Artificial Neural Network Modeling Approach Research Articles

Photocatalytic degradation of textile reactive dye using artificial neural network modeling approach

This study describes the application of TiO2, ZnO, TiO2–ZnO-based systems modified with 1.5 and 2.5% wt. Fe using the impregnation method for the maxilon blue 5G dye discoloration. Specific surface area (BET method), X-ray diffraction, thermogravimetric analysis, and photoacoustic spectroscopy characterization techniques were used in this work. The presented results indicate that structure alterations, textural properties, nominal metallic charges, and photocatalyst thermal treatment affected the photocatalytic activity of the system. Moreover, all of the synthesized catalysts yielded complete discoloration of the maxilon blue 5G dye within 180 min of reaction. The reaction time required for total discoloration was strongly affected by the temperature of photocatalysts calcination. In general, increasing the catalysts nominal metallic charge improves the adsorption process. A neural model was developed for each studied catalyst using MLP network with three intermediate layers, backpropagation learning algorithm, and sigmoid activation function implemented in FORTRAN. The three models presented the best results with three neurons in the intermediate layer. Therefore, the neural networks can be successfully employed to model the discoloration process involving the synthesized catalyst (R2 varying between 0.98 and 0.99).

A simulation optimization approach for flow-shop scheduling problem: a canned fruit industry

Flow-shop scheduling is one of the major problems in many manufacturing systems. Canned fruit is one of the industries in which the flow-shop scheduling has already been used. In this paper, an aggregated artificial neural network and simulation modeling approach are proposed to find optimal solution for such cases. Therefore, artificial neural network and simulation (ANNS) approach is introduced and used as a new approach to solve a certain flow-shop scheduling problem with the objective of minimizing total cost. In this research, flow-shop scheduling problem with parallel identical machines is investigated. The proposed approach is compared with the previous works, and the performance of the proposed approached is studied on a test problem. Experimental results show the superiority of the presented approach over conventional simulation approaches.

Medium-Term Urban Water Demand Forecasting with Limited Data Using an Ensemble Wavelet–Bootstrap Machine-Learning Approach

AbstractAccurate and reliable weekly and monthly water demand forecasting is important for effective and sustainable planning and use of urban water supply infrastructure. This study explored a hybrid wavelet–bootstrap–artificial neural network (WBANN) modeling approach for weekly (one-week) and monthly (one- and two-month) urban water demand forecasting in situations with limited data availability. The performance of WBANN models was also compared with that of standard artificial neural networks (ANN), bootstrap-based ANN (BANN), and wavelet-based ANN (WANN) models. The proposed WBANN method is aimed at improving the accuracy and reliability of water demand forecasting by incorporating the capability of wavelet transformation and bootstrap analysis using artificial neural networks. Daily and monthly maximum temperature, total precipitation, and water demand data for almost three years obtained from the city of Calgary, Alberta, Canada were used in this study. For weekly and monthly lead-time forecasting,...

Performance prediction of gravity concentrator by using artificial neural network-a case study

In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used. Optimisation along with performance prediction of the unit operation is necessary for efficient recovery. So, in this present study, an artificial neural network (ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade (%) and recovery (%). A three layer feed forward neural network (3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate (L/min), deck tilt angle (degree) and slurry feed rate (L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.

Artificial neural network modelling approach for a biomass gasification process in fixed bed gasifiers

The number of the small and middle-scale biomass gasification combined heat and power plants as well as syngas production plants has been significantly increased in the last decade mostly due to extensive incentives. However, existing issues regarding syngas quality, process efficiency, emissions and environmental standards are preventing biomass gasification technology to become more economically viable. To encounter these issues, special attention is given to the development of mathematical models which can be used for a process analysis or plant control purposes. The presented paper analyses possibilities of neural networks to predict process parameters with high speed and accuracy. After a related literature review and measurement data analysis, different modelling approaches for the process parameter prediction that can be used for an on-line process control were developed and their performance were analysed. Neural network models showed good capability to predict biomass gasification process parameters with reasonable accuracy and speed. Measurement data for the model development, verification and performance analysis were derived from biomass gasification plant operated by Technical University Dresden.

