Usporedba modela “neuronskih mreža i višestrukih linearnih regresija” za opisivanje odbacivanja mikroonečišćivala membranama

A rejection process of organic compounds by nanofiltration and reverse osmosis membranes was modelled using the artificial neural networks. Three feed-forward neural networks based on quantitative structure-activity relationship (QSAR-NN models) characterised by a similar structure (twelve neurons for QSAR-NN1, QSAR-NN2, and QSAR-NN3 in the input layer, one hidden layer and one neuron in the output layer), were constructed with the aim of predicting the rejection of organic compounds. A set of 1394 data points for QSAR-NN1, 980 data points for QSAR-NN2, and 436 data points for QSAR-NN3 were used to construct the neural networks. Good agreements between the predicted and experimental rejections were obtained by QSAR-NN models (the correlation coefficient for the total dataset were 0.9191 for QSAR-NN1, 0.9338 for QSAR-NN2, and 0.9709 for QSAR-NN3). Comparison between the feed-forward neural networks and multiple linear regressions based on quantitative structure-activity relationship “QSAR-MLR” revealed the superiority of the QSAR-NN models (the root mean squared errors for the total dataset for the QSAR-NN models were 10.6517 % for QSAR-NN1, 9.1991 % for QSAR-NN2, and 5.8869 % for QSAR-NN3, and for QSAR-MLR models they were 20.1865 % for QSAR-MLR1, 19.3815 % for QSAR-MLR2, and 16.2062 % for QSAR-MLR3). This work is licensed under a Creative Commons Attribution 4.0 International License.Postupak odbacivanja organskih spojeva nanofiltracijom i membranama reverzne osmoze modeliran je umjetnim neuronskim mrežama. Konstruirane su tri neuronske mreže zasnovane na kvantitativnom odnosu strukture-aktivnosti (QSAR-NN modeli) karakterizirane sličnom strukturom (dvanaest neurona za QSAR-NN1, QSAR-NN2 i QSAR-NN3 u ulaznom sloju, jedan skriveni sloj i jedan neuron u izlaznom sloju), s ciljem predviđanja odbacivanja organskih spojeva. Za izgradnju neuronskih mreža upotrijebljeni su skupovi od 1394 podatkovnih točaka za QSAR-NN1, 980 podatkovnih točaka za QSAR-NN2 i 436 podatkovnih točaka za QSAR-NN3. Dobre usklađenosti između predviđenih i eksperimentalnih odbacivanja dobivene su modelima QSAR-NN (korelacijski koeficijent za ukupni skup podataka bio je 0,9191 za QSAR-NN1, 0,9338 za QSAR-NN2 i 0,9709 za QSAR-NN3). Usporedba neuronskih mreža i višestrukih linearnih regresija zasnovanih na kvantitativnom odnosu struktura-aktivnost “QSAR-MLR” otkrila je superiornost modela QSAR-NN (korijenske srednje kvadratne pogreške za ukupni skup podataka za modele QSAR-NN bile su 10,6517 % za QSAR-NN1, 9,1991 % za QSAR-NN2, i 5,8869 % za QSAR-NN3, a za modele QSAR-MLR 20,1865 % za QSAR-MLR1, 19,3815 % za QSAR-MLR2, i 16,2062 % za QSAR-MLR3). Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study

The global solar radiation prediction is the most necessary part of the project and performance of solar energy applications. The objective of the present work is to predict global solar radiation (GSR) received on the horizontal surface using an artificial neural network (ANN). For the city (Relizane) in the west region of Algeria. The inputs used in the neural network are: time (h), day, month, year, temperature (k), relative humidity (%), pressure (mbar), wind speed (m/s), wind direction (°), and rainfall (kg/m2). The neural network-optimal model was trained and tested using 80 %, and 20 % of whole data, respectively. The best results were obtained with the structure 10-25-1 (10 inputs, 25 hidden, and 1 output neurons) presented an excellent agreement between the calculated and the experimental data during the test stage with a correlation coefficient of R = 0.9879, root means squared error of RMSE = 47.7192 (Wh/m2), mean absolute error MAE = 27.7397 (Wh/m2), and mean squared error MSE = 2.2771e+03(Wh/m2), considering a three-layer Feedforward neural network with Regularization Bayesienne (trainbr)  training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. The results demonstrate proper ANN’s predictions with a root mean square error (RMSE) of less than 0.50 (Wh/m2) and coefficient of correlation (R) higher than 0.98, which can be considered very acceptable. This model can be used for designing solar energy systems in the hottest regions

