Feed-forward Back-propagation Artificial Neural Network Research Articles

Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network.

The precise experimental estimation of mechanical properties of rubber blends can be a very costly and time-consuming process. The present work explores the possibilities of increasing its efficiency by using artificial neural networks to study the mechanical behavior of these widely used materials. A multilayer feed-forward back-propagation artificial neural network model, with a strain and the carbon black content as input parameters and stress as an output parameter, has been developed to predict the uniaxial tensile response of vulcanized natural rubber blends with different contents of carbon black in the form of engineering stress-strain curves. A novel procedure has been created for the simulation of the optimized artificial neural network model with input datasets generated by a regression model of an experimental dependence of tensile strain-at-break on the carbon black content in the investigated blends. Errors of the prediction of experimental stress-strain curves, as well as of tensile strain-at-break, tensile stress-at-break and M100 tensile modulus were estimated for all simulated stress-strain curves. The present study demonstrated that the performance of a developed neural network model to predict the stress-strain curves of rubber blends with different contents of carbon black is also exceptionally high in the case of a network that had never learned the input data, which makes it a suitable tool for extensive use in practice.

Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network (ANN) model

Abstract The aim of the present study was to investigate the modeling and prediction of the high temperature flow characteristics of a cast magnesium (Mg–Al–Ca) alloy by both constitutive equation and ANN model. Toward this end, hot compression experiments were performed in 250–450 °C and in strain rates of 0.001–1 s−1. The true stress of alloy was first and foremost described by the hyperbolic sine function in an Arrhenius-type of constitutive equation taking the effects of strain, strain rate and temperature into account. Predictions indicated that unlike low strain rates and high temperature with dominant DRX activation, in relatively high strain rate and low temperature values, the precision of the models become decreased due to activation of twinning phenomenon. At that moment and for a better evaluation of twinning effect during deformation, a feed-forward back propagation ANN was developed to study the flow behavior of the investigated alloy. Then, the performance of the two suggested models has been assessed using a statistical criterion. The comparative assessment of the gained results specifies that the well-trained ANN is much more precise and accurate than the constitutive equations in predicting the hot flow behavior.

Translaminar neuromorphotopological clustering and classification of dentate nucleus neurons.

Thisstudy aims to determine whether dentate neurons can be translaminarlyneuromorphotopologically classified as ventrolateral or dorsomedial type. Adulthuman dentate interneuron 2D binary images are analyzed. The analysis isperformed on both real and virtual neuron samples and 29 parameters are used.They are divided into the classes: neuron surface, shape, length, branching andcomplexity. Clustering is performed by an algorithm that employs predictor extraction (matrix attractor analysis/non-negative matrix factorization and cluster analysis of predictor factors - separate unifactor analysis/Student’s t-test and MANOVA) and multivariate cluster analysis (cluster analysis, principal component analysis, factor analysis with pro/varimax rotation, Fisher’s linear discriminant analysis and feed-forward backpropagation artificial neural networks). The separate unifactor analysis extracted as significant the following predictors from the natural cell sample: the Npd (p< 0:05), and from the virtual cell sample: the Adt (p< 0.05),Do (p< 0.001), Ms (p< 0.01), Dwdth (p< 0:001), Npd (p< 0:05), Nsd (p< 0.001), Nt/hod (p< 0.001), Nmax (p< 0.01), Ds (p< 0.001), Cdf (Nt/hod)st (p< 0.05). For the multidimensional analysis, with the exception of the Fisher’s linear discriminant analysis which gave a false positive result, all other analyses rejected the translaminar dentate neuron classification. Thus, dentate neurons cannot be classified into ventrolateral/dorsomedial neuromorphotopological subtypes. Although some differences were found to exist, they are not sufficient to carry this classification. The methods of multidimensional statistical analysis are again shown to be the best for such kinds of analysis.

