Predviđanje količine bikarbonata u pitkoj vodi regije Médéa modeliranjem umjetnom neuronskom mrežom

The region of Médéa (Algeria) located in an agricultural site requires a large amount of drinking water. For this purpose, the water analyses in question are imperative. To examine the evolution of the drinking water quality in this region, firstly, an experimental protocol was done in order to obtain a dataset by taking into account several physicochemical parameters. Secondly, the obtained data set was divided into two parts to form the artificial neural network, where 70 % of the data set was used for training, and the remaining 30 % was also divided into two equal parts: one for testing and the other for validation of the model. The intelligent model obtained was evaluated as a function of the correlation coefficient nearest to 1 and lowest mean square error (RMSE). A set of 84 data points were used in this study. Eighteen parameters in the input layer, five neurons in the hidden layer, and one parameter in the output layer were used for the ANN modelling. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid, and linear transfer function were used, respectively, for the hidden and the output layers. The results obtained during the present study showed a correlation coefficient of R = 0.99276 with root mean square error RMSE = 11.52613 mg dm–3. These results show that obtained ANN model gave far better and more significant results. It is obviously more accurate since its relative error is small with a correlation coefficient close to unity. Finally, it can be concluded that obtained model can effectively predict the rate of soluble bicarbonate in drinking water in the Médéa region. This work is licensed under a Creative Commons Attribution 4.0 International License.Regija Médéa (Alžir) smještena na poljoprivrednom zemljištu zahtijeva veliku količinu pitke vode te je stoga analiza vode od iznimne važnosti. Da bi se ispitao razvoj kvalitete pitke vode u toj regiji, najprije je napravljen eksperimentalni protokol za dobivanje skupa podataka uzimajući u obzir nekoliko fizikalno-kemijskih parametara. Zatim je dobiveni skup podataka podijeljen na dva dijela za stvaranje umjetne neuronske mreže, gdje je 70 % skupova podataka upotrijebljeno za trening, a preostalih 30 % dodatno je podijeljeno na dva jednaka dijela: jedan za testiranje, a drugi za validaciju modela. Dobiveni inteligentni model procijenjen je kao funkcija koeficijenta korelacije najbližeg 1 i najnižeg korijena srednje kvadratne pogreške (RMSE). U ovom istraživanju upotrijebljen je skup od 84 podatkovnih točaka. Za modeliranje ANN-a upotrijebljeno je osamnaest parametara u ulaznom sloju, pet neurona u skrivenom sloju i jedan parametar u izlaznom sloju. Za skriveni i izlazni sloj upotrijebljeni su algoritam učenja Levenberg Marquardt (LM), logaritamski sigmoid i funkcija linearnog prijenosa. Rezultati dobiveni tijekom ovog istraživanja pokazali su koeficijent korelacije R = 0,99276 s korijenom srednje kvadratne pogreške RMSE = 11,52613 mg dm–3. Ti rezultati pokazuju da je dobiveni model neuronske mreže dao daleko bolje rezultate, jer je točniji a njegova relativna pogreška je mala s koeficijentom korelacije blizu 1. Konačno, zaključeno je da taj model može učinkovito predvidjeti brzinu topljivosti bikarbonata u vodi za piće u regiji Médéa. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Obrada procjednih voda iz odlagališta otpada Aïn Defla (Alžir) procesom oksidacije i biosorpcije

The aim of this study was the treatment of leachate from the Technical Landfill Centre Aïn Defla in Algeria, which presents a serious threat to the environment. Elimination of organic matter (expressed as chemical oxygen demand (COD) and biological oxygen demand (BOD5)), suspended matter (SM), mineral matter (phosphates and ammoniacal nitrogen), and heavy metals (zinc and iron) were experimentally studied using the coupling of oxidation (OP) and biosorption processes (BS). The analysis results showed that the leachate studied at pH 8.8 was very loaded in organic matter (turbidity of 553 NTU, SM = 820 mg l–1, COD = 9669 mg O2 l–1, and BOD5 = 8875 mg O2 l–1), in salts (EC = 19.4 mS cm–1), in ammoniacal nitrogen (2027 mg l–1), in phosphates (22.9 mg l–1), and in sulphates (750 mg l–1). It also contained significant amounts of heavy metals, notably zinc (4.21 mg l–1) and iron (47.5 mg l–1). The evolution of the physicochemical parameters during the treatment showed that, under the optimal conditions (T = 45 °C, [H2O2] = 1.6 mol l–1, volume fraction φ(H2O2) = 5 %, and [Fe3+] = 0.5 mmol l–1), the reduction in COD was about 99 %, the reduction in BOD5 was 100 %, the elimination of colloidal particles (SM) could reach 95 %, reduction in phosphates was 78 %, reduction in ammonium was 98 %, reduction in sulphates was 96 %, reduction in zinc was 92 %, and the reduction in iron was 98 %.Tema ovog istraživanja je obrada procjednih voda odlagališta otpada Aïn Defla u Alžiru, koje predstavljaju ozbiljnu prijetnju po okoliš. Ispitivano je uklanjanje organskih tvari (izraženo preko kemijske potrošnje kisika (KPK) i biološke potrošnje kisika (BPK5)), suspendiranih čestica (SČ), mineralnih tvari (fosfati i amonijačni dušik) i teških metala (cink i željezo) iz procjednih voda kombiniranjem procesa oksidacije i biosorpcije. Rezultati analiza pokazali su da je procjedna voda imala pH 8,8 te je bila jako opterećena organskom tvari (zamućenje = 553 NTU, SČ = 820 mg l–1; KPK = 9669 mg O2 l–1, te BPK5 = 8875 mg O2 l–1), u solima (EC = 19,4 mS cm–1), amonijačnim dušikom (2027 mg l–1), fosfatima (22,9 mg l–1) i sulfatima (750 mg l–1). Također sadržavala je i značajne količine teških metala, osobito cinka (4,21 mg l–1) i željeza (47,5 mg l–1). Iz fizikalno-kemijskih parametara praćenih tijekom obrade vidljivo je, pri optimalnim uvjetima (45 °C, [H2O2] = 1,6 mol l–1), volumni udio φ(H2O2) = 5 % i [Fe3+] = 0,5 mmol l–1), smanjenje KPK vrijednosti od 99 %, BPK5 vrijednosti od 100 %, smanjenje fosfata, amonijaka, sulfata, cinka i željeza za 78 %,98 %, 96 %, 92 %, odnosno 98 %. Također, 95,6 % suspendiranih čestica uklonjeno je tijekom obrade procjedne vode

