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

Statistical modeling and optimization of Escherichia coli growth parameters for the biological treatment of phenol

peer reviewedAromatic compounds, including phenols, are a significant source of pollution which need to be treated by environmentally-friendly methods, such as bioprocesses. This work focuses on the biodegradation of phenol in a batch reactor with bacteria, and the optimization of the growth parameters in order to obtain the highest phenol degradation. The model and algorithms fitting the growth data are emphasized. Primary models, applied to monitor the dynamic evolution of the microbial biomass of the selected strain, were fitted to the data by nonlinear regression based on the Levenberg Marquart algorithm. The statistically-validated Baranyi and Roberts equation was used to evaluate the growth parameters: maximum growth rate (μmax), latency time (λ), and maximum optical density (ODmax). To improve bacterial growth and phenol degradation performance, physico-chemical conditions, such as initial phenol concentration, pH, and nitrogen source (ammonium sulfate), were optimized using secondary models based on a central composite rotatable design (CCRD). The correlation coefficient, R², for each regression equation is > 94%. The optimal values of growth parameters are λmin = 21.08 h, µmax = 8.68 h–1, and ODmax = 0.39 at pH = 6.3 for an initial concentration of phenol = 200 mg/L and initial concentration of ammonium sulfate = 1.33 g/L. Escherichia coli showed an ability to degrade up to 963 mg/L of phenol in 250 h without prior acclimatization of the strain