Artificial neural network modelling for organic and total nitrogen removal of aerobic granulation under steady-state condition

ABSTRACTAerobic granulation is a recent technology with high level of complexity and sensitivity to environmental and operational conditions. Artificial neural networks (ANNs), computational tools capable of describing complex non-linear systems, are the best fit to simulate aerobic granular bioreactors. In this study, two feedforward backpropagation ANN models were developed to predict chemical oxygen demand (Model I) and total nitrogen removal efficiencies (Model II) of aerobic granulation technology under steady-state condition. Fundamentals of ANN models and the steps to create them were briefly reviewed. The models were respectively fed with 205 and 136 data points collected from laboratory-, pilot-, and full-scale studies on aerobic granulation technology reported in the literature. Initially, 60%, 20%, and 20%, and 80%, 10%, and 10% of the points in the corresponding datasets were randomly chosen and used for training, testing, and validation of Model I, and Model II, respectively. Overall coefficient of determination (R2) value and mean squared error (MSE) of the two models were initially 0.49 and 15.5, and 0.37 and 408, respectively. To improve the model performance, two data division methods were used. While one method is generic and potentially applicable to other fields, the other can only be applied to modelling the performance of aerobic granular reactors. R2 value and MSE were improved to 0.90 and 2.54, and 0.81 and 121.56, respectively, after applying the new data division methods. The results demonstrated that ANN-based models were capable simulation approach to predict a complicated process like aerobic granulation.

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

An experiment on reinforced concrete beams using four-point bending test during an ultrasonic test was conducted. Three beam specimens were considered for each water/cement ratio (WC) of 40% and 60%, with three reinforcement schedules named design A (comprising two top bars and two bottom bars), design B (with two bottom bars), and design C (with one bottom bar). The concrete beam had a size of 100 mm × 100 mm × 400 mm in length with a plain reinforcement bar of 9 mm in diameter. An ultrasonic test with pitch–catch configuration was conducted at each loading with the transducers oriented in direct transmission across the beams' length with recordings of 68 datasets per beam specimen. Recordings of ultrasonic test results and strains at the top and bottom surfaces subjected to multiple step loads in the experiment were done. After the collection of the data, feed-forward backpropagation artificial neural network (ANN) was used to investigate the sensitivity of the ultrasonic parameters to the mechanical load applied. Five input parameters were examined, as follows: neutral axis (NA), fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), and peak-to-peak amplitude (PPA), while the output parameter was the percentage of ultimate load. Optimum models were chosen after training, validating, and testing 60 ANN models. The optimum model was chosen on the basis of the highest Pearson’s Correlation Coefficient (R) and soundness, confirming that it exhibited good behavior in agreement with theories. A classification of sensitivity was performed using simulations based on the developed optimum models. It was found that A2 and NA were sensitive to all WC and reinforcements used in the ANN simulation. In addition, the range of sensitivity of A2 and NA was inversely and directly proportional to the reinforcing bars, respectively. This study can be used as a guide in the selection of ultrasonic parameters to assess damage in concrete with low or high WC and varying reinforcement content.

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater

In this a, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal an upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the end of UASB operation, microbial community characterization revealed satisfactory composition of microbes whereas morphology depicted rod-shaped archaea. pH, COD, NH4+, VFA, OLR and biogas yield were selected by principal component analysis and used as input variables. Whilst tangent sigmoid function (tansig) and linear function (purelin) were assigned as activation functions at the hidden-layer and output-layer, respectively, optimum BPANN architecture was achieved with Levenberg-Marquardt algorithm (trainlm) after eleven training algorithms had been tested. Based on performance indicators such the mean squared errors, fractional variance, index of agreement and coefficient of determination (R2), the BPANN model demonstrated significant performance with R2 reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible.