Integration of Artificial Neural Network Modeling and Genetic Algorithm Approach for Enrichment of Laccase Production in Solid State Fermentation by Pleurotus ostreatus

Black gram husk was used as a solid substrate for laccase production by Pleurotus ostreatus, and various fermentation conditions were optimized based on an artificial intelligence method. A total of six parameters, i.e., temperature, inoculum concentration, moisture content, CuSO4, glucose, and peptone concentrations, were optimized. A total of 50 experiments were conducted, and the obtained data were modeled by a hybrid of artificial neural network (ANN) and genetic algorithm (GA) approaches. ANN was employed to model the experimental data, and the predicted values were further optimized by GA. Employment of ANN–GA hybrid methodology resulted in a significant improvement, as approximately two-fold laccase production (4244 U/gds) was achieved.

Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater

AbstractGroundwater modelling has emerged as a powerful tool to develop a sustainable management plan for efficient groundwater utilization and protection of this vital resource. This study deals with the development of five hybrid artificial neural network (ANN) models and their critical assessment for simulating spatio‐temporal fluctuations of groundwater in an alluvial aquifer system. Unlike past studies, in this study, all the relevant input variables having significant influence on groundwater have been considered, and the hybrid ANN technique [ANN‐cum‐Genetic Algorithm (GA)] has been used to simulate groundwater levels at 17 sites over the study area. The parameters of the ANN models were optimized using a GA optimization technique. The predictive ability of the five hybrid ANN models developed for each of the 17 sites was evaluated using six goodness‐of‐fit criteria and graphical indicators, together with adequate uncertainty analyses. The analysis of the results of this study revealed that the multilayer perceptron Levenberg–Marquardt model is the most efficient in predicting monthly groundwater levels at almost all of the 17 sites, while the radial basis function model is the least efficient. The GA technique was found to be superior to the commonly used trial‐and‐error method for determining optimal ANN architecture and internal parameters. Of the goodness‐of‐fit statistics used in this study, only root‐mean‐squared error, r2 and Nash–Sutcliffe efficiency were found to be more powerful and useful in assessing the performance of the ANN models. It can be concluded that the hybrid ANN modelling approach can be effectively used for predicting spatio‐temporal fluctuations of groundwater at basin or subbasin scales. Copyright © 2014 John Wiley & Sons, Ltd.

The Neural Network Control Algorithm Research of Single Crystal Furnace Temperature System

Straight pull single crystal furnaces temperature control system has problem of the long time lag and nonlinearity, so the precise mathematic mode that is hard to build. Advanced control strategies show strong advantages for resolving these problems. This paper use artificial neural network modeling approach to establish single crystal furnace temperatures neural network control BP structure model, use adaptive method to control the temperature of the single crystal furnace.

Artificial Neural Network Approach for Impingement Drying of Germinated Brown Rice Soaking with Turmeric Solution

The objectives of this research are to determine the evolution of moisture transfer for germinated Thai jasmine Khao Dawk Mali 105 (KDML105) brown rice variety using impingement drying by eight commonly empirical drying modeling and artificial neural network (ANN) method. The experiments were carried out with drying temperatures of 80-100°C, initial moisture content of KDML105 rice samples soaking with turmeric solution was of 54-55% dry-basis and the desired final moisture content for each drying conditions was fixed at 14-16% dry-basis. The air flow rate was fixed at 7.0 m/s. The measured data in each drying conditions were simulated for getting drying equation by non-linear regression analysis. The results showed that the rice soaking with herb turmeric solution had no effect to drying kinetics and the simulated data using empirical drying equation of Henderson model had the best fitting to all measured data (R2of 0.9978-0.9995 and RMSE of 0.0001441-0.000414). For applying ANN modeling approach, the drying temperature and drying time were considered as the input variables for the topology of neural network while the moisture ratio was the output layer. The simulation results concluded that the simulated values of the ANN model, which was not concerned with any complicated physical properties of grain rice kernels, could be used for prediction drying kinetics and was relatively high accuracy compared to those predicted results of empirical models. So the ANN method without any complicated properties related of rice samples can approach for good prediction their drying kinetics as well as the complicated drying simulations method.

