Tangent Sigmoid Research Articles

Machine learning approach for prediction of paracetamol adsorption efficiency on chemically modified orange peel.

High consumption of paracetamol (PCM) has led to the discharge of a large quantity of its metabolite into the environment and there is an urgent need to remove this harmful contaminant in a sustainable manner. In this work, Artificial Neural Network (ANN) was used as a Machine Learning tool for prediction of PCM adsorption efficiency on chemically modified orange peel (CMOP). Orange peel was chemically modified with orthophosphoric acid and then characterized using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). Thereafter, batch adsorption of PCM on CMOP were conducted at different operating conditions namely: contact time (0-330min), temperature (30-50°C) and initial drug concentration (10mg/L-50mg/L) to obtain the residual concentration of PCM in solution. Experimental data was used to compute the adsorption efficiency of PCM on CMOP. To predict the adsorption efficiency, different ANN architectures were examined. A neural network structure with Levenberg Marquardt (LM) training algorithm, 17 hidden neurons, and tangent sigmoid transfer function at both the input and output layers gave the best level of prediction. Comparing with experimental data, the optimal model yielded Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation coefficient (R2) of 5.8985×10-04, 0.0243 and 0.9958 respectively. The results obtained showed that ANN is efficient in predicting the adsorption efficiency of PCM on CMOP.

Modeling of tool wear in machining of AISI 52100 steel using artificial neural networks

Machining is an arduous process for both the cutting tool and workpiece material. Conducting a series of machining tests involves a lot of money and time. It is important to prevent time consuming runs and prioritize technology. Hence, in this investigation, artificial neural network (ANN) approach is employed to estimate the cutting tool wear. From the experimentally obtained values, the input variables chosen for ANN modeling are cutting speed, feed and depth of cut corresponding to the output variable tool wear. The ANN modeling comprises Levenberg-Marquardt (trainlm) and tangent sigmoid (tansig) as training and testing functions respectively. Optimum architecture 3–6-6–1 is obtained based on the mean sum squared error (MSE) and average error (AE) of input data. Tool flank wear is assessed in three different machining environments such as dry machining, wet machining (flood cooling) and minimum quantity lubrication (MQL) machining. The emulsifier oil based cutting fluid is used as cutting fluid in wet and MQL machining. ANN model predicted values are compared with experimental results. Based on the correlation coefficient factor, it was observed that the prediction of ANN is high, showing a significant harmony between predicted response and experimental output.

A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®

Aspen Plus® is one of the practicable software for investigation of the biomass gasification characteristics. Also, artificial neural networks (ANN) as a deep learning approach are often used in the prediction of parameters such as syngas composition, lower heating value (LHV), exergy, etc. However, to our best knowledge, a universal deep learning model based on the thermodynamic equilibrium approach to predict LHV of syngas in circulating fluidized bed (CFB) gasifier is not available in literature yet. In this paper, a unique CFB gasifier model was developed in Aspen Plus® as a tool to create a total of over 1 million datasets for the training of a deep learning model that predicts the LHV of the syngas. The CFB gasifier model was found to be in agreement with the results when compared with the experimental data in the literature. 56 biomass with various elemental and proximate properties were gasified in a newly developed CFB gasifier model under different operating conditions by using sensitivity analysis in Aspen Plus®. A novel artificial neural network model, which is regularized with Levenberg-Marquardt algorithm was used as a deep learning model with a 6-12-1 tangent sigmoid architecture to predict LHV of syngas in circulating fluidized bed (CFB) gasifier, requiring minimal specificity as compared to commercial simulators require significant modelling effort and test runs. Results showed that the estimated LHV of the syngas in an agreement with the calculated values. The coefficient of determination score was calculated as R2 > 0.99 for all datasets.

Relating groundwater levels with meteorological parameters using ANN technique

In this study, the groundwater occupied within the boundaries of River Ravi and River Sutlej was investigated by using artificial neural networks (ANN) by using the meteorological parameters (data of rainfall (R), maximum temperature (Max.T), minimum temperature (Min.T), solar radiation (S.R.), wind (W), relative humidity (R.H.), the elevation of area (E), polygon area (A) and water table depths (D/W). The best efficient models were studied by using different types of network architecture, such as the number of neurons, hidden layers, and activation function with a different percent of data in training, validation, and testing. The developed Levenberg-Marquardt back-propagation ANN models were compared through statistical performance criteria: Mean Square Error (MSE), Mean Absolute Error (MAE) and Coefficient of determination (R). The results (TT-8-24-1 for pre-monsoon and TT-8-24-1 for post-monsoon) show that ANN model with single hidden layer, 24 neurons, 80% of data for training, 10% for validation, 10% for testing and using tangent sigmoid activation function was found to be optimistic ANN model with MAE, MSE and R values of 0.0338, 0.0023 and 0.97 for pre-monsoon and 0.031, 0.0021 and 0.974 for the case of post-monsoon respectively.

