The treatment of wastewater from heavy metal ions such as hexavalent chromium Cr(VI) is considered as an important issue in recent years, which is harmful to human health and environment. Since, in engineering, performing the experiments to solve problems is time-consuming and costly. In this study, adaptive neuro-fuzzy inference system (ANFIS) was coupled with particle swarm optimization (PSO) algorithm to develop a predictive model for modeling of Cr(VI) removal percent on NiO nanoparticle. To this end, the trace of four initial parameters containing contact time, Cr(VI) initial concentration, NiO adsorbent dosage, and pH on removing Cr(VI) was investigated. The performance of the developed algorithm was evaluated by statistical parameters such as mean absolute relative deviation mean squared error (MSE)

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The tie-line values of the aqueous mixture (H 2 O + phosphoric acid + organic solvents) were checked at several temperatures and air pressures. Methyl butyl ketone or 1-pentanol was selected as the organic solvent. Solubility data were specified for an aqueous ternary mixture employing the cloud point measurements. All the ternary mixtures exhibit the type-1 liquid−liquid phase behavior system. Experimental data were determined employing the refractive index method, the Karl−Fischer technique, and acidimetric titration. Binary interaction parameters were predicted employing liquid−liquid equilibrium modeling. The NRTL and UNIQUAC models were employed to define which model is more reliable. The trustworthiness of the experiment outcome was approved by considering the Hand and Othmer-Tobias equations. Selectivity factors and distribution coefficients were derived to determine the solvents' extraction capacity. The effect of temperature change was checked on the solubility curve and extraction process at different temperatures.