Metoda potpornih vektora u procjeni utjecaja karakteristika unaprijednih osmotskih membrana na zadržavanje organskih molekula

The forward osmosis (FO) process is currently being studied more despite other energy-consuming processes. In addition, several works show the performance of FO membranes as its major challenges, the study of the rejection of different molecules, energy consumption, and modelling of different objectives related to this process. The main purpose of our study was to evaluate the impact of the FO membranes characteristics on the rejection of organic molecules (neutral) by modelling of the latter. However, the current work deals with the application of Support Vector Machines (SVM) for predicting the rejection of organic molecules (53) by the FO membranes. In addition, the SVM model was compared with two other models: Artificial Neural Network (ANN) and Multiple Linear Regression (MLR). The coefficient of correlation (R) for the testing data was applied to display the best SVM model. The SVM model generated with Radial Basis Function (RBF) as the kernel function showed the best R value equal to 0.8526. MLR and ANN models had R values of 0.7630 and 0.8723, respectively.Proces unaprijedne osmoze (FO) trenutačno se učestalo proučava, a glavne su tematike zadržavanje različitih molekula, potrošnja energije i modeliranje samog procesa. Glavna svrha ovog istraživanja bila je, primjenom modeliranja, procijeniti utjecaj karakteristika FO membrana na zadržavanje neutralnih organskih molekula. Rad je fokusiran na primjenu metode potpornih vektora (engl. Support Vector Machines, SVM) za predviđanje zadržavanja organskih molekula (53) FO membranama. Razvijeni SVM model uspoređen je s dva druga modela: modelom umjetne neuronske mreže i modelom višestruke linearne regresije. SVM model generiran uz radijalnu baznu funkciju pokazao je najbolju vrijednost koeficijenta korelacije u iznosu 0,8526. Vrijednosti koeficijenta korelacije kod modela umjetne neuronske mreže i modela višestruke linearne regresije iznosile su 0,7630, odnosno 0,8723

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

Strojno učenje i neuronske mreže u modeliranju zadržavanja polarnih farmaceutski aktivnih tvari nanofiltracijom i reverznom osmozom

The retention of polar pharmaceutical active compounds (PPhACs) by nanofiltration and reverse osmosis (NF/RO) membranes is of paramount importance in membrane separation processes. The retention of 21 PPhACs was correlated using artificial intelligence techniques: multi-layer perceptron (MLP), feedforward neural network with radial basis function (RBF), and support vector machine (SVM). A database of 541 retention values has been collected from the literature. The results showed a high predictive capacity of the MLP model for the retention of PPhACs by NF/RO with a very high correlation coefficient (R = 0.9714) and a very low root mean squared error (RMSE = 3.9139 %) for the entire data set. The comparison between the three models showed the superiority of the MLP model. The sensitivity analysis emphasised that the retention of PPhACs is governed by three interactions arranged in descending order: polarity interactions (hydrophobicity/hydrophilicity), electrostatic repulsion, and steric hindrance. This research suggests that the PPhACs retention on the NF/RO membrane strongly depends on the topological polar surface area.Zadržavanje polarnih farmaceutski aktivnih tvari (PPhAC) tijekom nanofiltracije i reverzne osmoze (NF/RO) od iznimne je važnosti u membranskim separacijskim procesima. Membransko zadržavanje 21 PPhAC-a korelirano je sa svojstvima PPhAC-a, karakteristikama membrane i uvjetima provedbe procesa filtracije. Pri tome su primijenjene tehnike umjetne inteligencije: višeslojni perceptron (MLP), neuronska mreža s radijalnom baznom funkcijom (RBF) i metoda potpornih vektora (SVM). Iz literature je prikupljena 541 vrijednost zadržavanja. Rezultati su pokazali visok kapacitet predviđanja MLP modela za cijeli skup podataka, s vrlo visokom vrijednošću koeficijenta korelacije (R = 0,9714) i vrlo niskom vrijednošću korijena srednje kvadratne pogreške (RMSE = 3,9139 %). Usporedba s preostala dva modela (RBF i SVM) pokazala je superiornost MLP modela. Analiza osjetljivosti ukazala je na to da zadržavanjem PPhAC-a upravljaju tri interakcije i to (padajućim redoslijedom): polarne interakcije (hidrofobnost/hidrofilnost), elektrostatsko odbijanje i steričke smetnje. Provedenoo istraživanje sugerira da zadržavanje PPhACs na NF/RO membrani snažno ovisi o topologiji polarne površine