Correlating bubble points of ternary systems involving nine solvents and two ionic liquids using artificial neural network

The use of ionic liquids is being increasingly investigated in separation technologies, for example, in special distillation processes such as azeotropic or close-boiling mixtures. Such applications require accurate knowledge of the physical properties of the mixtures involved. In this respect, the correlation or estimation of the bubble points might be difficult due to the complex nature of some ternary systems, especially in the presence of ionic liquids. In the present study, the bubble points of several ternary mixtures containing an ionic liquid were correlated using an artificial neural network modeling approach. The solvents investigated consisted of 1-propanol, 2-propanol, ethyl ethanoate, methyl ethanoate, chloroform, propanone, ethanol, methanol and water and the ionic liquids considered were 1-ethyl-3-methylimidazolium trifluoromethane-sulfonate ([emim][CF3SO3]) and 1-butyl-3-methylimidazolium tetrafluoroborate ([bmim][BF4]). For this purpose, a total of 529 experimental data points from previously published literature were collected to aid in finding the best network architecture and the optimum parameters. To do this, the collected data were divided into two subsets, namely the training and testing subsets. Using the training data set, and based on a trial and error procedure, the optimum network parameters were determined to be three layers including one input, one hidden and one output layer, nine neurons in the hidden layer, the logarithmic-sigmoid transfer function for the hidden layer, and the purline function for the output layer. The optimized weights and biases were also obtained and presented. The feasibility of the proposed network for the correlation of ternary bubble point was then examined using data that were not used in optimizing the network. The overall average absolute relative deviation (AARD %), mean square error (MSE), maximum deviation (Emax), minimum deviation (Emin) and correlation coefficient (R2) were calculated to be 0.20%, 0.9953, −0.97, 0.87 and 0.95, respectively.

Based on the BP Neural Network VAc Synthesis Reactor in Temperature Control

This paper analyzes the synthesis of vinyl acetate production process and technology, and applied the artificial neural network modeling approach, by using the adaptive learning rate BP learning algorithm, then established the medium temperature control of BP neural network in the structure model of the VAc synthesis process of flurried bed synthesis reactor, by controlling medium temperature of the synthesis reactor, the results show that: Be able to effectively will in reactor temperature control in good accuracy.

Forecasting the Tropospheric Ozone using Artificial Neural Network Modelling Approach: A Case Study of Megacity Madras, India

Ozone, which is a secondary pollutant in the troposphere, is very injurious to human health causing irritation of respiratory system, reducing lung capacity, etc. Adversely affecting the plant growth and deteriorating the materials. Thus it is of prime importance to predict the ozone concentration so that effective mitigation strategies can be adopted. As the formation of the tropospheric ozone is dependent on various meteorological parameters and the concentration of various other air pollutants, it is necessary to consider this dependence aspect in the modelling approach. In this background, the present paper puts forward the study of short-term prediction of tropospheric ozone concentration using Artificial Neural Network (ANN) modelling approach for a busy traffic junction of Madras city, one of the four megacities of India. 8-hourly averaged values of 11 air pollutants concentrations and 6 meteorological parameters were used for the study. The respective data was collected at a busy traffic junction of the city for a period of 19 months i.e. during September 2008–March 2010. 70% of the data was used for training the ANN models while the remaining of 30% data was used for validating them. By changing the neural architecture, 34 ANN models were formulated which were statistically analyzed. Based on the encouraging results (d=0.80, r=0.69, etc.), the paper puts forward the suitability of ANN modelling approach for the short-term prediction of tropospheric ozone concentration.