Elaboration et caractérisation de mousses métalliques à base de Sn-Pb

Cette étude est consacrée à l'élaboration par le procédé d'infiltration de l'alliage liquide et a la caractérisation (comportement mécanique et microstructure) de mousses métalliques à base d'Etain-Plomb de différentes densités relatives et taille de pores. Des essais de compression uniaxiale à l'ambiante ont été réalisés afin d'étudier l'influence de la taille de cellule et de la densité relative sur le comportement en compression et de situer ces relations dans le cadre des théories. Une caractérisation à l'échelle microscopique (métallographie et dureté) à été réalisée afin de relier les caractéristiques morphologiques et mécaniques des phases présentes dans les brins, les paramètres du procédé d'élaboration et le comportement mécanique macroscopique

Modeliranje adsorpcijskog fenomena određenih fenola metodom potpornih vektora Dragonfly pomoću vlakana aktivnog ugljena

The objective of this research was to build a mathematical model based on a Support Vector Machine (SVM) capable of predicting the amount adsorbed at equilibrium (qe). Activated carbon fibres (ACF) were used for the adsorption of certain phenols (phenol, 2-chlorophenol, 4-chlorophenol, 2,4,6-trichlorophenol, 4-nitrophenol, and 2,4-dinitrophenol). An experimental dataset of 129 points was collected from previously published papers. The inputs considered for modelling were temperature (T), concentration at equilibrium (ce), and two descriptors (boiling point (BP) and density (d)) to differentiate between the pollutants studied. The data used were pre-processed by the statistical analysis to ensure that they were adequate for modelling. The results showed a superiority of the Gaussian kernel function DA-SVM model demonstrated by its determination coefficient (R2 = 0.997) and root mean squared error (RMSE = 0.027 mmol l–1). This work is licensed under a Creative Commons Attribution 4.0 International License.Cilj ovog istraživanja bio je izraditi matematički model zasnovan na metodi potpornih vektora (SVM) koji može predvidjeti količinu adsorbiranu u ravnoteži (qe). Vlakna s aktivnim ugljenom (ACF) upotrijebljena su za adsorpciju određenih fenola (fenol, 2-klorofenol, 4-klorofenol, 2,4,6-triklorofenol, 4-nitrofenol i 2,4-dinitrofenol). Eksperimentalni skup podataka od 129 bodova prikupljen je iz prethodno objavljenih radova. Ulazi parametri koji su uzeti u obzir za modeliranje bili su temperatura (T), koncentracija u ravnoteži (ce) i dva deskriptora (točka vrenja (BP) i gustoća (d)) za razlikovanje ispitivanih onečišćujućih tvari. Korišteni podatci prethodno su obrađeni statističkom analizom da bi se osigurala njihova primjerenost za modeliranje. Rezultati su pokazali superiornost modela DA-SVM Gaussove kernel funkcije demonstriranog njegovim koeficijentom determinacije (R2 = 0,997) i srednjom kvadratnom pogreškom (RMSE = 0,027 mmol l–1). Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Mechanical and Numerical Analysis Concerning Compressive Properties of Tin-Lead Open-Cell Foams

