In this study, methyl orange (MO) dye removal by adsorption utilizing activated carbon made from date seeds (DPAC) was modeled using an artificial neural network (ANN) technique. Instrumental investigations such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET) analysis were used to assess the physicochemical parameters of adsorbent. By changing operational parameters including adsorbent dosage (0.01-0.03 g), solution pH 3-8, initial dye concentration (5-20 mg/L), and contact time (2-60 min), the viability of date seeds for the adsorptive removal of methyl orange dye from aqueous solution was assessed in a batch procedure. The system followed the pseudo 2nd order kinetic model for DPAC adsorbent, according to the kinetic study (R2 = 0.9973). The mean square error (MSE), relative root mean square error (RRMSE), root mean square error (RMSE), mean absolute percentage error (MAPE), relative error (RE), and correlation coefficient (R2) were used to measure the ANN model performance. The maximum RE was 8.24% for the ANN model. Two isotherm models, Langmuir and Freundlich, were studied to fit the equilibrium data. Compared with the Freundlich isotherm model (R2 = 0.72), the Langmuir model functioned better as an adsorption isotherm with R2 of 0.9902. Thus, this study demonstrates that the dye removal process can be predicted using an ANN technique, and it also suggests that adsorption onto DPAC may be employed as a main treatment for dye removal from wastewater.
The purpose of this study was to examine the application of the mathematical model of drift flux to the experimental results of the effect of cationic trimethyl-ammonium bromide (CTAB)-aided continuous foam flotation harvesting on the lipid content in Chlorella vulgaris microalgae. An experiment was conducted to determine the effect of the operating conditions on the enrichment factor (EF) and percentage recovery efficiency (%RE), where the flow rates at the inlet and bottom outlet remained constant. Data for the binary system (without algae) and ternary system (with algae) in an equal-area foam column show that the EF decreases linearly with increasing initial CTAB concentrations ranging from 30 to 75 mg/L for three levels of the studied air volumetric flow rate range (1-3) L/min. The percentage harvesting efficiency increased with increasing initial CTAB concentration and air volumetric flow rate to 96 % in the binary systems and 94 % in the ternary systems. However, in the foam column with the riser used in the three systems, a lower volume of liquid foam in the upward outlet stream resulted in a lower RE% than that of the column without the riser. The objective function of EF for the system with algae increased when the initial CTAB concentration was increased from 30 to 45 mg/L in the foam column with a riser for all air flow rates, and after 45 mg/L, a sudden drop in the microalgae EF was observed. In the comparison between the foam column with and without the riser for the system with algae, the optimum EF was 145 for the design of the column with the riser and 139 for the column without the riser.
Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.