Modeliranje adsorpcije metana, dušika, ugljikova dioksida te njihovih binarnih i ternarnih smjesa na aktivnim ugljenima pomoću umjetne neuronske mreže

This work examines the use of neural networks in modelling the adsorption process of gas mixtures (CO2, CH4, and N2) on different activated carbons. Seven feed-forward neural network models, characterized by different structures, were constructed with the aim of predicting the adsorption of gas mixtures. A set of 417, 625, 143, 87, 64, 64, and 40 data points for NN1 to NN7, respectively, were used to test the neural networks. Of the total data, 60 %, 20 %, and 20 % were used, respectively, for training, validation, and testing of the seven models. Results show a good fit between the predicted and experimental values for each model; good correlations were found (R = 0.99656 for NN1, R = 0.99284 for NN2, R = 0.99388 for NN3, R = 0.99639 for Q1 for NN4, R = 0.99472 for Q2 for NN4, R = 0.99716 for Q1 for NN5, R = 0.99752 for Q3 for NN5, R = 0.99746 for Q2 for NN6, R = 0.99783 for Q3 for NN6, R = 0.9946 for Q1 for NN7, R = 0.99089 for Q2 for NN7, and R = 0.9947 for Q3 for NN7). Moreover, the comparison between the predicted results and the classical models (Gibbs model, Generalized dual-site Langmuir model, and Ideal Adsorption Solution Theory) shows that the neural network models gave far better results. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom radu ispitana je primjena neuronskih mreža u modeliranju procesa adsorpcije smjese plinova (CO2, CH4 i N2) na različitim aktivnim ugljicima. Izrađeno je sedam modela neuronskih mreža, karakteriziranih različitim strukturama s ciljem predviđanja adsorpcije smjesa plinova. Za testiranje neuronskih mreža primijenjen je skup od 417, 625, 143, 87, 64, 64 i 40 podatkovnih točaka za NN1 do NN7. Od ukupnih podataka 60 %, 20 % i 20 % rabljeno je za obuku, validaciju i testiranje sedam modela. Rezultati pokazuju dobar odnos predviđenih i eksperimentalnih vrijednosti za svaki model; pronađene su dobre korelacije (R = 0,99656 za NN1, R = 0,99284 za NN2, R = 0,99388 za NN3, R = 0,99639 za Q1 za NN4, R = 0,99472 za Q2 za NN4, R = 0,99716 za Q1 za NN5, R = 0,99972 za Q3 za NN5, R = 0,99746 za Q2 za NN6, R = 0,99783 za Q3 za NN6, R = 0,9946 za Q1 za NN7, R = 0,99089 za Q2 za NN7 i R = 0,9947 za Q3 za NN7). Dodatno, usporedba predviđenih rezultata i klasičnih modela (Gibbsov model, generalizirani Langmuirov model i teorija idealne adsorpcije otopine) pokazuje da su modeli neuronskih mreža dali daleko bolje rezultate. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

An artificial intelligence approach for modeling the rejection of anti-inflammatory drugs by nanofiltration and reverse osmosis membranes using kernel support vector machine and neural networks

The rejection of anti-inflammatory drugs by membranes has shown paramount importance in separation membrane processes such as nanofiltration and reverse osmosis (NF/RO) membranes for pharmaceutical industries. Therefore, the main objective of this paper is to use support vector machine (SVM) and artificial neural network (ANN) to model the rejections of anti-inflammatory drugs by NF/RO membranes using 300 experimental data points gathered from the literature. Both approaches (ANN and SVM) gave close results with a slight superiority of the neural networks model demonstrated by its correlation coefficient (R) and root mean square error (RMSE) values of 0.9930 and 1.8094% respectively, in contrast to 0.9900 and 2.2355% for SVM. Sensitivity analysis by the weight method demonstrates that the most relevant variables that influence the rejection of anti-inflammatory drugs are: effective diameter of an organic compound in water “$d_{{c}}$”, molecular length, contact angle, and zeta potential. These input relevant variables have a significant contribution (relative importance superior to 10%)

An artificial intelligence approach for modeling the rejection of anti-inflammatory drugs by nanofiltration and reverse osmosis membranes using kernel support vector machine and neural networks

The rejection of anti-inflammatory drugs by membranes has shown paramount importance in separation membrane processes such as nanofiltration and reverse osmosis (NF/RO) membranes for pharmaceutical industries. Therefore, the main objective of this paper is to use support vector machine (SVM) and artificial neural network (ANN) to model the rejections of anti-inflammatory drugs by NF/RO membranes using 300 experimental data points gathered from the literature. Both approaches (ANN and SVM) gave close results with a slight superiority of the neural networks model demonstrated by its correlation coefficient (R) and root mean square error (RMSE) values of 0.9930 and 1.8094% respectively, in contrast to 0.9900 and 2.2355% for SVM. Sensitivity analysis by the weight method demonstrates that the most relevant variables that influence the rejection of anti-inflammatory drugs are: effective diameter of an organic compound in water “$d_{{c}}$”, molecular length, contact angle, and zeta potential. These input relevant variables have a significant contribution (relative importance superior to 10%)

QSPR studije karbonilnih, hidroksilnih, polienskih indeksa i prosječne molekulske težine polimera pod fotostabilizacijom pristupom ANN i MLR

