scholarly journals Pyrolysis of Mixed Plastic Waste: II. Artificial Neural Networks Prediction and Sensitivity Analysis

2021 ◽  
Vol 11 (18) ◽  
pp. 8456
Author(s):  
Ibrahim Dubdub ◽  
Mohammed Al-Yaari
Keyword(s):  
Sensitivity Analysis ◽  
Transfer Functions ◽  
Pearson Correlation ◽  
Input Parameter ◽  
Propagation Model ◽  
Ann Model ◽  
Polymer Mixtures ◽  
Feed Forward Back Propagation ◽  
Proposed Model ◽  
Artificial Neural

In this work, an artificial neural network (ANN) model was efficiently developed to predict the pyrolysis of mixed plastics, including pure polystyrene (PS), polypropylene (PP), low-density polyethylene (LDPE), and high-density polyethylene (HDPE), at a heating rate of 60 K/min using thermogravimetric analysis (TGA) data. The data of seventeen experimental tests of polymer mixtures with different compositions were used. A feed-forward back-propagation model, with 15 and 10 neurons in two hidden layers and TANSIG-TANSIG transfer functions, was constructed to predict the weight left percent during the pyrolysis of the mixed polymer samples. The model input variables include the composition of each polymer (PS, PP, LDPE, and HDPE), and temperature. The results showed an excellent agreement between the experimental and the predicted weight left percent values, where the correlation coefficient (R) is greater than 0.9999. In addition, to validate the proposed model, a highly efficient performance was found when the proposed model was simulated using new input data. Furthermore, a sensitivity analysis was performed using Pearson correlation to find the uncertainties associated with the relationship between the output and the input parameters. Temperature was found to be the most sensitive input parameter.

scholarly journals Predicting fire resistance ratings of timber structures using artificial neural networks

2020 ◽  
Vol 14 (2) ◽  
pp. 28-39
Author(s):  
Pham Thanh Tung ◽  
Pham Thanh Hung
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Fire Resistance ◽  
Input Parameter ◽  
Training Algorithms ◽  
Ann Model ◽  
Wooden Floor ◽  
Artificial Neural ◽  
Input Variables

This paper describes a method to predict the fire resistance ratings of the wooden floor assemblies using Artificial Neural Networks. Experimental data collected from the previously published reports were used to train, validate, and test the proposed ANN model. A series of model configurations were examined using different popular training algorithms to obtain the optimal structure for the model. It is shown that the proposed ANN model can successfully predict the fire resistance ratings of the wooden floor assemblies from the input variables with an average absolute error of four percent. Besides, the sensitivity analysis was conducted to explore the effects of the separate input parameter on the output. Results from analysis revealed that the fire resistance ratings are sensitive to the change of Applied Load (ALD) and the number of the Ceiling Finish Layer (CFL) input variables. On the other hand, the outputs are less sensitive to a variation of the Joist Type (JTY) parameter. Keywords: artificial neural networks; fire resistance; wooden floor assembly; sensitivity analysis.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

scholarly journals Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach

2020 ◽  
Vol 48 (1) ◽  
pp. 366-377 ◽  
Author(s):  
Yeşim Benal ÖZTEKİN ◽  
Alper TANER ◽  
Hüseyin DURAN
Keyword(s):  
Neural Networks ◽  
Transfer Functions ◽  
Geometric Mean ◽  
Back Propagation ◽  
Castanea Sativa ◽  
Average Error ◽  
Ann Model ◽  
Neural Network Approach ◽  
Castanea Sativa Mill ◽  
Artificial Neural