Artificial Neural Network-Based 4-D Hyper-Chaotic System on Field Programmable Gate Array

In this presented study, a 4-D hyper-chaotic system newly proposed to the literature, has been implemented as Multi-Layer Feed-Forward Artificial Neural Network-based on FPGA chip with 32-bit IEEE-754-1985 floating-point number standard to be utilized in real time chaos-based applications. In the first step of the study, 4-D hyper-chaotic system has been numerically modeled on FPGA using Dormand-Prince numeric algorithm. In the second step, the data set (4X10,000) obtained from Matlab-based numeric model has been divided into two parts as training data set (4X8,000) and test data set (4X2,000) to create ANN-based 4-D hyper-chaotic system. A Multi-Layer Feed-Forward ANN structure with 4 inputs and 4 outputs has been constructed for ANN-based 4-D hyper-chaotic system. This structure has only one hidden layer and there are 8 neurons having Tangent Sigmoid activation function used as the activation function in each neuron. 2.58E-07 Mean Square Error (MSE) value has been obtained from the training of ANN-based 4-D hyper-chaotic system. In the third step, after the successful training of ANN-based 4-D hyper-chaotic system, the design of ANN-based 4-D hyper-chaotic system has been carried out on FPGA by taking the bias and weight values of the ANN structure as reference. In this step, at first, Matlab-based Feed-Forward Multi-Layer 4X8X4 network structure has been coded in Very High Speed Integrated Circuit Hardware Description Language (VHDL) to be implemented on FPGA chips. Then, the bias and weight values of the ANN structure has been converted from decimal number system to floating-point number standard and these converted values have been embedded into the network structure. In the last step, the ANN-based 4-D hyper-chaotic system designed on FPGA has been synthesized and tested using Xilinx ISE Design Suite. The chip statistics have been given after the Place&Route process carried out for the Virtex XC6VHX255T-3FF1155 FPGA chip. The maximum operating frequency of ANN-based 4-D hyper-chaotic system on FPGA has been obtained as 240.861 MHZ.

Robust adaptive control for continuous wheel slip rate tracking of vehicle with state observer

This article proposes a novel robust adaptive wheel slip rate tracking control method with state observer. First, a modified tracking differentiator is proposed based on a combination of tangent sigmoid function with terminal attraction factor and linear function to improve convergence speed and avoid chattering phenomenon, and then, the modified tracking differentiator is used as state observer to smooth and estimate the states of the system. Second, a robust adaptive wheel slip rate tracking control law with fuzzy uncertainty observer and modified adaptive laws is derived based on Lyapunov-based method. The fuzzy uncertainty observer is used for estimating and compensating the additive uncertainty, and the modified adaptive laws are used for estimating the unknown optimal weight vector of the fuzzy uncertainty observer and the multiplicative uncertainty. Finally, the performance of the robust adaptive wheel slip rate tracking control method is verified based on the model-in-the-loop simulation system.

Optimization of hydrogen production via toluene steam reforming over Ni–Co supported modified-activated carbon using ANN coupled GA and RSM

Hydrogen (H2) is a clean fuel that can be produced from various resources including biomass. Optimization of H2 production from catalytic steam reforming of toluene using response surface methodology (RSM) and artificial neural network coupled genetic algorithm (ANN-GA) models has been investigated. In RSM model, the central composite design (CCD) is employed in the experimental design. The CCD conditions are temperature (500–900 °C), feed flow rate (0.006–0.034 ml/min), catalyst weight (0.1–0.5 g) and steam-to-carbon molar ratio (1–9). ANN model employs a three-layered feed-forward backpropagation neural network in conjugation with the tangent sigmoid (tansig) and linear (purelin) as the transfer functions and Levenberg-Marquardt training algorithm. Best network structure of 4-14-1 is developed and utilized in the GA optimization for determining the optimum conditions. An optimum H2 yield of 92.6% and 81.4% with 1.19% and 6.02% prediction error are obtained from ANN-GA and RSM models, respectively. The predictive capabilities of the two models are evaluated by statistical parameters, including the coefficient of determination (R2) and root mean square error (RMSE). Higher R2 and lower RSME values are reported for ANN-GA model (R2 = 0.95, RMSE = 4.09) demonstrating the superiority of ANN-GA in determining the nonlinear behavior compared to RSM model (R2 = 0.87, RMSE = 6.92). These results infer that ANN-GA is a more reliable and robust predictive steam reforming modelling tool for H2 production optimization compared to RSM model.

