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

A study on the characteristics of Algerian Hassi-Messaoud asphaltenes:Algerian Hassi-Messaoud asphaltenes: solubility and precipitation

This study focuses on detailed characterizations of asphaltene fractions extracted from the Algerian Hassi-Messaoud oil field. It was found that the extracted asphaltenes are not completely soluble in toluene, instead two fractions of asphaltenes were obtained upon solubilizing the heptane-precipitated neat asphaltenes in toluene. Extensive characterizations of the toluene-soluble and insoluble fractions were carried out using elemental analysis, Fourier transform infrared (FTIR), thermogravimetric analysis (TGA), X-ray diffraction (XRD) and solid-state nuclear magnetic resonance (ssNMR). It was suggested that the high oxygen content and uneven compositional structures are the main contributors to asphaltene instability. The toluene-insoluble fractions were found to have higher polarity and aromaticity as well as more oxygen content than the neat asphaltenes and toluene-soluble fractions

Estimation of Properties of Liquid-Vapor Mixture of Some Refrigerants at High Pressure for Solar-Photovoltaic Refrigeration

Abstract. In this work, a hybrid method based on neural network and particle swarm optimization is applied to literature data to develop and validate a model that can predict with precision vapor-liquid equilibrium data for the binary systems (hexafluoroethane (R116(1)), 1,1,1,2-tetrafluoroethane (R134a) and R1234ze) . ANN was used for modelling the non-linear process. The PSO was used for two purposes: replacing the standard back propagation in training the ANN and optimizing the process. The training and validation strategy has been focused on the use of a validation agreement vector, determined from linear regression analysis of the predicted versus experimental outputs, as an indication of the predictive ability of the neural network model. Statistical analysis of the predictability of the optimized neural network model shows excellent agreement with experimental data (coefficient of correlation equal to 0.998). Furthermore, the comparison in terms of average relative deviation (AARD%) between, the predicted results for the whole temperature and pressure range shows that the ANN-PSO model can predict far better the mixture properties than cubic equations of state

Mechanical Behavior Analysis of a Friction Stir Welding (FSW) for Welded Joint Applied to Polymer Materials

Welding is a technique of fusion joining the material involving a process of inter-molecular diffusion adhesion. Polymer welding is an assembly method among several known assembly techniques such as gluing. This welding process applies to thermoplastics; they have the rheological or softening characteristics during melting. This process is fast and controlled in order to obtain a solid and durable mechanical connection on the series parts. This study focuses on the weldability of high density polyethylene (HDPE) using the friction stir welding technique. A parametric choice was made to optimize the operating parameters namely the shape of the welding tool, the speed of rotation and the speed of advance of the tool.  Monotonic tensile tests were used to compare the mechanical characteristics between a HDPE test specimen and a specimen taken from an FSW weldment. It emerges from this study that the FSW welding introduces a weakening of the joints characterized by a clear decrease of the deformation at break

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Novel Approach for Estimating Monthly Sunshine Duration Using Artificial Neural Networks: A Case Study

This work deals with the potential application of artificial neural networks to model sunshine duration in three cities in Algeria using ten input parameters. These latter are: year and month, longitude, latitude and altitude of the site, minimum, mean and maximum air temperature, wind speed and relative humidity. They were selected according to their availability in meteorological stations and based on the fact that they are considered as the most used parameters by researchers to model sunshine duration using artificial neural networks. Several network architectures were tested to choose the most accurate and simple scheme. The optimum number of layers and neurons was determined by trial and error method. The optimized network was obtained using Levenberg-Marquardt back-propagation algorithm, one hidden layer including 25 neurons with Tan-sigmoid transfer function. The model developed in this study has the ability to estimate sunshine duration with a mean absolute percentage error value equals to 2.015%, a percentage root mean square error of 2.741% and a determination coefficient of 0.9993 during test stage

Alkaline treatment of timber sawdust: A straightforward route toward effective low-cost adsorbent for the enhanced removal of basic dyes from aqueous solutions

The present study assesses the ability of two low-cost adsorbents – timber sawdust (TS–OH) and its alkaline treated analog (TS–ONa) – to remove two basic dyes, namely, Methylene Blue and Methyl Green, from aqueous solutions. The presence of new functional groups on the surface of TS–ONa resulted in a dramatic increase of surface polarity and the density of sorption sites, thereby improving the sorption efficiency of the cationic dyes. The results obtained from the sorption characteristics have revealed that the sorption process for TS–ONa was uniform and rapid. The adsorption of cationic dyes reached equilibrium within the first 10 min of contact time and the treated material acts efficiently in a wide pH range of dye solutions. The extent of adsorption was measured through equilibrium sorption isotherms and analyzed using the Langmuir model. The monolayer saturation capacities for Methylene Blue are 694.44 and 1928.31 mg g−1 and for Methyl Green are 892.86 and 1821.33 mg g−1 for TS–OH and TS–ONa, respectively. Therefore, the chemically treated sawdust proved two- to threefold higher adsorption capacities of these dyes than those of the untreated analog. The exothermic nature of adsorption is demonstrated by a decrease of adsorption capacity with increasing temperature, and the negative value of free energy change indicated the spontaneity of adsorption. Desorption experiments with 1 M aqueous NaCl put into evidence that cationic dyes were completely desorbed from the matrices and the reusability of the TS–ONa matrix after three repeated cycles led to just a slight attenuation in its performance. These results show that alkaline treatment of a low value by-product of the timber industry leads to a powerful and efficient low-cost adsorbent, which may be used for the treatment of colored wastewaters