The present study investigated the possible use of artificial neural networks (ANN) to classify five chestnut (Castanea sativa Mill.) varieties. For chestnut classification, back-propagation neural networks were framed on the basis of physical and mechanical parameters. Seven physical and mechanical characteristics (geometric mean diameter, sphericity, volume of nut, surface area, shell thickness, shearing force and strength) of chestnut were determined. It was found that these characteristics were statistically different and could be used in the classification of species. In the developed ANN model, the design of the network is 7-(5-6)-1 and it consists of 7 input, 2 hidden and 1 output layers. Tansig transfer functions were used in both hidden layers, while linear transfer functions were used in the output layer. In ANN model, R2 value was obtained as 0.99999 and RMSE value was obtained as 0.000083 for training. For testing, R2 value was found as 0.99999 and RMSE value was found as 0.00031. In the approximation of values obtained with ANN model to the values measured, average error was found as 0.011%. It was found that the results found with ANN model were very compatible with the measured data. It was found that the ANN model obtained can classify chestnut varieties in a fast and reliable way.

scholarly journals An artificial neural network model for rainfall forecasting in Bangkok, Thailand

2009 ◽  
Vol 13 (8) ◽  
pp. 1413-1425 ◽  
Author(s):  
N. Q. Hung ◽  
M. S. Babel ◽  
S. Weesakul ◽  
N. K. Tripathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Input Parameter ◽  
Rain Gauge ◽  
Ann Model ◽  
Rainfall Forecasting ◽  
Forecast Performance ◽  
Hourly Data ◽  
Artificial Neural

Abstract. This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

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.

Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network

2017 ◽  
Vol 68 (11) ◽  
pp. 2070 ◽  
Author(s):  
Manh-Ha Bui ◽  
Thanh-Luu Pham ◽  
Thanh-Son Dao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Transfer Functions ◽  
Learning Algorithm ◽  
Oxygen Demand ◽  
Cyanobacterial Blooms ◽  
Cyanobacteria Bloom ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

An artificial neural network (ANN) model was used to predict the cyanobacteria bloom in the Dau Tieng Reservoir, Vietnam. Eight environmental parameters (pH, dissolved oxygen, temperature, total dissolved solids, total nitrogen (TN), total phosphorus, biochemical oxygen demand and chemical oxygen demand) were introduced as inputs, whereas the cell density of three cyanobacteria genera (Anabaena, Microcystis and Oscillatoria) with microcystin concentrations were introduced as outputs of the three-layer feed-forward back-propagation ANN. Eighty networks covering all combinations of four learning algorithms (Bayesian regularisation (BR), gradient descent with momentum and adaptive learning rate, Levenberg–Mardquart, scaled conjugate gradient) with two transfer functions (tansig, logsig) and 10 numbers of hidden neurons (6–16) were trained and validated to find the best configuration fitting the observed data. The result is a network using the BR learning algorithm, tansig transfer function and nine neurons in the hidden layer, which shows satisfactory predictions with the low values of error (root mean square error=0.108) and high correlation coefficient values (R=0.904) between experimental and predicted values. Sensitivity analysis on the developed ANN indicated that TN and temperature had the most positive and negative effects respectively on microcystin concentrations. These results indicate that ANN modelling can effectively predict the behaviour of the cyanobacteria bloom process.

scholarly journals Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time

2013 ◽  
Vol 14 (1) ◽  
pp. 10-17
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Potable Water ◽  
Back Propagation ◽  
Ann Model ◽  
Data Set ◽  
Test Set ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Comparison Of The Results

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration (expressed as carbon and bromine) with an ANN model, i.e., capturing non-linear relationships among the water quality variables. Thus, carbon and bromine concentrations in potable water (the second chosen due to the toxicity of brominated trihalomethanes, THMs) were predicted using artificial neural networks (ANNs) based mainly on multi-layer perceptrons (MLPs) architecture. The chlorination (detention) time as much as 58 hours in Athens distributed network, comprised the input variables to the ANNs models. Moreover, to develop an ANN model for estimating carbon and bromine, the available data set was partitioned into training, validation and test set. In order to reach an optimum amount of hidden layers or nodes, different architectures were tested. The quality of the ANN simulations was evaluated in terms of the error in the validation sample set for the proper interpretation of the results. The calculated sum-squared errors for training, validation and test set were 0.056, 0.039 and 0.060 respectively for the best model selected. Comparison of the results showed that a two-layer feed-forward back propagation ANN model could be used as an acceptable model for predicting carbon and bromine contained in potable water THMs.