Modeling pseudo-second-order kinetics of orange peel-paracetamol adsorption process using artificial neural network

In this work, an artificial neural network (ANN) was developed to model the Pseudo- Second Order (PSO) kinetics of orange peel-paracetamol adsorption process. The orange peel used for the adsorption process was prepared, activated, and characterized using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) techniques respectively. Batch adsorption experiment was carried out to obtain the concentrations of paracetamol (PCM) adsorbed on orange peel activated carbon (OPAC) at different operating conditions which include contact time (0–330 ​minutes), initial PCM concentration (10–50 ​mg/L), and temperature (30–50 ​°C). Then, the experimental data was used to compute PSO kinetics of the orange peel – paracetamol adsorption process. To predict the PSO kinetics, different ANN structures were investigated. The optimal ANN structure which uses 18 hidden neurons, hyperbolic tangent sigmoid transfer function (tansig) at the input layer, linear transfer function (purelin) at the output layer, and Levenberg Marquardt as its backpropagation algorithm demonstrated the optimal prediction ability. Specifically, the optimal ANN model gave Mean Average Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and R2 values of 0.0515, 0.0064, 0.0798 and 1.0000 respectively when compared with the experimental data. The results obtained showed that ANN can be used to effectively model PSO kinetics of orange peel-paracetamol adsorption process.

Neural-Network-Based Root Mean Delay Spread Model for Ubiquitous Indoor Internet-of-Things Scenarios

Massive robust communication demands among machines and humans are required in ubiquitous Internet-of-Things (IoT) applications. To design the appropriate communication system, the knowledge of the propagation characteristics for various IoTs scenarios is necessary. In this article, a measurement-based neural-network-based root-mean-square (RMS) delay spread model for ubiquitous indoor IoTs scenarios is presented. The proposed model is a two-layer feedforward neural network plus a random variable, characterizing the average RMS delay spread and uncertain shadowing effect, respectively. The neural network consists of five inputs, including transmitting/receiving antennas (Tx/Rx) separation, frequency, antenna height, environment, and line-of-sight/non-line-of-sight (LOS/NLOS) propagation condition, seven hidden layer neurons, and one output layer neuron. Compared with different configurations of the neural network, the hyperbolic tangent sigmoid functions and the Levenberg–Marquardt backpropagation algorithm are selected as neurons’ activation functions and training method, respectively. Additionally, the random variable is found to follow the normal distribution using the maximum-likelihood estimation. Finally, the novel model is experimentally validated to be accurate, general, and extensible compared with the conventional normally distributed RMS delay spread model. This model is well applicable to the design and planning of the ubiquitous communication links for future IoTs scenarios.

Modeling the adsorption of chlorobenzene on modified bentonite using an artificial neural network

In this paper, a radial basis function (RBF) feed-forward network has been used to predict the adsorption capacity for chlorobenzene (CB) onto modified Algerian bentonite (HDTMA-bentonite). The input parameters used for training of the RBF model include contact time, amount of adsorbent, pH, and the initial concentrations of chlorobenzene. The adsorption capacity for chlorobenzene onto modified bentonite is considered as an output of the neural network. An RBF model is used to predict the behavior of the adsorption process with the Levenberg–Marquardt algorithm (LMA). The model uses a linear transfer function (purelin) at the output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with four neurons. The values of the determination coefficient (R2 ​= ​0.984) and the root mean square error (RMSE ​= ​0.015) showed good prediction results. Hence, the RBF model as a predictive tool has a great capacity to estimate the effect of operational parameters on the adsorption process.

