Modeling to predict thermal aging for flame-retardant fabrics considering thermal stability under fire exposure

The performance of firefighters’ clothing will deteriorate due to various exposures. Predicting its service life before decommissioning is essential to guide the use and maintenance of the uniform. The aim of this study is to introduce a model to predict the tensile strength of flame-retardant fabrics under fire exposure. The thermal degradation and microstructure of Kevlar/polybenzimidazole and polyimide/Kevlar fabrics were investigated. The decrease of tensile strength was attributed to the chemical changes and the development of microstructure cracks and charring of the fibers. Multiple linear regression (MLR) and artificial neural network (ANN) models were established to predict the tensile strength after thermal aging. The ANN model presented a better prediction result ( R2 = 0.88, root mean square error (RMSE) = 96.91) than the MLR method ( R2 = 0.76, RMSE = 138.61). The addition of fabric backside temperature ( T), glass transition temperature ( Tg), and degradation temperature ( Td) further increased the R2 (4%) and decreased the RMSE (14.99) of the ANN model, which was recommended as a prediction approach with better accuracy. The findings of this study will contribute to estimating the continuous performance of firefighting clothing.

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Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

Algerian Journal of Renewable Energy and Sustainable Development ◽

10.46657/ajresd.2021.3.1.5 ◽

2021 ◽

Vol 03 (01) ◽

pp. 45-52

Author(s):

Hadjira Maouz ◽

◽

Asma Adda ◽

Salah Hanini ◽

◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Thermal Aging ◽

Training Data ◽

Ethylene Propylene Diene Monomer ◽

Ann Model ◽

Ethylene Propylene ◽

Artificial Neural ◽

Input Variables ◽

Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

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Prediction of the optimal FSW process parameters for joints using machine learning techniques

Materials Testing ◽

10.1515/mt-2021-0058 ◽

2021 ◽

Vol 63 (12) ◽

pp. 1104-1111

Author(s):

Furkan Sarsilmaz ◽

Gürkan Kavuran

Keyword(s):

Mechanical Properties ◽

Tensile Strength ◽

Machine Learning Techniques ◽

Support Vector ◽

Ann Model ◽

Friction Stir ◽

Learning Techniques ◽

Svm Model ◽

Artificial Neural Network Ann ◽

Fsw Parameters

Abstract In this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical properties such as ultimate tensile strength (UTS) and hardness of welded joints were determined experimentally. Secondly, these data were evaluated through modeling and the optimization of the FSW process as well as an optimal parametric combination to affirm tensile strength and hardness using a support vector machine (SVM) and an artificial neural network (ANN). In this study, a new ANN model, including the Nelder-Mead algorithm, was first used and compared with the SVM model in the FSW process. It was concluded that the ANN approach works better than SVM techniques. The validity and accuracy of the proposed method were proved by simulation studies.

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A new nonlinear formulation-based prediction approach using artificial neural network (ANN) model for rubberized cement composite

Engineering With Computers ◽

10.1007/s00366-020-01054-3 ◽

2020 ◽

Cited By ~ 1

Author(s):

Mostafa Jalal ◽

Zachary Grasley ◽

Charles Gurganus ◽

Jeffrey W. Bullard

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Cement Composite ◽

Ann Model ◽

Nonlinear Formulation ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Prediction Approach

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Development of Neural Network Models for a Crude Oil Distillation Column

Jurnal Teknologi ◽

10.11113/jt.v38.509 ◽

2012 ◽

Author(s):

Khairiyah Mohd. Yusof ◽

Fakhri Karray ◽

Peter L. Douglas

Keyword(s):

Neural Network ◽

Steady State ◽

Crude Oil ◽

Distillation Column ◽

Network Models ◽

Ann Model ◽

Neural Network Models ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

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Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

10.4018/978-1-6684-2408-7.ch062 ◽

2022 ◽

pp. 1287-1300

Author(s):

Balaji Prabhu B. V. ◽

M. Dakshayini

Keyword(s):

Neural Network ◽

Crop Production ◽

Demand Forecasting ◽

Forecasting Model ◽

Ann Model ◽

Demand Prediction ◽

Computational Performance ◽

Societal Needs ◽

Ann Models ◽

Artificial Neural Network Ann

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

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Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2020100103 ◽

2020 ◽

Vol 12 (4) ◽

pp. 35-47

Author(s):

Balaji Prabhu B. V. ◽

M. Dakshayini

Keyword(s):

Neural Network ◽

Crop Production ◽

Demand Forecasting ◽

Ann Model ◽

Demand Prediction ◽

Computational Performance ◽

Societal Needs ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Agricultural Food

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Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

International Journal of Polymer Science ◽

10.1155/2016/5340252 ◽

2016 ◽

Vol 2016 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

P. Noorunnisa Khanam ◽

MA AlMaadeed ◽

Sumaaya AlMaadeed ◽

Suchithra Kunhoth ◽

M. Ouederni ◽

...