scholarly journals Development of Artificial Neural Network for Predicting The Photodegradation of Reactive Black 5 Dye

Jurnal IPTEK ◽  
2019 ◽  
Vol 23 (2) ◽  
pp. 101-116
Author(s):  
Dika Rahayu Widiana ◽  
Ryan Yudha Adhitya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Titanium Dioxide ◽  
Irradiation Time ◽  
Input Parameter ◽  
Reactive Black 5 ◽  
Impregnation Method ◽  
Ann Model ◽  
Doped Titanium Dioxide ◽  
Artificial Neural

We applied a multilayer artificial neural network (ANN) developed using a Lavenberg–Marquadt algorithm to predict the photodegradation activity of the Reactive Black 5 (RB5) dye. A copper-doped titanium dioxide was employed as a photocatalyst. A copper doped titanium dioxide was synthesized through a wet-impregnation method. To optimize the network the operational parameters including the RB5 initial concentration, photocatalyst dose, irradiation time, hydrogen peroxide concentration, and visible light intensity were used as the input parameter. Removal efficiency of RB5 was selected as output. The number of neurons in the second hidden layer was optimized to determine the suitable ANN model structure for the RB5 removal. ANN based through Levenberg-Marquadth algorithm with structure 1-10-21-1 gave the best performance in this study. The criteria for the applicability of the model were the root mean square error (0.1) and coefficient of correlation (0.98275).

scholarly journals An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN

2019 ◽  
Vol 2 (1) ◽  
pp. 10 ◽  
Author(s):  
Salaheddin Hosseinzadeh ◽  
Hadi Larijani ◽  
Krystyna Curtis ◽  
Andrew Wixted
Keyword(s):  
Signal Propagation ◽  
Propagation Model ◽  
Basic Knowledge ◽  
Training Dataset ◽  
Accurate Estimation ◽  
Optimum Number ◽  
Ann Model ◽  
Deterministic Models ◽  
Inference System ◽  
Proposed Model

This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios.

Predicting the effective thermal conductivity of unfrozen soils with various water contents based on artificial neural network

2021 ◽  
Author(s):  
Chuan-Yong Zhu ◽  
Zhi-Yang He ◽  
Mu Du ◽  
Liang Gong ◽  
Xinyu Wang
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Water Content ◽  
Effective Thermal Conductivity ◽  
Pearson Correlation ◽  
Complex Structure ◽  
Ann Model ◽  
Input Parameters ◽  
Artificial Neural

Abstract The effective thermal conductivity of soils is a crucial parameter for many applications such as geothermal engineering, environmental science, and agriculture and engineering. However, it is pretty challenging to accurately determine it due to soils’ complex structure and components. In the present study, the influences of different parameters, including silt content (m si), sand content (m sa), clay content (m cl), quartz content (m qu), porosity, and water content on the effective thermal conductivity of soils, were firstly analyzed by the Pearson correlation coefficient. Then different artificial neural network (ANN) models were developed based on the 465 groups of thermal conductivity of unfrozen soils collected from the literature to predict the effective thermal conductivity of soils. Results reveal that the parameters of m si, m sa, m cl, and m qu have a relatively slight influence on the effective thermal conductivity of soils compared to the water content and porosity. Although the ANN model with six parameters has the highest accuracy, the ANN model with two input parameters (porosity and water content) could predict the effective thermal conductivity well with acceptable accuracy and R 2=0.940. Finally, a correlation of the effective thermal conductivity for different soils was proposed based on the large number of results predicted by the two input parameters ANN-based model. This correlation has proved to have a higher accuracy without assumptions and uncertain parameters when compared to several commonly used existing models.

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