔE data were trained and tested on the ANN model using Graphical User Interface (GUI) application, a neuralnetwork- toolbox-based ANN on Matlab R2014a. The training and testing results of the ANN model showed that the best network structure were 12 input neurons, 5 neurons of the first hidden layer, 5 neurons of the second hidden layer, 1 neuron of the third hidden layer, and 1 output neuron (12-5-5-1-1). The value of MSE obtained by the ANN model structure was 0.0005121 with 16 times epoch. The validity test result showed that the coefficient of determination value for the training process (R2 train) equal to 0.987 and for the testing process (R2 test) equal to 0.976. Meanwhile, the optimization analysis result showed that the value of MSE and MRE were quite small, as well as the MSE and MRE value on each parameter variation. It showed that the ANN model is valid to be used to predict the color difference of sago flour drying on PCRD machine.AbstrakPneumatic conveying recirculate dryer (PCRD) adalah salah satu mesin pengering buatan yang cocok digunakan untuk mengeringkan bahan tepung. Pada penelitian terdahulu telah dirancang mesin PCRD untuk mengeringkan tepung sagu. Pengukuran perubahan warna tepung sagu pada mesin PCRD sangat sulit dilakukan secara langsung selama proses pengeringan. Tujuan penelitian ini adalah mengembangkan model jaringan syaraf tiruan (JST) untuk memprediksi perbedaan warna atau color difference (ΔE) antara tepung sagu basah sebelum dikeringkan dengan tepung sagu kering setelah dikeringkan dengan mesin PCRD. Nilai ΔE observasi diperoleh berdasarkan hasil perhitungan data warna tepung sagu. Warna tepung sagu diukur menggunakan color meter (TES 135A). Data ΔE observasi tersebut dilatih dan diuji pada model JST menggunakan aplikasi Graphical User Interface (GUI) JST berbasis neural network toolbox pada Matlab R2014a. Hasil pelatihan dan pengujian model JST menunjukkan bahwa struktur jaringan yang terbaik adalah 12 neuron input, 5 neuron lapisan hidden layer 1, 5 neuron lapisan hidden layer 2, 1 neuron lapisan hidden layer 3, dan 1 neuron output (12-5-5-1-1). Nilai MSE yang dicapai struktur model JST tersebut, sebesar 0,0005121 dengan epoch 16 kali. Hasil uji validitas menunjukkan bahwa nilai koefisien determinasi untuk proses pelatihan (R2 latih) sebesar 0.987, dan proses pengujian (R2uji) sebesar 0.976. Sedangkan hasil analisis optimasi menunjukkan bahwa, nilai MSE dan MRE yang dihasilkan cukup rendah, begitupula nilai MSE dan MRE pada setiap parameter variasi. Hal ini menunjukkan bahwa model JST tersebut valid digunakan untuk memprediksi color difference pengeringan tepung sagu pada mesin PCRD

MODEL JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI KADAR AIR BAHAN PADA PNEUMATIC CONVEYING RECIRCULATED DRYER

Recirculation drying process ofmaterial on pneumatic conveying recirculated dryer (PCRD) are very complexand not linear, so it is very difficult to predict the final required moisture content.The purpose of this study was to develop a model of Artificial Neural Networks (ANN) to predict the final moisture content of the material on the PCRD machine. In this study, PCRD machine has been designed with variability in recirculation, and ANN Graphical User Interface (GUI) application using Neural Network in computer software. AAN models have been designed using the structure of a network with 11 input neurons, hidden multilayers neurons, and one output neuron with backpropagation learning algorithm. Training and testing of models using 54 and 27 data set observations respectively. The validity test results of the model obtained the value of r2 trainning was 0.99 or 99%, and r2 of the testingwas 0.96 or 96%. This indicated that the models are very valid to predict the final moisture content of the materialon the PCRD machine. The results also revealed RMSE, MAE, MRE value of ANN optimization model was 0.118% wb, 0.056% wb, and 0.644% respectively. While the value of RMSE, MAE, MRE ofthe process of the model testing was 0.226% wb, 0.129 % wb, and 1.496% respectively