RSM and ANN Modeling Approaches For Predicting Average Cutting Speed During WEDM of SiCp/6061 Al MMC

This paper describes the response surface methodology (RSM) and artificial neural network (ANN) based mathematical modeling for average cutting speed of SiCp/6061 Al metal matrix composite (MMC) during wire electric discharge machining (WEDM). Four WEDM parameters namely servo voltage (SV), pulse-on time (TON), pulse-off time (TOFF) and wire feed rate (WF) were chosen as machining process parameters. A back propagation neural network was developed to establish the process model. The performance of the developed ANN models were compared with the RSM mathematical models of average cutting speed. The comparison clearly indicates that the ANN models provide more accurate prediction compared to the RSM models. Combined effect of input process parameters on average cutting speed shows that voltage is more significant parameter on avergae cutting speed than pulse-off time and wire feed rate.

Empirical Model and Artificial Neural Network Model Approach for Air Dried Sheets (ADS) Rubber

The objective of this research was to predict drying behavior of hot air drying using an empirical model (EM) and an artificial neural network model (ANN). Rubber sheet with initial moisture content ranging of 23-40% dry-basis was dried by temperature ranging of 40-70°C and air flow rate of 0.7 m/s. The desired final moisture content was set at 0.15% dry-basis. The results showed that drying rate of rubber sheet dried with hot air convection was faster than conventional natural aeration. The EM and ANN were simulated to describe the drying behavior of products. Furthermore, prediction results between EM and ANN were compared with the experimental data. In this research, it was obviously found that ANN can describe the drying behavior effectively. Additionally, it was also found that predicted results of Multilayer feed forward Levenberg-Maqurdt’s Back-propagation ANN were good agreement with the experimental results compared to those results of EM. It is the optimum architecture for prediction the evolution of moisture transfer for hot air drying.

Neural network nonlinear modeling for hydrogen production using anaerobic fermentation

The potential of utilizing artificial neural network (ANN) model approach for simulate and predict the hydrogen yield in batch model using Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) was investigated. A unique architecture has been introduced in this research to mimic the inter-relationship between three input parameters initial substrate, initial medium pH and reaction temperature (37 °C, 6.0 ± 0.2, 10), respectively, to predict hydrogen yield. Sixty data records from the experiment have been utilized to develop the ANN model. The results showed that the proposed ANN model provided significant level of accuracy for prediction with maximum error (10 %). Furthermore, a comparative analysis with a traditional approach Box–Wilson design (BWD) has proved that the ANN model output significantly outperformed the BWD. ANN model overcomes the limitation of the BWD approach with respect to the number of records, which is merely considering limited length of stochastic pattern for hydrogen yield (15 records).

The Prediction of Undersaturated Crude Oil Viscosity: An Artificial Neural Network and Fuzzy Model Approach

Viscosity is one of the most important governing parameters of the fluid flow, either in the porous media or in pipelines. So it is important to use an accurate method to calculate the oil viscosity at various operating conditions. In the literature, several empirical correlations have been proposed for predicting undersaturated crude oil viscosity. However these correlations are not able to predict the oil viscosity adequately for a wide range of conditions. An extensive experimental data of undersaturated oil viscosities from different samples of Iranian oil reservoirs was applied to develop an Artificial Neural Network (ANN) model and fuzzy model to predict and calculate the undersaturated oil viscosity. Validity and accuracy of these models has been confirmed by comparing the obtained results of these correlations and with experimental data for Iranian oil samples. It was observed that there is acceptable agreement between the ANN model and fuzzy model results with experimental data.

ANN modeling approach for designing low noise phemt amplifier in wireless communication systems

Future efficient wireless communication systems require, for high quality performance a Broadband Amplifier in the frequency range under consideration. This could be plugged into the measuring path so that it enables the system to perceive even the weakest signals. For this a new Scattering-parameter model for the microwave analysis of a PHEMT (Pseudomorphic High Electron Mobility Transistor) has been developed, which is valid for a wide frequency range. The developed neural network model is used in designing the PHEMT power amplifier. The calculated S-parameters, gain and minimum noise figure from the ANN (Artificial Neural Networks) model are the parameters which are used to design the low noise PHEMT power amplifier. The various gains so obtained from the S-parameters are plotted with the frequency and yield a closer fit to the simulated model. The neural network training has been done using Levenberg-Marqaurdt back propagation algorithm implemented in ANN toolbox of MATLAB software. All the results have been compared with the experimental data and show a close agreement and the validity of our model.