Development of a new esomeprazole delayed release gastro-resistant pellet formulation with improved storage stability

This study describes the development of a new esomeprazole (ESO) delayed release gastro-resistant formulation with improved storage stability. A three-step (drug-, sub(seal)- and enteric-) coating process was employed with the aid of a fluid bed coater. Several formulation factors (namely, size and quantity of starting non-pareil sugar spheres, binder quantity during drug-layering, sub(seal)-coating polymer type, and quantity and enteric coating quantity) were evaluated and the whole process was modeled with the aid of feed-forward back-propagation artificial neural networks (ANNs). Results showed that the selection of small-sized starting spheres (45/60 mesh size) leads to pellet agglomeration, while as sub(seal)-coating weight gain increases a reduction in ESO dissolution rate is observed. The enteric-coating applied (Eudragit L30D-55) showed good gastro-resistant performance in both 0.1 N HCl and pH 4.5 media, while immediate release profiles with more than 85% of ESO being released in less than 30 min were obtained. The effect of cellulose-based sub(seal)-coating polymers, (namely, hydroxypropyl cellulose and hydroxypropylmethyl cellulose) on formulation’s storage stability at 40 ± 2 °C/75 ± 5%RH indicated that only hydroxypropylmethyl cellulose was able to stabilize ESO delayed-release formulations in terms of assay, dissolution, impurities, and gastro-resistance performance. Finally, scanning electron microscopy (SEM) analysis revealed smooth and homogeneous external surface/coating layers in all three levels (drug-, sub(seal)-, and enteric- coating), while x-ray diffraction showed no polymorphic transformations.

Bridging Communication Gap Among People with Hearing Impairment: An Application of Image Processing and Artificial Neural Network

Before the present study, no sign language recognition system for the Nigeria indigenous sign language particularly Yoruba language has been developed. As a result, this research endeavors at introducing a Yoruba Sign Language recognition system using image processing and Artificial Neural Network (ANN).The proposed system (YSLRS) was implemented and tested. 600 images from 60 different signers were gathered. The images were acquired using vision based method, the different signers were asked to stand in front of a laptop’s camera make sign number from one to ten with their fingers in three different times and the images were stored in a folder. The image dataset was pre-processed for proper presentation for de-noising, segmentation and feature extraction. Thereafter, pattern recognition was done using feed forward back propagation ANN. The study revealed that Median filter with higher PSNR of 47.7 a lower MSE of 1.11, performed better than the Gaussian filter. Furthermore, the efficiency of the developed system was determined using mean square error and the best validation performance occurred at 25 epochs with a MSE of 0.004052, implying than ANN was able to adequately recognize the pattern of the Yoruba signs. Histogram was also used to determine the efficiency of the system, it can be seen that the histogram of the trained, tested and validated error bars were close to zero error, implying that the ANN and Receiver Operating Characteristic (ROC) was used to evaluate the performance of ANN in matching the features of the Yoruba Signs, which shows that ANN performed efficiently, having a high true positive rate and a minimum false positive rate. Finally, YSLRS developed in the study would reduce negative attitudes of victimizations suffered by the hearing-impaired individuals, by bridging communication gap among Nigerian PWD with hearing impairment.

Creep analysis of bimaterial microcantilever beam for sensing device using artificial neural network (ANN)

In this study, a feed-forward back-propagation Artificial Neural Network (ANN) is used to predict the stress relaxation and behavior of creep for bimaterial microcantilever beam for sensing device. Results obtained from ANSYS® 8.1 finite element (FE) simulations, which show good agreement with experimental work [1], is used to train the neural network. Parametric studies are carried out to analyze the effects of creep on the microcantilever beam in term of curvature and stress deve loped with time. It is shown that ANN accurately predicts the stress level for the microcantilever beam using the trained ANSYS® simulation results due to the fact that there is no scattered data in the FE simulation results. ANN takes a small fraction of time and effort compar ed to FE prediction.

Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network.