Primjena umjetne neuronske mreže i regresije potpornih vektora u modeliranju kvantitativnog odnosa strukture-svojstva i topljivosti otopljenih čvrstih tvari u superkritičnom CO2

In this study, the solubility of 145 solid solutes in supercritical CO2 (scCO2) was correlated using computational intelligence techniques based on Quantitative Structure-Property Relationship (QSPR) models. A database of 3637 solubility values has been collected from previously published papers. Dragon software was used to calculate molecular descriptors of 145 solid systems. The genetic algorithm (GA) was implemented to optimise the subset of the significantly contributed descriptors. The overall average absolute relative deviation MAARD of about 1.345 % between experimental and calculated values by support vector regress SVR-QSPR model was obtained to predict the solubility of 145 solid solutes in supercritical CO2, which is better than that obtained using ANN-QSPR model of 2.772 %. The results show that the developed SVR-QSPR model is more accurate and can be used as an alternative powerful modelling tool for QSAR studies of the solubility of solid solutes in supercritical carbon dioxide (scCO2). The accuracy of the proposed model was evaluated using statistical analysis by comparing the results with other models reported in the literature. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom je istraživanju korelirana topljivost 145 čvrstih otopljenih tvari u superkritičnom CO2 (scCO2) primjenom tehnika računalne inteligencije zasnovanim na modelima kvantitativne strukture i svojstva (QSPR). Baza podataka 3637 topljivosti prikupljena je iz prethodno objavljenih radova. Program Dragon primijenjen je za izračunavanje molekularnih deskriptora 145 čvrstih sustava. Genetski algoritam (GA) implementiran je kako bi se optimizirao podskup deskriptora sa značajnim doprinosom. Ukupno prosječno apsolutno relativno odstupanje MAARD od oko 1,345 % između eksperimentalnih i izračunatih vrijednosti pomoću regresije potpornih vektora modelom SVR-QSPR dobiveno je za predviđanje topljivosti 145 čvrstih otopljenih tvari u superkritičnom CO2, što je bolje od onog dobivenog primjenom modela ANN-QSPR (2,772 %). Rezultati pokazuju da je razvijeni model SVR-QSPR precizniji i da se može primijeniti kao alternativni alat za modeliranje QSAR studija topljivosti otopljenih čvrstih tvari u superkritičnom ugljikovu dioksidu (scCO2). Točnost predloženog modela procijenjena je statističkom analizom uspoređivanjem rezultata s ostalim modelima zabilježenim u literaturi. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Modeliranje vremena sušenja praha Candesartan Cilexetil primjenom tehnike računalne inteligencije

