Mineralization of cyanide originating from gold leaching effluent using electro-oxidation: multi-objective optimization and kinetic study

Abstract

This study examines the electro-oxidation (EO) of cyanide originating from an industrial plant´s gold leaching effluent. Experiments were carried out in a laboratory-scale batch cell reactor. Monopolar configuration of electrodes consisting of graphite (anode) and aluminum (cathode) was employed, operating in galvanostatic mode. Response Surface Methodology (RSM), based on a Box-Behnken experimental Design (BBD), was used to optimize the EO operational conditions. Three independent process variables were considered: initial cyanide concentration ([CN − ] 0 = 1000-2000 mg L −1 ), current density (J =7-107 mA cm −2 ), and stirring velocity (η = 250-750 rpm). The cyanide conversion X CN − , Chemical Oxygen Demand (COD) removal percentage (%R COD ), and specific Energy Consumption per unit mass of removed cyanide (EC) were analyzed as response variables. Multi-objective optimization let to establish the most effective EO conditions ([CN − ] 0 = 1000 mg L −1 , J = 100 mA cm −2 and η = 750 rpm). The experimental data ( X CN − , %R COD , and EC) were fitted to second-order polynomial models with adjusted correlation coefficients ( R 2 adj ) of ca. 98, 99 and 87%, respectively. The kinetic analysis, performed at optimal EO operational conditions, allowed determination of time required to meet Colombian permissible discharge limits. The predictive capacity of kinetic expressions was verified against experimental data obtained for gold leaching effluent. Total cyanide removal and 96% of COD reduction were obtained, requiring EC of 71.33 kWh kg −1 and 180 min. The BOD 5 (biological oxygen demand)/COD ratio increased from 4.52 × 10 −4 to 0.5573, confirming effluent biodegradability after EO treatment.

Analysis of the Capacity of the Fenton Process for the Treatment of Polluted Wastewater from the Leather Dyeing Industry

“…Thus, the statistically important factors correspond to all those values that exceed the vertical line ( Figure 4 ). The percentage effect of each factor on the response variable was calculated using equation ( 14 ), where b i corresponds to the value of the standardized effect for factor i [ 38 ]. …”

“…Tese present a series of bars whose extensions refect the frequency or impact of each factor (e.g., the longer the bar is, the greater the infuence and efect of the factor on the response variable), regardless of whether the efect is positive (increasing the efect will cause an increase in the response variable) or negative (increasing the efect will cause a decrease in the 4). Te percentage efect of each factor on the response variable was calculated using equation (14), where b i corresponds to the value of the standardized efect for factor i [38].…”

“…RSM involves a group of mathematical and statistical techniques based on the adjustment of empirical models to the experimental data obtained according to the selected matrix [ 24 , 37 ]. Linear or square polynomial functions are used to describe the performance of the studied system and to examine the experimental conditions until their optimization [ 38 ]. Its application as an optimization tool involves the following steps: (i) the choice of independent variables with important effects on the studied system (factors) and the definition of their experimental range (through preliminary experiments), (ii) the selection of dependent variables (responses), (iii) the selection of the experimental design and its experimental running, (iv) the statistical analysis and mathematical treatment (fitting to a polynomial function) of the experimental data obtained, (v) the evaluation of the fitted model, (vi) the optimization, and (vii) the experimental validation of the model.…”

Designing an Efficient Surfactant–Polymer–Oil–Electrolyte System: A Multi-Objective Optimization Study

“…The objective functions considered in this study were the minimum conductivity (J 1 ), the minimum turbidity (J 2 ), the minimum viscosity (J 3 ), and the minimum interfacial tension (J 4 ). These objectives were combined into a scalar goal as follows: J = w 1 J 1 + w 2 J 2 + w 3 J 3 + w 4 J 4 (10) where w 1 , w 2 , w 3 , and w 4 are weighting factors, which can be calculated using the rank sum method [16,[43][44][45].…”

Carbon-Nitrogen Bond Formation on Cu Electrodes During Co2 Reduction in No3- Solution

“…The formation of NO during an oxidative scan suggests oxidation of a (surface adsorbed) product, formed in the reductive scan, which based on SERS data is cyanide. Formed cyanide in solution can thus be decomposed by direct electrooxidation 59 . Cu is effective in this reaction by forming complexes which can oxidize at less positive potentials than free cyanide.…”