This paper presents one of the soft computing methods, specifically the artificial neural network technique, that has been used to model the temperature dependence of dynamic mechanical properties and visco-elastic behavior of widely exploited thermoplastic polyurethane over the wide range of temperatures. It is very complex and commonly a highly non-linear problem with no easy analytical methods to predict them directly and accurately in practice. Variations of the storage modulus, loss modulus, and the damping factor with temperature were obtained from the dynamic mechanical analysis tests across transition temperatures at constant single frequency of dynamic mechanical loading. Based on dynamic mechanical analysis experiments, temperature dependent values of both dynamic moduli and damping factor were calculated by three models of well-trained multi-layer feed-forward back-propagation artificial neural network. The excellent agreement between the modeled and experimental data has been found over the entire investigated temperature interval, including all of the observed relaxation transitions. The multi-layer feed-forward back-propagation artificial neural network has been confirmed to be a very effective artificial intelligence tool for the modeling of dynamic mechanical properties and for the prediction of visco-elastic behavior of tested thermoplastic polyurethane in the whole temperature range of its service life.

Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks.

In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A life cycle assessment (LCA) approach was used to investigate the environmental impact categories of processed tea based on the cradle to gate approach, i.e., from production of input materials using raw materials to the gate of tea processing units, i.e., packaged tea. Thus, all the tea processing operations such as withering, rolling, fermentation, drying, and packaging were considered in the analysis. The initial data were obtained from tea processing units while the required data about the background system was extracted from the EcoInvent 2.2 database. LCA results indicated that diesel fuel and corrugated paper box used in drying and packaging operations, respectively, were the main hotspots. Black tea processing unit caused the highest pollution among the three processing units. Three feed-forward back-propagation ANN models based on Levenberg-Marquardt training algorithm with two hidden layers accompanied by sigmoid activation functions and a linear transfer function in output layer, were applied for three types of processed tea. The neural networks were developed based on energy equivalents of eight different input parameters (energy equivalents of fresh tea leaves, human labor, diesel fuel, electricity, adhesive, carton, corrugated paper box, and transportation) and 11 output parameters (yield, global warming, abiotic depletion, acidification, eutrophication, ozone layer depletion, human toxicity, freshwater aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, and photochemical oxidation). The results showed that the developed ANN models with R 2 values in the range of 0.878 to 0.990 had excellent performance in predicting all the output variables based on inputs. Energy consumption for processing of green tea, oolong tea, and black tea were calculated as 58,182, 60,947, and 66,301MJ per ton of dry tea, respectively.

An artificial neural network to predict resting energy expenditure in obesity

The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity. A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30-50kgm-2) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation. Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision=68.1%, MAPE=8.6% and RMSPE=210kcal/d), independently from BMI subgroups within 30-50kgm-2. External validation confirmed the better predictive performances of ANN model (precision=73% and 65%, MAPE=7.7% and 8.6%, RMSPE=187kcal/d and 200kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects. We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive equations independent from BMI subgroups. For convenient use in clinical settings, we provide a simple ANN-REE calculator available at: https://www.crnh-rhone-alpes.fr/fr/ANN-REE-Calculator.

Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks

ABSTRACTIn galvanising line of cold rolling mill, mechanical properties, i.e. yield strength (YS) and ultimate tensile strength (UTS), are achieved by controlling the key process parameters within specified limits. In this paper, a feed-forward back-propagation artificial neural network (ANN) is proposed to predict the mechanical properties of a coil from its chemical composition, thickness, width and key galvanising process parameters. Principal component analysis is used to avoid redundancy and collinearity effects in input variables for the ANN. The model predicted the YS and UTS with an accuracy of ±10 megapascal (MPa) for 90% of the data. The model was implemented in the continuous galvanising line of Tata Steel, India. An online quality monitoring system was developed to monitor the predicted mechanical properties and process parameters of a galvanised coil. This system helps quality team in decision making.

