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

Novi pristup u predviđanju izravnog i otvorenog solarnog sušenja pomoću umjetne neuronske mreže za ljekovito bilje

In this study, an artificial neural network (ANN) was developed to obtain a generalized model for predicting the direct and open sun drying process for some medicinal plants. Since the quality of the experimental dataset can lead to a very performant model, in this study the dataset was collected from previously published papers and divided randomly into three subsets, namely 70 %, 15 %, and 15 % for training, testing, and validation. Based on the complex solar drying behaviour, ten parameters were considered as inputs: time, global solar radiation (GSR), outside temperature, inclination, emissivity, altitude, longitude, latitude, inside temperature, and nutritional value, to predict moisture content (MC), and drying rate (DR). Based on a trial and error method, the best ANN model was found with a topology of 10-28-14-2, with regression coefficient and root mean square error of (R = 97.044 %. RMSE = 4.589 %) and (R = 99.968 %, RMSE = 1.185 %) for MC and DR, respectively. It can be concluded that the obtained ANN model provides the best method for solar dryer modelling which can be generalized for any location in the world. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom istraživanju razvijena je umjetna neuronska mreža (ANN) za dobivanje uopćenog modela za predviđanje izravnog i otvorenog postupka solarnog sušenja za određene ljekovite biljke. Budući da kvaliteta eksperimentalnog skupa podataka može dovesti do modela visoke izvedbe, u ovoj je studiji skup podataka prikupljen iz prethodno objavljenih radova i nasumce podijeljen u tri podskupine – 70 % za trening, 15 % za testiranje i 15 % za validaciju. Na temelju složenog postupka solarnog sušenja, za predviđanje sadržaja vlage (SV) i brzine sušenja (BS) uzima se deset ulaznih parametara: vrijeme, globalno sunčevo zračenje (GSZ), vanjska temperatura, nagib, emisivnost, nadmorska visina, zemljopisna dužina, zemljopisna širina, unutarnja temperatura i hranjiva vrijednost. Na temelju metode pokušaja i pogreške, pronađen je najbolji model ANN s topologijom 10-28-14-2 te koeficijentom regresije i srednjom kvadratnom pogreškom od (R = 97,044 %, RMSE = 4,589 %) za SV i (R = 99,968 %, RMSE = 1,185 %) za BS. Može se zaključiti da je dobiveni ANN najbolji model za modeliranje solarnih sušilica koji se može generalizirati za bilo koje mjesto na svijetu. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

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

Proces sušenja kompozita cementne žbuke ojačane celuloznim vlaknima: eksperiment i matematičko modeliranje

In this paper, six novel mathematical models based on semi-empirical calculus are proposed and applied to characterise the oven-drying process of cement mortar composites reinforced with cellulosic fibres (CMCRCFs). The drying experiments were carried out on four levels of oven-drying temperatures (70, 85, 105, and 120 °C), with four different cellulosic fibres content (0, 5, 10, and 20 %). Obtained results were compared to those derived by regression analysis of six most typically used mathematical drying models (Newton, Page, Page modified1, Page modified2, Handerson Pabis, and Logarithmic) in addition to six proposed models. The regression accuracy of the drying process was evaluated by the coefficient of determination (R2), low mean square error (MSE), low root mean squared error (RMSE), and mean absolute error (MAE). Additional criteria were used to ensure more validity of the selected models. The obtained values indicate a highly accurate fit of the proposed model MR9, meaning that the proposed model can clearly interpret the experimental drying data and predict the dry state of CMCRCFs.U ovom radu predloženo je i primijenjeno šest novih matematičkih modela temeljenih na poluempirijskom proračunu za karakterizaciju procesa sušenja u pećnici kompozita cementne žbuke ojačane celuloznim vlaknima (CMCRCF). Pokusi sušenja provedeni su pri četiri razine temperature sušenja u pećnici (70, 85, 105 i 120 °C) s četiri različita udjela celuloznih vlakana (0, 5, 10 i 20 %). Dobiveni rezultati uspoređeni su s onima dobivenim regresijskom analizom šest najčešće primjenjivanih matematičkih modela sušenja (Newton, Page, Page modified1, Page modified2, Handerson Pabis i Logarithmic) uz šest predloženih modela. Regresijska točnost procesa sušenja procijenjena je koeficijentom determinacije (R2), srednjom kvadratnom pogreškom (MSE), korijenom srednje kvadratne pogreške (RMSE) i srednjom apsolutnom pogreškom (MAE). Primijenjeni su i dodatni kriteriji da bi se osigurala veća valjanost odabranih modela. Dobivene vrijednosti pokazuju dobro slaganje predloženog modela MR9 s eksperimentalnim vrijednostima, što znači da predloženi model može jasno interpretirati eksperimentalne podatke o sušenju i predvidjeti suho stanje CMCRCF-a

