scholarly journals Application of artificial neural networks in the prediction of sugarcane juice Pol

2019 ◽  
Vol 23 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Anderson P. Coelho ◽  
João V. T. Bettiol ◽  
Alexandre B. Dalri ◽  
João A. Fischer Filho ◽  
Rogério T. de Faria ◽  
...  
Keyword(s):  
Activation Function ◽  
High Accuracy ◽  
Sugarcane Juice ◽  
Mean Square ◽  
Training Algorithm ◽  
Ann Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mean Square Errors ◽  
Innovative Techniques

ABSTRACT Innovative techniques that seek to minimize the costs of production and the laboriousness of certain operations are one of the great challenges in the sugar-energy sector nowadays. Thus, the objective of the present study was to estimate the Pol values of sugarcane juice as a function of °Brix and wet cake weight (WCW) using artificial neural network (ANN) modeling. A database was organized consisting of 204 technological analyses from a field experiment with 15 treatments and 2 years of evaluation. 75% of the data were used for the calibration of the model and 25% for its validation. Multilayer Perceptron ANNs were used for calibration and validation of the data. Before calibration, the variables were normalized. The training algorithm used was backpropagation and the activation function was the sigmoid. The ANNs were established with two hidden layers and the number of neurons ranging from 4 to 20 in each. The 15 ANNs with the lowest root mean square errors were randomly presented by the software, among which 6 were chosen to verify the accuracy. The ANNs had a high accuracy in the estimation of sugarcane juice Pol, both in the calibration phase (R2 = 0.948, RMSE = 0.36%) and in the validation (R2 = 0.878, RMSE = 0.41%), and can replace the standard method of analysis. Simpler networks can be trained to have the same accuracy as more complex networks.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Natasha Munirah Mohd Fahmi ◽  
◽  
Nor Aira Zambri ◽  
Norhafiz Salim ◽  
Sim Sy Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Climatic Factors ◽  
Activation Function ◽  
Solar Panels ◽  
Ann Model ◽  
Power Characteristics ◽  
Step Procedure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.

scholarly journals Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids

2020 ◽  
Author(s):  
Behzad Vaferi
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thermophysical Properties ◽  
Convective Heat Transfer Coefficient ◽  
Industrial Applications ◽  
Mean Square ◽  
Ann Models ◽  
Artificial Neural ◽  
Mean Square Errors

Nanofluids have recently been considered as one of the most popular working fluid in heat transfer and fluid mechanics. Accurate estimation of thermophysical properties of nanofluids is required for the investigation of their heat transfer performance. Thermal conductivity coefficient, convective heat transfer coefficient, and viscosity are some the most important thermophysical properties that directly influence on the application of nanofluids. The aim of the present chapter is to develop and validate artificial neural networks (ANNs) to estimate these thermophysical properties with acceptable accuracy. Some simple and easy measurable parameters including type of nanoparticle and base fluid, temperature and pressure, size and concentration of nanoparticles, etc. are used as independent variables of the ANN approaches. The predictive performance of the developed ANN approaches is validated with both experimental data and available empirical correlations. Various statistical indices including mean square errors (MSE), root mean square errors (RMSE), average absolute relative deviation percent (AARD%), and regression coefficient (R2) are used for numerical evaluation of accuracy of the developed ANN models. Results confirm that the developed ANN models can be regarded as a practical tool for studying the behavior of those industrial applications, which have nanofluids as operating fluid.

scholarly journals Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 148 ◽  
Author(s):  
Bikhtiyar Ameen ◽  
Heiko Balzter ◽  
Claire Jarvis ◽  
James Wheeler
Keyword(s):  
Test Data ◽  
Sunshine Duration ◽  
Mean Square ◽  
Climate Variables ◽  
Accurate Data ◽  
Training Algorithm ◽  
Clear Sky ◽  
Levenberg Marquardt ◽  
Artificial Neural ◽  
Input Variables

More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs.

scholarly journals Artificial Neural Network (ANN) Modeling for Prediction of Pesticide Wastewater Degradation by FeGAC/H2O2 Process