The use of artificial neural network (ANN) for modeling adsorption of sunset yellow onto neodymium modified ordered mesoporous carbon

Discharging coloring products in water bodies has degraded water quality irreversibly over the past several decades. Order mesoporous carbon (OMC) was modified by embedding neodymium(III) chloride on the surface of OMC to enhance the adsorptive removal towards these contaminants. This paper represents an artificial neural network (ANN) based approach for modeling the adsorption process of sunset yellow onto neodymium modified OMC (OMC-Nd) in batch adsorption experiments. Neodymium modified OMC was characterized using N2 adsorption-desorption isotherm, TEM micrographs, FT-IR and XPS spectra analysis techniques. 2.5 wt% Nd loaded OMC was selected as the final adsorbent for further experiments because OMC-2.5Nd showed highest removal efficiency of 93%. The ANN model was trained and validated with the adsorption experiments data where initial concentration, reaction time, and adsorbent dosage were selected as the variables for the batch study, whereas the removal efficiency was considered as the output. The ANN model was first developed using a three-layer back propagation network with the optimum structure of 3-6-1. The model employed tangent sigmoid transfer function as input in the hidden layer whereas a linear transfer function was used in the output layer. The comparison between modeled data and experimental data provided high degree of correlation (R2 = 0.9832) which indicated the applicability of ANN model for describing the adsorption process with reasonable accuracy.

Hydrological drought assessment through streamflow forecasting using wavelet enabled artificial neural networks

In semi-arid watersheds, hydrological drought is manifested by reasonably low streamflow conditions. This makes streamflow forecasting as an inevitable component for implementing drought management practices. Data-driven modelling techniques are often applied for simulating the streamflow forecasts. In this study, a comparison between conventional feedforward neural network (FFNN) model and wavelet enabled artificial neural network (WANN) model is carried out to analyse their effectiveness in streamflow forecasting. The input data used to develop and simulate the models are monthly precipitation, and monthly river stage of twenty-five years (January 1991 to December 2015). Data pre-processing is carried out using correlation analysis prior to neural network modelling for selecting appropriate input combinations. The preprocessed data is directly given as input for FFNN; whereas for WANN, the preprocessed time series datasets are decomposed into several sub-series and are used as the inputs. Analysis on three different transfer functions that are commonly used in ANN models is carried out to identify the best transfer function. Hyperbolic tangent sigmoid transfer function is found to be best suitable for modelling streamflow forecasts. The result also shows that there is a significant improvement in streamflow forecasting ability for WANN models compared to FFNN. Drought forecasting is carried out by developing a standardized streamflow index from the forecasted streamflow. The drought forecasting technique discussed here will help planners to make informed decisions on watershed management and drought mitigation measures.

Efficiency of artificial neural networks in determining scour depth at composite bridge piers

ABSTRACT Scouring is the most common cause of bridge failure. This study was conducted to evaluate the efficiency of the Artificial Neural Networks (ANN) in determining scour depth around composite bridge piers. The experimental data, attained in different conditions and various pile cap locations, were used to obtain the ANN model and to compare the results of the model with most well-known empirical, HEC-18 and FDOT, methods. The data were divided into training and evaluation sets. The ANN models were trained using the experimental data, and their efficiency was evaluated using statistical test. The results showed that to estimate scour at the composite piers, feed-forward propagation network with three neurons in the hidden layer and hyperbolic sigmoid tangent transfer function was with the highest accuracy. The results also indicated a better estimation of the scour depth by the proposed ANN than the empirical methods.

Modeling of fixed-bed dye adsorption using response surface methodology and artificial neural network

In this study, artificial neural network (ANN) and response surface methodology (RSM) were applied to analyze the fixed bed adsorption of FD&C red 40 dye (Food, drug and cosmetic dye red 40) on polyurethane/chitosan foam (PU/CS foam). The adsorbent was prepared and characterized. The effects of the process variables, including flow rate and bed height, were investigated through two different levels using RSM. Breakthrough curves were used as training data set for the ANN. The ANN was customized with 10 neurons in the hidden layer using the hyperbolic tangent sigmoid transfer function as activation function and the linear transfer function in the output layer. The optimal range of bed operation was 5–6 cm for bed height and 15–17.05 mL min−1 for flow rate. The values of experimental adsorption capacity of the column ranged from 44.3 to 108.1 mg g−1, and were compared with the resulting values of the ANN and RSM models. The ANN can predict the experimental data with more accuracy than the RSM. The values found for the coefficient of determination ​​were 0.9911 for the ANN and 0.8853 for the RSM. The various error functions tested between predicted and experimental values ​​of the ANN and RSM models demonstrated a better applicability and efficiency of the ANN model. Finally, PU/CS foam proved to be a promising, low-cost adsorbent with excellent potential for removing FD&C red 40 dye.

Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions

Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for preventing and mitigating landslide hazards as it provides information regarding locations where landslides are most likely to occur. The main purpose of this study is to produce a landslide susceptibility map of Mt. Umyeon in Korea using an artificial neural network (ANN) involving the factor selection method and various non-linear activation functions. A total of 151 historical landslide events and 20 predisposing factors consisting of Geographic Information System (GIS)-based morphological, hydrological, geological, and land cover datasets were constructed with a resolution of 5 x 5 m. The collected datasets were applied to information gain ratio analysis to confirm the predictive power and multicollinearity diagnosis to ensure the correlation of independence among the landslide predisposing factors. The best 11 predisposing factors that were selected in this study were randomly divided into a 70:30 ratio for training and validation datasets, which were used to produce ANN-based landslide susceptibility models. The ANN model used in this study had a multi-layer perceptron (MLP) structure consisting of an input layer, one hidden layer, and an output layer. In the output layer, the logistic sigmoid function was used to represent the result value within the range of 0 to 1, and six non-linear activation functions were used for the hidden layer. The performance of the landslide susceptibility models was evaluated using the receiver operating characteristic curve, Kappa index, and five statistical indices (sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) with the training dataset. In addition, the landslide susceptibility models were validated using the aforementioned measures with the validation dataset and were compared using the Friedman test to check the significant differences among the six developed models. The optimal number of neurons was determined based on the aforementioned performance evaluation and validation results. Overall, the model with the best performance was the MLP model with the logistic sigmoid activation function in the output layer and the hyperbolic tangent sigmoid activation function with five neurons in the hidden layer. The validation results of the best model showed a sensitivity of 82.61%, specificity of 78.26%, accuracy of 80.43%, PPV of 79.17%, NPV of 81.82%, a Kappa index of 0.609, and AUC of 0.879. The results of this study highlight the effectiveness of selecting an optimal MLP model structure for shallow landslide susceptibility mapping using an appropriate predisposing factor section method.

Artificial Neural Network Topology Optimization using K-Fold Cross Validation for Spray Drying of Coconut Milk

In this study, the development of an optimized topology neural network model for spray drying coconut milk is investigated using K-fold cross validation technique. Performance between standalone ANN and ANN with K-fold cross validation is compared, as K-fold cross validation method is integrated into neural network to overcome the limitations of restricted dataset. With inlet temperature (140 °C-180 °C), concentration of maltodextrin and sodium caseinate (0 w/w %- 10w/w %) are established as the input parameters, while moisture content (3.64%-5.1%), outlet temperature (76.5 °C-104.5 °C) and surface free fat percentage (0.35%-34.51%) are the output parameters for the neural network. Experimental data from the spray drying process is used to develop the neural network. Selection from the best training algorithm (gradient descent backpropagation, gradient descent with momentum, resilience backpropagation, conjugate gradient backpropagation with Polak-Riebre restarts, conjugate gradient backpropagation with Fletcher-Reeves, scaled conjugate gradient, Broyden-Flectcher-Goldfard-Shanno backpropagation algorithm and Levenberg-Marquardt backpropagation), transfer function (tansig, logsig, purelin and satlin), number of training runs (1000-5000), number of hidden layers (1-3) and nodes (5-15) have significant effect on the performance of the ANN models based on the lowest MSE values and R2 values. Overall, the optimum topology ANN model with k-fold cross validation outperformed the recorded lowest MSE value of 0.064 and highest R2 value of 0.855 compared to the optimum standalone ANN model with MSE value of 0.082 and R2 value of 0.832. The optimum ANN with K-fold cross validation implements the Levenberg-Marquart training algorithm with hyperbolic tangent sigmoid transfer function using 4500 times training runs with optimal topology configuration of 3-8-2-3. Result concludes that the developed neural network using K-fold cross validation represents the spray drying process as a highly reliable model with high degree of accuracy.