Modeliranje kinetike sušenja jabuke (sorta Golab): Frakcijski račun u odnosu na poluempirijske modele

In this work, two novel models have been proposed based on semi-empirical and factional calculus incorporating non-integer time derivatives in the Fick’s first law of anomalous diffusion. The experimental data has been collected from literature of 15 kinetics investigated in a convective dryer under the effect of temperatures ranging from 40 to 80 °C at 10 °C interval, and thickness of the slices of 2 to 6 mm at 2 mm interval. The collected experimental dataset was of apple slices (Golab variety). Results from this study were compared with a set of 64 thin-layer drying models previously published in the literature. The fitting capability of the models was compared using the mean of root mean square error MRMSE (%) of all kinetics and the global determination coefficient R2. All models’ constants and coefficients were optimised by dragonfly algorithm programmed in MATLAB software. Results show that the fractional model is highly capable of describing the drying curve of the apple slices with a determination coefficient (R2) of 0.99981, and average root mean square error (MRMSE) of 0.43 % in comparison to the best empirical models with R2 of 0.99968 and MRMSE of 0.61 %. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom radu predložena su dva nova modela temeljena na poluempirijskom i frakcijskom računu koji uključuje necjelobrojne vremenske derivate u Fickovom prvom zakonu anomalne difuzije. Eksperimentalni podatci o 15 kinetika istraženih u konvektivnom sušioniku pod utjecajem temperatura u rasponu od 40 do 80 °C u razmaku od 10 °C i debljine kriški od 2 do 6 mm u razmaku od 2 mm prikupljeni su iz literature. Prikupljeni eksperimentalni skup podataka bio je na kriškama jabuke (sorta Golab). Rezultati ove studije uspoređivani su s nizom od 64 modela tankoslojnog sušenja koji su prethodno objavljeni u literaturi. Sposobnost uklapanja modela uspoređena je koristeći srednju vrijednost srednje kvadratne pogreške MRMSE (%) svih kinetika i globalni koeficijent određivanja R2. Konstante i koeficijenti svih modela optimizirani su algoritmom dragonfly programiranim u softveru MATLAB. Rezultati pokazuju da je frakcijski model visoko sposoban opisati krivulju sušenja kriški jabuke s koeficijentom utvrđivanja (R2) 0,99981 i prosječnom srednjom kvadratnom pogreškom (MRMSE) 0,43 % u usporedbi s najboljim empirijskim modelima s R2 0,99968 i MRMSE 0,61 %. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Modeli umjetne neuronske mreže za predviđanje gustoće i kinematičke viskoznosti različitih sustava biogoriva i njihovih mješavina s dizelskim gorivom. Usporedna analiza

In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15 pure systems, 14 binary systems, and 5 ternary systems) was used for the development of these models. These models use six inputs, which are temperature (T) in the range of −10 – 200 °C, volume fractions (X1, X2, X3) in the range of 0–1, and to distinguish these systems, we used kinematic viscosity at 20 °C in the range of 0.67–74.19 mm2 s–1 and density at 20 °C in the range of 0.7560–0.9188 g cm–3. The best results were obtained with the architecture of {6-26-2: 6 neurons in the input layer – 26 neurons in the hidden layer – 2 neurons in the output layer}. Results of comparison between experimental and simulated values in terms of the correlation coefficients were: R2 = 0.9965 for density, and R2 = 0.9938 for kinematic viscosity. A 238 new database experimental of 4 systems (2 pure systems, 1 binary system, and 1 ternary system) was used to check the accuracy of the two ANN models previously developed. Results of prediction performances in terms of the correlation coefficients were: R2 = 0.9980 for density, and R2 = 0.9653 for kinematic viscosity. Comparison of validation results with those of the other studies 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 članku dva modela zasnovana na metodologiji umjetne neuronske mreže (ANN) optimizirana su za predviđanje gustoće (ρ) i kinematičke viskoznosti (μ) različitih sustava biogoriva i njihovih mješavina s dizelskim gorivom. Za razvoj tih modela upotrijebljena je eksperimentalna baza podataka od 1025 točaka, uključujući 34 sustava (15 čistih sustava, 14 binarnih sustava i 5 ternarnih sustava). Ti modeli koriste šest ulaza: temperatura (T) u rasponu od −10 do 200 °C, volumni udjeli (X1, X2, X3) u rasponu 0 – 1, a za razlikovanje tih sustava korištena je kinematička viskoznost pri 20 °C u rasponu 0,67 – 74,19 mm2 s–1 i gustoća pri 20 °C u rasponu 0,7560 – 0,9188 g cm–3. Najbolji rezultati dobiveni su arhitekturom {6-26-2: 6 neurona u ulaznom sloju – 26 neurona u skrivenom sloju – 2 neurona u izlaznom sloju}. Rezultati usporedbe eksperimentalnih i simuliranih vrijednosti u smislu korelacijskih koeficijenata bili su: R2 = 0,9965 za gustoću i R2 = 0,9938 za kinematičku viskoznost. Za provjeru točnosti dva prethodno razvijena modela ANN upotrijebljeno je 238 novih eksperimentalnih baza podataka s 4 sustava (2 čista sustava, 1 binarni sustav i 1 ternarni sustav). Rezultati performansi predviđanja s obzirom na korelacijske koeficijente bili su: R2 = 0,9980 za gustoću i R2 = 0,9653 za kinematičku viskoznost. Usporedba rezultata validacije s rezultatima drugih studija pokazuje da su modeli neuronske mreže dali znatno bolje rezultate. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Study of a Solar PV-Wind-Battery Hybrid Power System for a Remotely Located Region in the Southern Algerian Sahara: Case of Refrigeration