The aim of this work was to use two computational intelligence techniques, namely, artificial neural network (ANN) and support vector regression (SVR), to model the drying time of a pharmaceutical powder Candesartan Cilexetil, which is used for arterial hypertension treatment and heart failure. The experimental data set used in this work has been collected from previously published paper of the drying kinetics of Candesartan Cilexetil using vacuum dryer and under different operating conditions. The comparison between the two models has been conducted using different statistical parameters namely root mean squared error (RMSE) and determination coefficient (R2). Results show that SVR model shows high accuracy in comparison with ANN model to predict the non-linear behaviour of the drying time using pertinent variables with {R2 = 0.9991, RMSE = 0.262} against {R2 = 0.998, RMSE = 0.339} for SVR and ANN, respectively. This work is licensed under a Creative Commons Attribution 4.0 International License.Cilj ovog rada bio je primjena dvije tehnike računalne inteligencije (umjetne neuronske mreže (ANN) i regresije potpornih vektora (SVR)) za modeliranje vremena sušenja farmaceutskog praha Candesartan Cilexetil, koji se primjenjuje za liječenje arterijske hipertenzije i zatajenje srca. Eksperimentalni skup podataka korišten u ovom radu prikupljen je iz prethodno objavljenog rada o kinetici sušenja Candesartan Cilexetila pomoću vakuumskog sušionika i pod različitim radnim uvjetima. Usporedba između dva modela provedena je pomoću različitih statističkih parametara, odnosno korijenom srednje kvadratne pogreške (RMSE) i koeficijenta određivanja (R2). Rezultati su pokazali da u usporedbi s modelom ANN model SVR pokazuje visoku točnost za predviđanje nelinearnog ponašanja vremena sušenja koristeći odgovarajuće varijable {R2 = 0,9991, RMSE = 0,262} u odnosu na {R2 = 0,998, RMSE = 0,339} za SVR i ANN. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Umjetna inteligencija i matematičko modeliranje kinetike sušenja farmaceutskog praha

The study aims at modelling the drying kinetics of a pharmaceutical powder with active ingredient Candesartan Cilexetil. The kinetics was carried out in a vacuum dryer at different temperature levels, pressure, initial mass, and water content. The effect of some operating parameters on the drying time was studied. The modelling of drying times was based on the use of experimental design method. The data obtained were adjusted using 17 semi-empirical models, one proposed, a static ANN and DA_SVMR, regrouping all studied kinetics. The proposed model and DA_SVMR model were chosen as the most appropriate to describe the drying kinetics. This work is licensed under a Creative Commons Attribution 4.0 International License.Cilj rada je modeliranje kinetike sušenja farmaceutskog praha s aktivnim sastojkom Candesartan Cilexetil. Kinetika je izvedena u vakuumskoj sušilici pri različitim temperaturama, tlaku, početnoj masi i sadržaju vode. Proučavan je utjecaj nekih radnih parametara na vrijeme sušenja. Modeliranje vremena sušenja temeljilo se na primjeni eksperimentalne metode dizajna. Dobiveni podatci prilagođeni su pomoću 17 poluempirijskih modela, jednog predloženog, statičkog ANN i DA_SVMR, pregrupirajući svu proučavanu kinetiku. Predloženi model i model DA_SVMR pokazali su se kao najprikladniji za opisivanje kinetike sušenja. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Modeliranje sadržaja vlage i brzine sušenja određenih plodova solarnim sušenjem primjenom ANN-a

The aim of this work was to model the moisture content (MC) and drying rate (DR) using artificial neural network (ANN) methodology. Many architectures have been tested and the best topology was selected based on a trial and error method. The dataset was randomly divided into 60, 20, and 20 % for training, test, and validation stage of the ANN model, respectively. The best topology was 10-{29-13}-2 obtained with high correlation coefficient R (%) of {99.98, 98.41} and low root mean square error RMSE (%) (0.36, 6.29) for MC and DR, respectively. The obtained ANN can be used to interpolate the MC and DR with high accuracy. This work is licensed under a Creative Commons Attribution 4.0 International License.Cilj ovog rada bio je modelirati sadržaj vlage (MC) i brzinu sušenja (DR) primjenom metodologije umjetne neuronske mreže (ANN). Testirane su mnoge arhitekture, a najbolja topologija odabrana je na temelju metode pokušaja i pogrešaka. Skup podataka podijeljen je nasumično na 60, 20 i 20 % za fazu treninga, testa i validacije ANN modela. Najbolja topologija bila je 10-{29-13}-2 dobivena visokim koeficijentom korelacije R (%) od {99,98, 98,41} i niskom srednjom kvadratnom pogreškom RMSE (%) (0,36, 6,29) za MC, odnosno DR. Dobiveni ANN model može se s velikom točnošću primijeniti za interpolaciju MC-a i DR-a. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna