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

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

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

Performance Analysis for Bandwidth Allocation in IEEE 802.16 Broadband Wireless Networks using BMAP Queueing

This paper presents a performance analysis for the bandwidth allocation in IEEE 802.16 broadband wireless access (BWA) networks considering the packet-level quality-of-service (QoS) constraints. Adaptive Modulation and Coding (AMC) rate based on IEEE 802.16 standard is used to adjust the transmission rate adaptively in each frame time according to channel quality in order to obtain multiuser diversity gain. To model the arrival process and the traffic source we use the Batch Markov Arrival Process (BMAP), which enables more realistic and more accurate traffic modelling. We determine analytically different performance parameters, such as average queue length, packet dropping probability, queue throughput and average packet delay. Finally, the analytical results are validated numerically.Comment: 16 page

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