scholarly journals Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Priyanka Dhurve ◽  
Ayon Tarafdar ◽  
Vinkel Kumar Arora
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fluidized Bed ◽  
Drying Kinetics ◽  
Ann Model ◽  
Neural Network Models ◽  
Drying Behavior ◽  
Pumpkin Seeds ◽  
Artificial Neural ◽  
Semiempirical Models

Pumpkin seeds were dried in a vibro-fluidized bed dryer (VFBD) at different temperatures at optimized vibration intensity of 4.26 and 4 m/s air velocity. The drying characteristics were mapped employing semiempirical models and Artificial Neural Network (ANN). Prediction of drying behavior of pumpkin seeds was done using semiempirical models, of which, one was preferred as it indicated the best statistical indicators. Two-term model showed the best fit of data with R2 − 0.999, and lowest χ2 − 1.03 × 10−4 and MSE 7.55 × 10−5. A feedforward backpropagation ANN model was trained by the Levenberg–Marquardt training algorithm using a TANSIGMOID activation function with 2-10-2 topology. Performance assessment of ANN showed better prediction of drying behavior with R2 = 0.9967 and MSE = 5.21 × 10−5 for moisture content, and R2 = 0.9963 and MSE = 2.42 × 10−5 for moisture ratio than mathematical models. In general, the prediction of drying kinetics and other drying parameters was more precise in the ANN technique as compared to semiempirical models. The diffusion coefficient, Biot number, and hm increased from 1.12 × 10−9 ± 3.62 × 10−10 to 1.98 × 10−9 ± 4.61 × 10−10 m2/s, 0.51 ± 0.01 to 0.60 ± 0.01, and 1.49 × 10−7 ± 4.89 × 10−8 to 3.10 × 10−7 ± 7.13 × 10−8 m/s, respectively, as temperature elevated from 40 to 60°C. Arrhenius’s equation was used to the obtain the activation energy of 32.71 ± 1.05 kJ/mol.

scholarly journals Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

2020 ◽  
Vol 68 (2) ◽  
pp. 143-147
Author(s):  
Abira Sultana ◽  
Murshida Khanam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Rice Production ◽  
Series Data ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Estimated Error

Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh. Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

scholarly journals Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1594 ◽  
Author(s):  
Piotr Ofman ◽  
Joanna Struk-Sokołowska
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Batch Reactor ◽  
Nitrogen Concentration ◽  
Nitrogen Form ◽  
Ann Model ◽  
Nitrogen Forms ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural

Paper presents artificial neural network models (ANN) approximating concentration of selected nitrogen forms in wastewater after sequence batch reactor operating with aerobic granular activated sludge (GSBR) in the anaerobic and aerobic phases. Aim of the study was to determine parameters conditioning effectiveness of selected nitrogen forms removal in GSBR reactor process phases. Models of artificial neural networks were developed separately for N-NH4, N-NO3 and total nitrogen concentration in particular process phases of GSBR reactor. In total, 6 ANN models were presented in this paper. ANN models were made as multilayer perceptron (MLP), which were learned using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Developed ANN models indicated variables the most influencing of particular nitrogen forms in aerobic and anaerobic phase of GSBR reactor. Concentration of estimated nitrogen form at the beginning of anaerobic or aerobic phase, depending on ANN model, in all ANN models influenced approximated value. Obtained determination coefficients varied from 0.996 to 0.999 and were depending on estimated nitrogen form and GSBR process phase. Hence, developed ANN models can be used in further studies on modeling of nitrogen forms in anaerobic and aerobic phase of GSBR reactors.

scholarly journals Operational Dst index prediction model based on combination of artificial neural network and empirical model

2021 ◽  
Author(s):  
Wooyeon Park ◽  
Jaejin Lee ◽  
Kyung-Chan Kim ◽  
JongKil Lee ◽  
Keunchan Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Space Weather ◽  
Large Scale ◽  
Geomagnetic Storms ◽  
Ann Model ◽  
Neural Network Models ◽  
Dst Index ◽  
Artificial Neural