Keyword(s):

Tensile Strength ◽

Chemical Properties ◽

Stress Transfer ◽

Graphene Nanoplatelets ◽

Mechanical And Thermal Properties ◽

Degradation Temperature ◽

The Matrix ◽

Twin Screw ◽

Artificial Neural ◽

Artificial Neural Network Ann

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.

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Evaluation and ANN-Based Prediction on Functional Parameters of Surface Roughness in Precision Grinding of Cast Iron

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1017.166 ◽

2014 ◽

Vol 1017 ◽

pp. 166-171

Author(s):

Bin Zhao ◽

Song Zhang ◽

Jian Feng Li

Keyword(s):

Surface Roughness ◽

Cast Iron ◽

Three Dimensional ◽

Ann Model ◽

Linear Speed ◽

Functional Parameters ◽

Grinding Parameters ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Dimensional Surface

Three-dimensional surface roughness parameters are widely applied to characterize frictional and lubricating properties, corrosion resistance, fatigue strength of surfaces. Among them, the functional parameters of surface roughness, such as Sbi, Sci, and Svi, are used to evaluate bearing and fluid retention properties of surfaces. In this study, the effects of grinding parameters, including wheel linear speed (Vs), workpiece linear speed (Vw), grinding depth (ap), longitudinal feed rate (fa), and dressing rate (F), on functional parameters were studied in grinding of cast iron. An artificial neural network (ANN) model was developed for predicting the functional parameters of three-dimensional surface roughness. The inputs of the ANN models were grinding parameters (Vs, Vw, ap, fa, F), and the output parameters of the models were functional parameters of surface roughness (Sbi, Sci, Svi). With small errors (e.g MSE = 0.09%, 0.61%, and 0.0014%. ), the ANN-based models are considered sufficiently accurate to predict functional parameters of surface roughness in grinding of cast iron.

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Neural Network MIMO Model for Production of Isopropyl Myristate in a Semibatch Reactive Distillation Column

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.403 ◽

2013 ◽

Vol 284-287 ◽

pp. 403-408

Author(s):

Nur Alwani Ali Bashah ◽

Mohd Roslee Othman ◽

Norashid Aziz

Keyword(s):

Neural Network ◽

Reactive Distillation ◽

Batch Reactor ◽

Isopropyl Myristate ◽

Ann Model ◽

Validation Data ◽

Testing Data ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Mimo Model

Batch reactive distillation is an integrated unit of batch reactor and distillation. It provides benefits of having higher conversion and yield by continuous removal of side product. The aim of this paper is to develop an artificial neural network (ANN) based model for production of isopropyl myristate in an industrial scaled semibatch reactive distillation. Two cases of the MIMO model were developed. Case 1 does not consider historical data as inputs while case 2 does. The trained ANN for both cases was validated with independent validation data and the best architecture was optimized. Case 1 resulted to 8 inputs, 14 hidden nodes and 2 outputs [8-14-2] ANN while Case 2 resulted to [12-13-2] ANN. The results show that both ANN models have ability to predict the unknown validation and testing data very well. However, the [8-14-2] ANN model produce higher accuracy than [12-13-2] ANN model with MSE of 0.00094 and 0.0013, respectively.

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Modeling of hourly river water temperatures using artificial neural networks

Water Quality Research Journal ◽

10.2166/wqrjc.2014.007 ◽

2014 ◽

Vol 49 (2) ◽

pp. 144-162 ◽

Cited By ~ 1

Author(s):

Cindie Hebert ◽

Daniel Caissie ◽

Mysore G. Satish ◽

Nassir El-Jabi

Keyword(s):

Water Temperature ◽

Coefficient Of Determination ◽

Ann Model ◽

Air Temperatures ◽

Level Data ◽

Water Level Data ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

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