A review of artificial neural network techniques for environmental issues prediction

Journal of Thermal Analysis and Calorimetry - The smarter world needs more efforts to purposeful manage and usage of technologies, science, artificial intelligence, and artificial neural networks,...     

Simultaneous determination of antimony and bismuth by beta-correction spectrophotometry and an artificial neural network algorithm

Simultaneous determinations of antimony and bismuth were done by β-correction spectrophotometry and a feed forward neural network algorithm with back propagation of error. The sensitivity was improved using β-correction spectrophotometry. The determination of trace amounts of mixtures of Sb and Bi in various matrices (river, tap and industrial wastewater) were investigated by neural network and β-correction spectrophotometry using the complexes formed between pyrogallol red, Sb and Bi. The results showed that measurement is possible in the ranges of 0.05-5.0 and 0.2-3.2 μg ml⁻¹ for Sb(III) and Bi(III), respectively. The results also show very good agreement between true and predicted concentration values and have the ability to use in routine analysis.     

Evaluation of a mathematical model using experimental data and artificial neural network for prediction of gas separation

In recent times, membranes have found wide applications in gas separation processes. As most of the industrial membrane separation units use hollow fiber modules, having a proper model for simulating this type of membrane module is very useful in achieving guidelines for design and characterization of membrane separation units. In this study, a model based on Coker, Freeman, and Fleming＇s study was used for estimating the required membrane area. This model could simulate a multicomponent gas mixture separation by solving the governing differential mass balance equations with numerical methods. Results of the model were validated using some binary and multicomponent experimental data from the literature. Also, the artificial neural network （ANN） technique was applied to predict membrane gas separation behavior and the results of the ANN simulation were compared with the simulation results of the model and the experimental data. Good consistency between these results shows that ANN method can be successfully used for prediction of the separation behavior after suitable training of the network     

Single-step calibration, prediction and real samples data acquisition for artificial neural network using a CCD camera

An artificial neural network (ANN) model is developed for simultaneous determination of Al(III) and Fe(III) in alloys by using chrome azurol S (CAS) as the chromogenic reagent and CCD camera as the detection system. All calibration, prediction and real samples data were obtained by taking a single image. Experimental conditions were established to reduce interferences and increase sensitivity and selectivity in the analysis of Al(III) and Fe(III). In this way, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. Both Al(III) and Fe(III) can be determined in the concentration range of 0.25-4 μg ml⁻¹ with satisfactory accuracy and precision. The proposed method was also applied satisfactorily to the determination of considered metal ions in two synthetic alloys.     

Improving the accuracy of density-functional theory calculation: the genetic algorithm and neural network approach

The combination of genetic algorithm and neural network approach (GANN) has been developed to improve the calculation accuracy of density functional theory. As a demonstration, this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules. The neural network approach reduces the root-mean-square (rms) deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFTB3LYP6-31G(d) calculation, and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV.     

Fluorescence optosensor using an artificial neural network for screening of polycyclic aromatic hydrocarbons

This paper presents an optosensor for screening of four polycyclic aromatic hydrocarbons: anthracene (ANT), benzo[a]pyrene (BaP), fluoranthene (FLT), and benzo[b]fluoranthene (Bbf) using a photomultiplier device with an artificial neural network as transducer. The optosensor is based on the on-line immobilization on a non-ionic resin (Amberlite XAD-4) solid support in a continuous flow. The determination was performed in 15 mM H₂PO₄⁻/HPO₄²⁻ buffer solution at pH 7 and 25% of 1,4-dioxane. Feed forward neural networks (multiplayer perceptron) have been trained to quantify the considered Polycyclic aromatic hydrocarbons (PAHs) in mixtures under optimal conditions. The optosensor proposed was also applied satisfactorily to the determination of the considered PAHs in water samples in presence of the other 12 EPA–PAHs.     

An artificial neural network approach for the prediction of dynamic viscosity of MXene-palm oil nanofluid using experimental data

Journal of Thermal Analysis and Calorimetry - Given the excellent thermal properties of MXene, MXene nanomaterials-based nanofluids may have the potential of being used as heat transfer fluids. In...     

Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network

Journal of Thermal Analysis and Calorimetry - Nanofluids are employed in different thermal devices due to their enhanced thermophysical features which lead to noticeable heat transfer augmentation....     

Application of neural network calibrations to an halide ISE array

The Neural Network (NN) technique was applied to the calibration of an ion selective electrode (ISE) array comprising a bromide selective electrode, two chloride ISEs and one thiocyanate ISE. The measured samples were synthetic mixture solutions of chlorides and bromides in concentration ranges such that interference occurs. The NN method allowed to perform the calibration without estimating the coefficients of the Nikolskii-Eisenman theoretical relation. Only the determination of bromide was detailed. The results obtained using this method were better than those obtained using linear multivariate calibration methods.     

Artificial neural network for aspect ratio prediction of lignocellulosic micro/nanofibers

Cellulose - In this work a wide sample analysis, under similar conditions, has been carried out and a calibration strategy based on a careful selection of input variables combined with sensitivity...     

Application of an artificial neural network (ANN) and piezoelectric chemical sensor array for identification of volatile organic compounds

A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η=0.15), momentum term (μ=0.9), and the sigmoid parameter (β=1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds.     

Local and global quantitative structure-activity relationship modeling and prediction for the baseline toxicity

The predictive accuracy of the model is of the most concern for computational chemists in quantitative structure-activity relationship (QSAR) investigations. It is hypothesized that the model based on analogical chemicals will exhibit better predictive performance than that derived from diverse compounds. This paper develops a novel scheme called "clustering first, and then modeling" to build local QSAR models for the subsets resulted from clustering of the training set according to structural similarity. For validation and prediction, the validation set and test set were first classified into the corresponding subsets just as those of the training set, and then the prediction was performed by the relevant local model for each subset. This approach was validated on two independent data sets by local modeling and prediction of the baseline toxicity for the fathead minnow. In this process, hierarchical clustering was employed for cluster analysis, k-nearest neighbor for classification, and partial least squares for the model generation. The statistical results indicated that the predictive performances of the local models based on the subsets were much superior to those of the global model based on the whole training set, which was consistent with the hypothesis. This approach proposed here is promising for extension to QSAR modeling for various physicochemical properties, biological activities, and toxicities.