<p class="Abstract" style="margin: 6pt 0cm 0.0001pt; font-size: 12pt; font-family: 굴림, sans-serif; color: rgb(0, 0, 0); text-align: justify; text-indent: 36pt;"><span lang="EN-US" style="font-family: &quot;Times New Roman&quot;, serif;">In this paper, an operational Dst index prediction model is developed by combining empirical and artificial neural network models. Artificial neural network algorithms are widely used to predict space weather conditions. While they require a large amount of data for machine learning, large-scale geomagnetic storms have not occurred sufficiently for the last 20 years, ACE and DSCOVR mission operation period. Conversely, the empirical models are based on numerical equations derived from human intuition and are therefore applicable to extrapolate for large storms. In this study, we distinguish between Coronal Mass Ejection (CME) driven and Corotating Interaction Region (CIR) driven storms, estimate the minimum Dst values, and derive an equation for describing the recovery phase. The combined Korea Astronomy and Space Science Institute (KASI) Dst Prediction (KDP) model achieved better performance contrasted to Artificial Neural Network (ANN) model only. This model could be used practically for space weather operation by extending prediction time to 24 hours and updating the model output every hour.<o:p></o:p></span></p>

Prediction of the tactile comfort of fabrics from functional finishing parameters using fuzzy logic and artificial neural network models

2019 ◽  
Vol 89 (19-20) ◽  
pp. 4083-4094 ◽  
Author(s):  
Melkie Getnet Tadesse ◽  
Emil Loghin ◽  
Marius Pislaru ◽  
Lichuan Wang ◽  
Yan Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Extreme Values ◽  
Subjective Evaluation ◽  
Fuzzy Model ◽  
Percentage Error ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural

This paper aims to predict the hand values (HVs) and total hand values (THVs) of functional fabrics by applying the fuzzy logic model (FLM) and artificial neural network (ANN) model. Functional fabrics were evaluated by trained panels employing subjective evaluation scenarios. Firstly, the FLM was applied to predict the HV from finishing parameters; then, the FLM and ANN model were applied to predict the THV from the HV. The estimation of the FLM on the HV was efficient, as demonstrated by the root mean square error (RMSE) and relative mean percentage error (RMPE); low values were recorded, except those bipolar descriptors whose values are within the lowermost extreme values on the fuzzy model. However, the prediction performance of the FLM and ANN model on THV was effective, where RMSE values of ∼0.21 and ∼0.13 were obtained, respectively; both values were within the variations of the experiment. The RMPE values for both models were less than 10%, indicating that both models are robust, effective, and could be utilized in predicting the THVs of the functional fabrics with very good accuracy. These findings can be judiciously utilized for the selection of suitable engineering specifications and finishing parameters of functional fabrics to attain defined tactile comfort properties, as both models were validated using real data obtained by the subjective evaluation of functional fabrics.

COMPARISON OF PRODUCT QUALITY ESTIMATION OF PROPYLENE POLYMERIZATION IN LOOP REACTORS USING ARTIFICIAL NEURAL NETWORK MODELS

2016 ◽  
Vol 78 (6-13) ◽  
Author(s):  
Nur Fazirah Jumari ◽  
Khairiyah Mohd-Yusof
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Models ◽  
Melt Flow Index ◽  
Propylene Polymerization ◽  
Flow Index ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as xylene soluble, particle size distribution and melt flow index (MFI). As an alternative to the online instruments and conventional laboratory tests, these properties can be estimated using model based-soft sensor. This paper presents models for soft sensors to measure MFI in industrial polypropylene loop reactors using artificial neural network (ANN) model, serial hybrid neural network (HNN) model and stacked neural network (SNN) model. All models were developed and simulated in MATLAB. The simulation results of the MFI based on the ANN, HNN, and SNN models were compared and analyzed.  The MFI was divided into three grades, which are A (10-12g/10 min), B (12-14g/10 min) and C (14-16 g/10 min). The HNN model is the best model in predicting all range of MFI with the lowest root mean square error (RMSE) value, 0.010848, followed by ANN model (RMSE=0.019366) and SNN model (RMSE=0.059132). The SNN model is the best model when tested with each grade of the MFI. It has shown lowest RMSE value for each type of MFI (0.012072 for MFI A, 0.017527 for MFI B and 0.015287 for MFI C), compared to HNN model (0.014916 for MFI A, 0.041402 for MFI B and 0.046437 for MFI C) and ANN model (0.015156 for MFI A, 0.076682 for MFI B, and 0.037862 for MFI C).