Prediction of Draft Force of a Chisel Cultivator Using Artificial Neural Networks and Its Comparison with Regression Model

In this study, artificial neural networks (ANNs) were used to predict the draft force of a rigid tine chisel cultivator. The factorial experiment based on the randomized complete block design (RCBD) was used to obtain the required data and to determine the factors affecting the draft force. The draft force of the chisel cultivator was measured using a three-point hitch dynamometer and data were collected using a DT800 datalogger. A recurrent back-propagation multilayer network was selected to predict the draft force of the cultivator. The gradient descent algorithm with momentum, Levenberg–Marquardt algorithm, and scaled conjugate gradient descent algorithm were used for network training. The tangent sigmoid transfer function was the activation functions in the layers. The draft force was predicted based on the tillage depth, soil moisture content, soil cone index, and forward speed. The results showed that the developed ANNs with two hidden layers (24 and 26 neurons in the first and second layers, respectively) with the use of the scaled conjugate gradient descent algorithm outperformed the networks developed with other algorithms. The average simulation accuracy and the correlation coefficient for the prediction of draft force of a chisel cultivator were 99.83% and 0.9445, respectively. The linear regression model had a much lower accuracy and correlation coefficient for predicting the draft force compared to the ANNs.

A sustainable approach to utilize olive pips for the sorption of lead ions: Numerical modeling with aid of artificial neural network

The ability of olive pips in the removal of lead ions from polluted water was investigated using three-layer artificial neural network and 114 batch experiments. The experiments revealed that the highest removal (99%) was achieved at pH 5, agitation time- 60 min, biosorbent dose- 0.3 g/100 mL of liquid phase, speed- 200 rpm at room temperature and 50 mg/L initial concentration. The transfer functions in the artificial network model were linear and tangent sigmoid for output and hidden layers, respectively where eight neurons presented a good description for results of batch experiments with coefficient of correlation >0.997. The analysis of sensitivity proved that the initial pH of solution had more influence in the present sorption process with relative importance of 34%. The outputs of artificial network were integrated with the solute transport equation for description the propagation of lead ions through fixed bed column packed with olive pips. For breakthrough curves, a satisfactory concurrence was observed between outputs of model and measured concentrations because Willmott's index was >0.97.

Landfill area estimation based on solid waste collection prediction using ANN model and final waste disposal options

Public health of inhabitants has been affected by the increase of unsound waste management in the cities of developing countries. Solid waste management has received extensive attention from the municipalities. A sustainable plan and design of solid waste management system of a city necessitate accurate forecasting of the solid waste generated and collected by the city authorities in the landfill for final disposal and other renewable energy options. In the study, an Artificial Neural Network (ANN) surrogate model was used to predict solid waste collected from the year 2012–2016 at Matuail landfill site of Dhaka South City Corporation (DSCC). 120 monthly solid waste quantity and vehicle trip number getting from weighbridge were used as inputs data into the model. 70% of the data used for the model training, 15% of the data used for validation and 15% of that used for testing. The remaining 60 monthly waste quantities are used as output to develop the model. Feed-forward back propagation neural network was used with the hyperbolic tangent sigmoid activation function and the Levenberg–Marquardt optimization method. The model with 2-5-1-1 topology is selected as the best topology based on the performance metrics i.e., the minimum value of MSE and high value of regression. The ANN based solid waste forecasting model performed to be promising using the available weighbridge data with the coefficient of determination (R2) for training and testing 0.85 and 0.86. The developed model can be alternatively used successfully with weight bridge software in the landfill to efficiently forecast solid waste collection, particularly that in the countries with a similar demographic and social environment. Considering other proposed alternative disposal options and waste characterization the required landfill area are estimated and found that the landfill authority can save the valuable urban landfill area requirement up to 28.6%. The result shows that the innovative proposed method of landfill area estimation by ANN and final disposal methods can be used alternatively that helps for better planning and management of the landfill site.

Biomass ashes as potent adsorbent for pesticide: prediction of adsorption capacity by artificial neural network

Biomass ashes are used for adsorption of herbicides from aqueous solution. A relationship between physicochemical properties of biomass ashes such as carbon–hydrogen–nitrogen content (CHN analysis), silica content and BET surface area with their adsorption capacity was established and modeled using artificial neural network. 2,4-Dichlorophenoxyacetic acid (2,4-D) a commonly used herbicide is chosen a representative for this study. The artificial neural network model was trained, validated and tested using 35 data sets and was equipped with nine neuron hidden layers having tansig (tangent sigmoid) transfer function and an output layer with purelin (purely linear) transfer function. This model can be used to predict 2,4-D removal efficacy of any biomass ash by knowing its physicochemical properties like C, H, N, Si and BET surface area.