The present work shows an experimental investigation that uses a combination of solar and wind energy as hybrid system (HPS) for electrical generation under the Algerian Sahara area. The generated electricity has been utilized mainly for cooling and freezing. The system has also integrated a gasoline generator to be more reliable. This system is not linked with conventional energy and is not fixed in one region as it is the case of the military base in the Algerian borders. The cooling load consisted of three containers of 10 m3 each with total electricity consumption of 45 kWh/day, two positive rooms (with an internal temperature of +2°C and an external temperature of 35°C) and one negative room (with an internal temperature of -20°C and an external temperature of 35°C). Measurements included the solar radiation intensity, the ambient temperature and the wind speed was collected from Adrar weather station (a windy place in Algeria) for the year of 2010. To simulate the hybrid power system (HPS) HOMER was used. Emissions and renewable energy generation fraction (RF) of total energy consumption are calculated as the main environmental indicator. The net present cost (NPC) and cost of energy (COE) are calculated for economic evaluation. It is found that, for Adrar climates, the optimum results of HPS show a 50% reduction of emissions with 47% of renewable energy fraction

Uklanjanje klortetraciklin klorhidrata foto-Fentonovim postupkom: eksperimentalna studija i ANN modeliranje

The present work aimed to study the feasibility of photo-Fenton oxidation for the degradation of chlortetracycline chlorhydrate (CTC) in aqueous solutions, as well as the modelling of system behaviour by artificial neural networks. The removal performance of CTC oxidation by the Photo-Fenton process was studied under solar radiation. Different parameters were studied, such as pH (3 to 5), and initial concentrations of CTC (0.1 to 10 mg l–1), hydrogen peroxide (1.701 to 190.478 mg l–1), and ferrous ions (2.8 to 103.6 mg l–1). Results showed that a high removal efficiency of 92 % was achieved at pH 3 under optimised conditions, such as 10 mg l–1 of CTC, 127.552 mg l–1 of H2O2, and 36.4 mg l–1 of Fe2+. The transformation of CTC molecules was proved by UV-visible and HPLC analyses, which showed that almost no CTC molecules were remaining in the treated solution. A multi-layer perceptron artificial neural network has been developed to predict the experimental removal efficiency of CTC based on four dimensionless inputs: molecular weight, and initial concentrations of CTC, hydrogen peroxide and ferrous ions. The best network has been found with a high determination coefficient of 0.9960, and with a very acceptable root mean square error 0.0108. In addition, the global sensitivity analysis confirms that the most influential parameter for the CTC removal by photo-Fenton oxidation is the initial concentration of ferrous cations with a relative importance of 33 %.Cilj ovog rada bio je ispitati razgradnju klortetraciklin klorhidrata (CTC) u vodenoj otopini foto-Fentonovim procesom, kao i modelirati ponašanje sustava primjenom umjetnih neuronskih mreža. Učinkovitost uklanjanja CTC-a foto-Fentonovim procesom ispitana je pod sunčevom svjetlošću. Proučavani su različiti parametri poput pH (3 do 5) te početnih koncentracija CTC-a (0,1 do 10 mg l–1), vodikova peroksida (1,701 do 190,478 mg l–1) i željeznih iona (2,8 do 103,6 mg l–1). Dobivena je učinkovitost uklanjanja od 92 % pri pH 3, uz 10 mg l–1 CTC, 127,552 mg l–1 H2O2 i 36,4 mg l–1 Fe2+. Koncentracija CTC-a praćena je spektrofotometrijski i tekućinskom kormatografijom, te su utvrđene neznatne koncentracije CTC-a u vodenoj otopini nakon obrade. Umjetna neuronska mreža višeslojni perceptron razvijena je za predviđanje eksperimentalne učinkovitosti uklanjanja CTC-a na temelju četiri bezdimenzionalna ulaza: molekulske mase, te početnih koncentracija CTC-a, vodikova peroksida i željeznih iona. Pronađena je najbolja mreža s visokim koeficijentom determinacije od 0,9960 i vrlo prihvatljivom srednjom kvadratnom pogreškom od 0,0108. Globalna analiza osjetljivosti potvrdila je da je najutjecajniji parametar kod uklanjanja CTC-a foto-Fentonovim procesom početna koncentracija kationa željeza s relativnom važnošću od 33 %