scholarly journals Forecasting stock index based on hybrid artificial neural network models

2019 ◽  
Vol 3 (1) ◽  
pp. 52-57
Author(s):  
Ta Quoc Bao ◽  
Le Nhat Tan ◽  
Le Thi Thanh An ◽  
Bui Thi Thien My
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Arima Model ◽  
Network Models ◽  
Stock Index ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural

Forecasting stock index is a crucial financial problem which is recently received a lot of interests in the field of artificial intelligence. In this paper we are going to study some hybrid artificial neural network models. As main result, we show that hybrid models offer us effective tools to forecast stock index accurately. Within this study, we have analyzed the performance of classical models such as Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) model and the Hybrid model, in connection with real data coming from Vietnam Index (VNINDEX). Based on some previous foreign data sets, for most of the complex time series, the novel hybrid models have a good performance comparing to individual models like ARIMA and ANN. Regarding Vietnamese stock market, our results also show that the Hybrid model gives much better forecasting accuracy compared with ARIMA and ANN models. Specifically, our results tell that the Hybrid combination model delivers smaller Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) than ARIMA and ANN models. The fitting curves demonstrate that the Hybrid model produces closer trend so better describing the actual data. Via our study with Vietnam Index, it is confirmed that the characteristics of ARIMA model are more suitable for linear time series while ANN model is good to work with nonlinear time series. The Hybrid model takes into account both of these features, so it could be employed in case of more generalized time series. As the financial market is increasingly complex, the time series corresponding to stock indexes naturally consist of linear and non-linear components. Because of these characteristic, the Hybrid ARIMA model with ANN produces better prediction and estimation than other traditional models.  

Geometry Optimization of Cylindrical Flaps of Oscillating Wave Surge Converters Using Artificial Neural Network Models

2019 ◽  
Author(s):  
Chen-Chou Lin ◽  
Yi-Chih Chow ◽  
Yu-Yu Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Geometry Optimization ◽  
Network Models ◽  
Upper And Lower Bounds ◽  
Design Parameters ◽  
Ann Model ◽  
Neural Network Models ◽  
Wave Condition ◽  
Artificial Neural

Abstract This paper presents an optimization algorithm based on the Artificial Neural Network (ANN) to determine the optimal shape, size, and density for the cylindrical flap of the Bottom-Hinged Oscillating Wave Surge Converter (BH-OWSC) that can extract maximal wave power under a given wave condition. Eight parameters are selected, and their upper and lower bounds are set at the initial stage, and then 64 cases with different combinatorial parametric settings are generated by the Design of Experiment process. The 64 cases are then fed into FLOW-3D to simulate the operations of the BH-OWSC under the given wave condition for calculating the capture factor, establishing a database for subsequent ANN data training purpose. To search the maximal capture factor in the specific range of the flap models, we fed 107 random models with various levels of design parameters into the ANN model, which adopts the backpropagation architecture and one hidden layer with ten neuron cells. After three complete random searches, and by simulating the ANN-derived flap’s geometry using FLOW-3D, the result shows that a maximal capture factor of 1.824 can be obtained. The major geometric features of the flap with maximal capture factor are (1) the cylinder axis of the flap inclines to the opposite direction of incident wave propagation, (2) the cylinder’s sectional diameters are about the same size, and (3) the smaller flap density the better power capturing performance.

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