One of the main disadvantages of the use of synthetic or semi-synthetic polymeric materials is their degradation and aging. The purpose of this study was to use artificial neural networks (ANN) and multiple linear regressions (MLR) to predict the carbonyl, hydroxyl, and polyene indices (ICO, IOH, and IOP), and viscosity average molecular weight (MV) of poly(vinyl chloride), polystyrene, and poly(methyl methacrylate). These physicochemical properties are considered fundamental during the study of photostabilization of polymers. From the five repeating units of monomers, the structure of the polymer studied is shown. Quantitative structure-property relationship (QSPR) models obtained by using relevant descriptors showed good predictability. Internal validation {R2, RMSE, and Q2LOO}, external validation {R2, RMSE, Q2pred, rm2, Δrm2, k, and k’}, and applicability domain were used to validate these models. The comparison of the results shows that the ANN models are more efficient than those of the MLR models. Accordingly, the QSPR model developed in this study provides excellent predictions, and can be used to predict ICO, IOH, IOP, and MV of polymers, particularly for those that have not been tested. This work is licensed under a Creative Commons Attribution 4.0 International License.Jedan od glavnih nedostataka upotrebe sintetičkih ili polusintetičkih polimernih materijala je njihova razgradnja i starenje. Svrha ove studije je primjena umjetnih neuronskih mreža (ANN) i višestrukih linearnih regresija (MLR) za predviđanje karbonilnih, hidroksilnih i polienskih indeksa (ICO, IOH i IOP) i prosječne molekulske mase viskoznosti (MV) poli(vinil-klorida), polistirena i poli(metil metakrilata). Ta fizikalno-kemijska svojstva smatraju se važnim tijekom proučavanja fotostabilizacije polimera. Iz pet ponavljajućih jedinica monomera prikazana je struktura ispitivanog polimera. Kvantitativni modeli odnosa strukture-svojstava (QSPR) dobiveni primjenom relevantnih deskriptora pokazali su dobru predvidljivost. Za potvrdu tih modela provedene su: interna provjera {R2, RMSE i Q2LOO}, vanjska provjera {R2, RMSE, Q2pred, rm2, Δrm2, k i k’} i domena primjenjivosti. Usporedba rezultata pokazuje da su modeli ANN učinkovitiji od modela MLR. Prema tome, model QSPR razvijen u ovoj studiji pruža izvrsna predviđanja i može se primjenjivati za predviđanje ICO, IOH, IOP i MV polimera, posebno za one koji nisu testirani. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, Domain of Application and Prediction

International audienceQuantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q2ext and the Root Mean Square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides

Predviđanje klimatskih parametara iz fizikalno-kemijskih parametara pomoću umjetnih neuronskih mreža: studija slučaja Ain Defla (Alžir)

The knowledge of the climate of a region is a primordial task in that it allows predictions of climatic parameters in the future. In this study, monthly maximum and minimum air temperature (Tair,min, Tair,max), relative humidity (RH), and sunshine duration (SD) were modelled by multiple linear regression (MLR), and multilayer perceptron methods (MLP). For the four climatic parameters, the internal and external validations of MLP-ANN model showed high R2 and Q2 values in the range 0.81–0.98. The agreement between calculated and experimental values confirmed the ability of ANN-based equation to predict these parameters quickly and at lower cost. This work is licensed under a Creative Commons Attribution 4.0 International License.Poznavanje klime neke regije osnovni je zadatak jer omogućuje predviđanje klimatskih parametara u budućnosti. U ovom su istraživanju maksimalna i minimalna mjesečna temperatura zraka (Tair,min, Tair,max), relativna vlažnost (RH) i trajanje sunčeve svjetlosti (SD) modelirani višestrukom linearnom regresijom (MLR) i višeslojnim perceptronskim metodama (MLP). Za četiri klimatska parametra interna i eksterna validacija modela MLP-ANN pokazala je visoke vrijednosti R2 i Q2 u području 0,81 – 0,98. Usklađenost izračunatih i eksperimentalnih vrijednosti potvrdilo je da jednadžba temeljena na ANN-u brzo i uz niže troškove predviđa te parametre. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Artificial Neural Network-Based Equation to Predict the Toxicity of Herbicides on Rats

International audienceThe use of herbicides is increasing around the world. The benefits achieved by the use of these herbicides are indisputable. Despite their importance in agriculture, herbicides can be dangerous to the environment and the human health, depending on their toxicity, and the degree of contamination. Also, it is essential and evident that the risk assessment of herbicides is an important task in the environmental protection. The objective of this work was to investigate and implement an Artificial Neural Network (ANN) model for the prediction of acute oral toxicity of 77 herbicides to rats. Internal and external validations of the model showed high Q2 and r m 2 - values, in the range 0.782 – 0.997 for the training and the test. In addition, the major contribution of the current work was to develop artificial neural network-based equation to predict the toxicity of 13 other herbicides; the mathematical equation using the weights of the network gave very significant results, leading to an R2 value of 0.959. The agreement between calculated and experimental values of acute toxicity confirmed the ability of ANN-based equation to predict the toxicity for herbicides that have not been tested as well as new herbicide