Modeliranje umjetne neuronske mreže višesustavnom dinamičkom adsorpcijom organskih onečišćujućih tvari na aktivnom ugljenu

The aim of this work was to model multi-system dynamic adsorption using an artificial intelligence technique. A set of data points, collected from scientific papers containing the dynamic adsorption kinetics on activated carbon, was used to build the artificial neural network (ANN). The studied parameters were molar mass, initial concentration, flow rate, bed height, particle diameter, BET surface area, average pore diameter, time, and concentration of dimensionless effluents. Results showed that the optimized ANN was obtained with a high correlation coefficient, R = 0.997, a root mean square error of RMSE = 0.029, and a mean absolute deviation of AAD (%) = 1.810 during the generalisation phase. Furthermore, a sensitivity analysis was also conducted using the inverse artificial neural network method to study the effect of all the inputs on the dynamic adsorption. Also in this work, the traceability of the estimated results was conducted by developing a graphical user interface. This work is licensed under a Creative Commons Attribution 4.0 International License.Cilj ovog rada bio je modelirati višesustavnu dinamičku adsorpciju tehnikom umjetne inteligencije. Za izradu umjetne neuronske mreže (ANN) upotrijebljen je skup podataka prikupljen iz znanstvenih radova koji sadrže kinetiku dinamičke adsorpcije na aktivnom ugljenu. Ispitivani parametri bili su: molarna masa, početna koncentracija, brzina protoka, visina sloja, promjer čestica, površina BET, prosječni promjer pora, vrijeme i koncentracija bezdimenzijskih otpadnih voda. Rezultati su pokazali da je tijekom faze generalizacije dobiven optimiran ANN s visokim koeficijentom korelacije, R = 0,997, korijenom srednje kvadratne pogreške RMSE = 0,029 i srednjim apsolutnim odstupanjem AAD (%) = 1,810. Dodatno, provedena je i analiza osjetljivosti primjenom metode inverzne umjetne neuronske mreže kako bi se proučio učinak svih ulaza na dinamičku adsorpciju. U radu je provedena i sljedivost procijenjenih rezultata razvojem grafičkog korisničkog sučelja. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Kritična svojstva i acentrični čimbenici modeliranja čistih spojeva primjenom modela QSPR-SVM i algoritma Dragonfly

This work aimed to model the critical pressure, temperature, volume properties, and acentric factors of 6700 pure compounds based on five relevant descriptors and two thermodynamic properties. To that end, four methods were used, namely, multi-linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs) using sequential minimal optimisation (SMO), and hybrid SVM with Dragonfly optimisation algorithm (SVM-DA) to model each property. The results suggested that hybrid SVM-DA had better prediction performance compared to the other models in terms of average absolute relative deviation (AARD%) of {0.7551, 1.962, 1.929, and 2.173} and R2 of {0.9699, 0.9673, 0.9856, and 0.9766} for critical temperature, critical pressure, critical volume, and acentric factor, respectively. The developed models can be used to estimate the property of newly designed compounds only from their molecular structure.Cilj ovog rada bio je modeliranje kritičnog tlaka, temperature, volumnih svojstava i acentričnih čimbenika 6700 čistih spojeva na temelju pet relevantnih deskriptora i dva termodinamička svojstva. U tu svrhu primijenjene su četiri metode: višestruka linearna regresija (MLR), umjetna neuronska mreža (ANN), metoda potpornih vektora (SVM) i algoritam optimizacije Dragonfly (SVM-DA), koji se za modeliranje svakog svojstva koriste sekvencijalnom minimalnom optimizacijom (SMO) i hibridnim SVM-om. Rezultati su pokazali da hibridni SVM-DA daje bolje predviđanje u odnosu na ostale modele u smislu postotka prosječnog apsolutnog relativnog odstupanja (AARD%) od {0,7551, 1,962, 1,929 i 2,173} i R2 od {0,9699, 0,9673, 0,9856, i 0,9766} za kritičnu temperaturu, kritični tlak, kritični volumen i acentrični faktor. Razvijeni modeli mogu se primjenjivati za procjenu svojstava novodizajniranih spojeva samo iz njihove molekularne strukture