Practical modeling and optimization of ultrasound-assisted bleaching of olive oil using hybrid artificial neural network-genetic algorithm technique

Multi-objective modeling and optimization of ultrasound-assisted bleaching of olive oil were accomplished by a hybrid artificial neural network (ANN) and genetic algorithm (GA) method using an ultrasonic bath with a frequency of 25kHz. The influence of process parameters including ultrasonic power, bleaching clay dosage, process temperature and time (inputs) on final Lovibond red (Lr) and peroxide value (PV) (outputs) was modeled by a multilayer feed-forward back propagation ANN. The accurate 2-hidden layer model with 20 neurons in each, high R2 (up to 90%) and minimum mean square error (MSE) obtained by ANN was introduced to GA to find the best operation conditions to achieve minimum Lr and PV. The optimum treatment was found with ultrasonic power of 30%, bleaching clay of 1.2%, bleaching time of 13min and temperature of 65°C. Under optimal conditions, Lr and PV were 2.47 and 6.49 (meqO2/kg), respectively, that were consistent with predicted values.Optimally ultrasonic bleached olive oil and an industrially bleached olive oil were compared. In most cases, the results indicated no detrimental effects of ultrasound on oil structure. Thus, 40% reduction in bleaching clay dosage, 35% reduction in process temperature and 57% reduction in time over ultrasound-assisted bleaching which not only provided economic and environmental benefits, but also retained edible oil nutritional value in comparison to common bleaching procedure. The results of this study confirm the applicability of ultrasound-assisted bleaching by ultrasonic bath as an economic and feasible approach for bleaching of olive oil to reduce high bleaching costs.

Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption

Energy prediction models are used in buildings as a performance evaluation engine in advanced control and optimisation, and in making informed decisions by facility managers and utilities for enhanced energy efficiency. Simplified and data-driven models are often the preferred option where pertinent information for detailed simulation are not available and where fast responses are required. We compared the performance of the widely-used feed-forward back-propagation artificial neural network (ANN) with random forest (RF), an ensemble-based method gaining popularity in prediction – for predicting the hourly HVAC energy consumption of a hotel in Madrid, Spain. Incorporating social parameters such as the numbers of guests marginally increased prediction accuracy in both cases. Overall, ANN performed marginally better than RF with root-mean-square error (RMSE) of 4.97 and 6.10 respectively. However, the ease of tuning and modelling with categorical variables offers ensemble-based algorithms an advantage for dealing with multi-dimensional complex data, typical in buildings. RF performs internal cross-validation (i.e. using out-of-bag samples) and only has a few tuning parameters. Both models have comparable predictive power and nearly equally applicable in building energy applications.

Modeling roadway traffic noise in a hot climate using artificial neural networks

Road traffic noise is the most prevalent form of environmental noise pollution, and it can either be measured in the field or predicted through verified mathematical models. While many road traffic noise models are available around the world, these models cannot be simply generalized because local conditions affecting such noise (e.g., vehicle type and weather) vary from one locality to another. In this paper, the Artificial Neural Network (ANN) technique was employed to model road traffic noise in a city with known hot climate, namely Sharjah City in United Arab Emirates. Toward this end, field data were collected from three different road sites which resulted in more than 420 hourly measurements of noise level, traffic volume and classification, average speed, and roadway temperature. Overall, a total of 16 feed-forward back-propagation ANN models with one and two hidden layers were undertaken in this research. The best-performing models were compared with two conventional models: The Basic Statistical Traffic Noise model (BSTN) and the Ontario Ministry of Transport Road Traffic Noise model (ORNAMENT). In general, results showed that ANN models outperformed the conventional models. Furthermore, the performance of both ANN and conventional road noise models was further improved by including roadway temperature. While the reported work emphasizes the importance of adapting models to different local conditions, it also encourages others to consider the roadway temperature when modeling traffic noise, especially in areas of hot climate. Given that ANN models are often designated as “black-box,” a few techniques were utilized to expand the capabilities of ANN models to offer explanatory insights in modeling the inter-relations between the variables considered in this study. Generally, it was found that the ANN model was capable of identifying these relations as expected. In-depth analysis showed that the distance from the edge of road was found to be the most significant contributing factor whereas heavy-vehicle volume was surprisingly found to be the least.