2018 ◽  
Vol 65 ◽  
pp. 05004
Author(s):  
Augustine Chioma Affam ◽  
Malay Chaudhuri ◽  
Chee Chung Wong ◽  
Chee Swee Wong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Close Agreement ◽  
Transfer Functions ◽  
Cod Removal ◽  
Experimental Results ◽  
Ann Model ◽  
Ann Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The study examined artificial neural network (ANN) modeling for the prediction of chlorpyrifos, cypermethrin and chlorothalonil pesticides degradation by the FeGAC/H2O2 process. The operating condition was the optimum condition from a series of experiments. Under these conditions; FeGAC 5 g/L, H2O2 concentration 100 mg/L, pH 3 and 60 min reaction time, the COD removal obtained was 96.19%. The ANN model was developed using a three-layer multilayer perceptron (MLP) neural network to predict pesticide degradation in terms of COD removal. The configuration of the model with the smallest mean square error (MSE) of 0.000046 contained 5 inputs, 9 hidden and, 1 output neuron. The Levenberg–Marquardt backpropagation training algorithm was used for training the network, while tangent sigmoid and linear transfer functions were used at the hidden and output neurons, respectively. The predicted results were in close agreement with the experimental results with correlation coefficient (R2) of 0.9994 i.e. 99.94% showing a close agreement to the actual experimental results. The sensitivity analysis showed that FeGAC dose had the highest influence with relative importance of 25.33%. The results show how robust the ANN model could be in the prediction of the behavior of the FeGAC/H2O2 process.

Sugarcane Juice Clarification by Hydrogen Peroxide: Predictions with Artificial Neural Networks

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Juliana Aparecida de Souza Sartori ◽  
Katia Ribeiro ◽  
Antonio Carlos Silva Costa Teixeira ◽  
Nathalia Torres Correa Magri ◽  
Juliana Lorenz Mandro ◽  
...  
Keyword(s):  
Hydrogen Peroxide ◽  
Sugar Industry ◽  
Allergic Diseases ◽  
Sugarcane Juice ◽  
Effect Of Temperature ◽  
Sucrose Content ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Ph Range

Abstract: Hydrogen peroxide has been studied as an alternative for sulfur in the white sugar industry. Sulfur has been associated to allergic diseases, mainly asthma. In this study, artificial neural network (ANN) models are proposed to predict the effects of different variables (peroxidation time, temperature, pH, H2O2 dosage, and initial °Brix) on sugarcane juice color removal and sucrose content. Experimental results and the ANN models revealed that temperature showed the greatest influence on the decrease of juice color; nevertheless, the effect of temperature depended on pH: at pH<;5.0 a decrease in juice absorbance was observed at temperatures close to 38 °C, whereas in the pH range of 5.0–6.3, absorbance decreased only at about 50–62 °C, regardless of the amount of hydrogen peroxide used. On the other hand, the remaining sucrose content after peroxidation was influenced by the initial °Brix and by pH.

scholarly journals Artificial Neural Network (ANN) modeling of the pulsed heat load during ITER CS magnet operation

Cryogenics ◽  
2014 ◽  
Vol 63 ◽  
pp. 231-240 ◽  
Author(s):  
L. Savoldi Richard ◽  
R. Bonifetto ◽  
S. Carli ◽  
A. Froio ◽  
A. Foussat ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Load ◽  
Ann Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals PREDIKSI KINERJA MESIN DIESEL DENGAN BAHAN BAKAR BIODIESEL-SOLAR MENGGUNAKAN ARTIFICIAL NEURAL NETWORK

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Husin Ibrahim ◽  
Abdi Hanra Sebayang ◽  
S. Dharma ◽  
A.S. Silitonga
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Root Mean Square ◽  
Mean Square ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tulisan ini meneliti kinerja mesin diesel satu silinder menggunakan campuran bahan bakar biodiesel randu dengan solar. Tes dilakukan dengan berbagai perbandingan biodiesel-diesel (B10, B20 dan B30). Sebuah model artificial neural network (ANN) yang didasarkan pada algoritma back-propagasi standar digunakan untuk memprediksi kinerja mesin menggunakan MATLAB. Untuk memperoleh data untuk pelatihan dan pengujian yang diusulkan ANN, kecepatan mesin yang berbeda (1400-2200 rpm) dipilih sebagai parameter masukan, sedangkan kinerja mesin (BSFC dan BTE) dipilih sebagai parameter keluaran untuk ANN pemodelan dari mesin diesel. Kinerja mesin (BSFC dan BTE) ANN telah divalidasi dengan membandingkan hasil prediksi dengan hasil eksperimen. Hasil penelitian menunjukkan bahwa koefisien korelasi BSFC dan BTE masing masing adalah 0,99249 dan 0,99457. Nilai MAPE (mean absolute persentase kesalahan) BSFC dan BTE adalah 0,57467 dan 0,33424 dan root mean square (RSME) nilai di bawah 5% oleh model, yang diterima. Studi ini menunjukkan bahwa pemodelan teknik sebagai pendekatan dalam energi alternatif dapat memberikan peningkatan keuntungan dari kehandalan dalam prediksi kinerja mesin pembakaran dalam. 

Sign in / Sign up

Export Citation Format

Share Document