International audienceThe design of new or innovative materials has to meet two essential criteria: increased mechanical performance and minimization of the mass. This dual requirement leads to interest in the study of various classes of metallic foams. The actual research is focused on open-cell Tin-Lead foams manufactured by replication process using NaCl preform. A mechanical press equipped with a load cell and a local extensometer with a controlled deformation rate is used. Experimental tests were carried out in order to study the influences of both the cell size and of the relative density on the mechanical behavior during a compression deformation and to analyze the obtained properties variation within a new framework. This study has three main sections which start with the manufacturing description and mechanical characterization of the proposed metallic foams followed by the understanding and modeling of their response to a compression load via a Gibson-Ashby model, a Féret law, a proposed simple Avrami model, and a generalized Avrami model. Finally, an exposition of a numerical simulation analyzing the compression of the Sn-Pb foams concerning the variation of the relative densities with respect to the plastic strain is propose

Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity

In this study, four different mathematical models were considered to predict the coagulant dose in view of turbidity removal: response surface methodology (RSM), artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). The results showed that all models accurately fitted the experimental data, even if the ANN model was slightly above the other models. The SVM model led to almost similar results as the ANN model; the only difference was in the validation phase, since the correlation coefficient was very high and the statistical indicators were very low for the ANN model compared to the SVM model. However, from an economic point of view, the SVM model was more appropriate than the ANN model, since its number of parameters was 22, i.e. almost half the number of parameters of the ANN model (43 parameters), while the results were almost similar in all the data phase. To further reduce the economic costs, the RSM model can also be used which remained very useful due to its high coefficients related to the number of parameters – only 13. In addition, the statistical indicators of the RSM model remained acceptable

Razrada i mehaničko-elektrokemijska karakterizacija antimon-olovnih pjena s otvorenim ćelijama izrađenim “Metodom prekomjerne replikacije soli” za moguću primjenu u proizvodnji olovno-kiselih baterija

In this study, open cell 25 % antimony-lead alloy foams are fabricated for possible use to lighten thick plates of lead-acid batteries. A new inexpensive and simple variant of the salt replication process is developed and explored. Different morphology and shapes have been successfully obtained with “excess salt replication” method (abbreviated as ESR method). Best porosity of about 68 % is obtained with salt particles size of about 3 mm. SEM and EDXS investigation of the composite salt/antimony alloy before NaCl leaching revealed the presence of the lead oxides microfilm coating cell walls and becoming lead carbonates after salt removal. Uniaxial compressive behaviour of the resulting cellular materials is studied for foams with porosities between 45 % and 70 %, and salt grain size ranging between 2.5 and 5 mm. A higher plateau stress is reached compared to the results obtained in the literature working on the aluminium foams. The reproducibility of the process is proved along samples. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom istraživanju izrađene su otvorene ćelije od 25 % antimon-olovnih pjena za moguću primjenu u osvjetljavanju debelih ploča olovno-kiselih baterija. Razvijena je i istražena nova jeftina i jednostavna varijanta procesa repliciranja soli. Različite morfologije i oblici uspješno su dobiveni metodom “prekomjerne replikacije soli” (ESR metodom). Najbolja poroznost od oko 68 % dobivena je pri veličini čestica soli od oko 3 mm. SEM i EDXS ispitivanje kompozitne legure soli/antimona prije ispiranja s NaCl otkrilo je prisutnost mikrofilma olovnih oksida koji oblažu zidove ćelije i nakon uklanjanja soli postaju olovni karbonati. Jednoosno tlačno ponašanje dobivenih materijala ćelije proučava se za pjene s poroznošću između 45 % i 70 % i za veličine zrna soli između 2,5 i 5 mm. Postignuta je veća granica stlačivanja u usporedbi s rezultatima o aluminijskim pjenama dobivenim u literaturi. Ponovljivost postupka dokazana je na uzorcima. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Mixed Coagulant-flocculant Optimization for Pharmaceutical Effluent Pretreatment Using Response Surface Methodology and Gaussian Process Regression

peer reviewedWastewater from the Antibiotical-Saidal pharmaceutical plant (Medéa) was pretreated by coagulation-flocculation using copper sulfate (CuSO4), iron chloride (FeCl3), and mixture of the two salts combined in a 1:1 (v/v) ratio in the present study. Response surface methodology (RSM) was used to optimize pH and coagulant dosage as independent variables, while dissolved organic carbon (DOC), absorbance at 254 nm (UV 254), and turbidity were provided as dependent variables in the central composite design (CCD). Then, the databases of the three treatments were combined in a single database to create a general model valid for the three treatments at the same time, and to predict the reduction rates of DOC, UV254, and turbidity, using the Gaussian process regression coupled with the dragonfly optimization algorithm (GPR-DA). To have the best model obtained between RMS and GPR-DA, an experimental validation was carried out after having had the optimal conditions of each type of coagulant, using the multi-objective optimization technique. The results of the experimental validation show the superiority of the GPR-DA model compared to the RSM model. Also, the results show that the mixed coagulant (CuSO4+ FeCl3) obtain better results than CuSO4 or FeCl3 alone with a treatment efficiency equal to 92.68% at pH = 5 and dosage = 600 mg/L, and the reductions in DOC, UV 254 and turbidity are 97.32%, 82.90% and 96.47%, respectively