Suspended sediment load prediction of river systems: An artificial neural network approach

Abstract Information on suspended sediment load is crucial to water management and environmental protection. Suspended sediment loads for three major rivers (Mississippi, Missouri and Rio Grande) in USA are estimated using artificial neural network (ANN) modeling approach. A multilayer perceptron (MLP) ANN with an error back propagation algorithm, using historical daily and weekly hydroclimatological data (precipitation P ( t ) , current discharge Q ( t ) , antecedent discharge Q ( t −1) , and antecedent sediment load SL ( t −1) ), is used to predict the suspended sediment load SL ( t ) at the selected monitoring stations. Performance of ANN was evaluated using different combinations of input data sets, length of record for training, and temporal resolution (daily and weekly data). Results from ANN model were compared with results from multiple linear regressions (MLR), multiple non-linear regression (MNLR) and Autoregressive integrated moving average (ARIMA) using correlation coefficient ( R ), mean absolute percent error ( MAPE ) and model efficiency ( E ). Comparison of training period length was also made (4, 3 and 2 years of training and 1, 2 and 3 years of testing, respectively). The model efficiency ( E ) and R 2 values were slightly higher for the 4 years of training and 1 year of testing (4 * 1) for Mississippi River, indifferent for Missouri and slightly lower for Rio Grande River. Daily simulations using Input 1 ( P ( t ) , Q ( t ) , Q ( t −1) , SL ( t −1) ) and three years of training and two years of testing (3 * 2) performed better ( R 2 and E of 0.85 and 0.72, respectively) than the simulation with two years of training and three years of testing (2 * 3) ( R 2 and E of 0.64 and 0.46, respectively). ANN predicted daily values using Input 1 and 3 * 2 architecture for Missouri ( R 2 = 0.97) and Mississippi ( R 2 = 0.96) were better than those of Rio Grande ( R 2 = 0.65). Daily predictions were better compared to weekly predictions for all three rivers due to higher correlation within daily than weekly data. ANN predictions for most simulations were superior compared to predictions using MLR, MNLR and ARIMA. The modeling approach presented in this paper can be potentially used to reduce the frequency of costly operations for sediment measurement where hydrological data is readily available.

Artificial neural network modeling of fixed bed biosorption using radial basis approach

In modern day scenario, biosorption is a cost effective separation technology for the removal of various pollutants from wastewater and waste streams from various process industries. The difficulties associated in rigorous mathematical modeling of a fixed bed bio-adsorbing systems due to the complexities of the process often makes the development of pure black-box artificial neural network (ANN) models particularly useful in this field. In this work, radial basis function network has been employed as ANN to model the breakthrough curves in fixed bed biosorption. The prediction has been compared to the experimental breakthrough curves of Cadmium, Lanthanum and a dye available in the literature. Results show that this network gives fairly accurate representation of the actual breakthrough curves. The results obtained from ANN modeling approach shows the better agreement between experimental and predicted breakthrough curves as the error for all these situations are within 6%.

Previsão de Vazões com a Metodologia DPFT e com Redes Neurais Artificiais

The comparison of two flood forecasting methodologies, using models with few parameters to calibrate is presented. In DPFT method (First Differences of the Transfer Function) one uses a multi-events rainfall-runoff set and an iterative algorithm for the simultaneous identification of the average Unit Hydrograph (Transfer Function) of the watershed and of a series of effective precipitations associated to each event. This last particularity allows to the calibration and comparison of different Loss Function models, relating observed precipitations to calculated effective precipitations identified by the methodology. In this case a simple Loss Function model is used, the GR3 model. The DPFT methodology is compared with a simple Artificial Neural Network ( ANN ) model approach, where the non-linearities inerent to the rainfall-runoff process are represented by a MLP structure with 4 neurons concerning to the discharges and the precipitations. The results of application and comparison of the methods in two medium sized watersheds are presented and analysed. In this study case, the DPFT methodology showed a slightly better fitting than the ANN model, despite more irregular results. Keywords: methodology DPFT, artificial neural networks