A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.

Artificial neural network-based prediction technique for coating thickness in Fe-Al coatings fabricated by mechanical milling

ABSTRACTThe objective of this study was to evaluate the effect of milling time, milling speed and particle size of initial powders on the coating thickness of Fe-Al intermetallic coating by using artificial neural network (ANN). Coating morphology and cross-section microstructures were evaluated using a scanning electron microscope (SEM). It was found that an increase in the milling time provided an increase in the coating-layer thickness due to the cold welding process between particles and the steel substrate. The microstructure of the coating surface was refined by ball impacts in the milling process. As a result of this study, the ANN was found to be successful for predicting the coating thickness of Fe-Al intermetallic coatings. The correlation between the predicted values and the experimental data of the feed-forward back-propagation ANN was quite adequate. The mean absolute percentage error (MAPE) for the predicted values didn’t exceed 7.46%. The ANN model can be used for predicting the coating thickness of Fe-Al intermetallic coating produced for different milling time, milling speed and particle size.

Prognostication of energy use and environmental impacts for recycle system of municipal solid waste management

The aim of this study is to perform a life cycle and energy flow assessments of Municipal Solid Waste (MSW). These latter are modeled using Artificial Neural Network (ANN) for integrated waste management system in Tehran Municipality, Iran. The initial data used in this study have been obtained from Waste Management Organization of Tehran Municipality, while the required data related to the background system were extracted from the EcoInvent 2.2 database. Based on the obtained results from the energy cycle analysis, energy consumptions for transportation and processing plus recycling were calculated as 227.02 and 155.95 Gigajoules (GJ), respectively for 8500 t MSW. This energy consumption has led to the production of 18,884.03 GJ energy output (recycled materials) with an energy ratio of 121.56. A high energy ratio demonstrated that recycling of municipal solid is a new gateway against energy challenges. By optimizing energy consumption, especially energy related to transportation, this ratio could increase more than before. The results obtained from Life Cycle Assessment (LCA) indicate that transportation is considered the most important hot spot in the recycling of MSW while recycling papers is the main contributor to the redaction of environmental indicators in the recycling system. More intensity is observed for indicators related to toxicity due to the lack of waste disposal and to the entry of undesirable substances in natural resources. The 5-7-7-11 structure is the best topology to predict the amount of recycled materials and the impacts of environmental emissions from MSW recycling process developed by feed-forward back-propagation ANN models which are based on Levenberg-Marquardt (LM) training algorithm. Coefficient of determination values for train are in the ranges of 0.926–0.978 and have excellent performance in predicting all the output parameters based on the input parameters.

Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions

The purpose of this study is to predict the thermal conductivity of copper oxide (CuO) nanofluid by using feed forward backpropagation artificial neural network (FFBP-ANN). Thermal conductivity of CuO nanofluid is measured experimentally using transient hot-wire technique in temperature range of 20–60 °C and in volume fractions of 0.00125, 0.0025, 0.005 and 0.01% for neural network training and modeling. In addition, in order to evaluate accuracy of modeling in predicting the coefficient of nanofluid thermal conductivity, indices of root-mean-square error, coefficient of determination (R 2) and mean absolute percentage error have been used. FFBP-ANN with two input parameters (volume fraction and nanofluid temperature) and one output parameter (nanofluid thermal conductivity) in addition to two hidden layers and one outer layer which purelin, logsig and tansig functions are used was considered as the most optimum structure for modeling with neuron number of 4–10–1. In this study, among common methods of theoretical modeling of nanofluid thermal conductivity, theoretical method of Maxwell and also multivariate linear regression model was used for explaining the importance of modeling and predicting the results using neural network. According to this research, the results of indices and predictions show high accuracy and certainty of ANN modeling in comparison with empirical results and theoretical models.