Advanced Simulation of Removing Chromium from a Synthetic Wastewater by Rhamnolipidic Bioflotation Using Hybrid Neural Networks with Metaheuristic Algorithms

Abstract

This work aims at presenting an advanced simulation approach for a novel rhamnolipidic-based bioflotation process to remove chromium from wastewater. For this purpose, the significance of key influential operating variables including initial solution pH (2, 4, 6, 8, 10 and 12), rhamnolipid to chromium ratio (RL:Cr = 0.010, 0.025, 0.050, 0.075 and 0.100), reductant (Fe) to chromium ratio (Fe:Cr of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0), and air flowrate (50, 100, 150, 200 and 250 mL/min) were investigated and evaluated using Analysis of Variance (ANOVA) method. The RL as both collector and frother was produced using a pure strain of Pseudomonas aeruginosa MA01 under specific conditions. The bioflotation tests were carried out within a bubbly regimed column cell with the dimensions of 60×5.70×0.1 cm. Four optimization techniques based on Artificial Neural Network (ANN) including Cuckoo, genetic, firefly and biogeography-based optimization algorithms were applied to 113 experiments to identify the optimum values of studied factors. The ANOVA results revealed that all four variables influence the bioflotation performance through a non-linear trend. Their influences, except for aeration rate, were found statistically significant (p-value < 0.05), and all parameters followed the normal distribution according to Anderson-Darlin (AD) criterion. Maximum chromium removal of about 98% was achieved at pH of 6, rhamnolipid to chromium ratio of 0.05, air flowrate of 150 mL/min, and Fe to Cr ratio of 1.0. Flotation kinetics study indicated that chromium bioflotation follows the first-order kinetic model with a rate of 0.023 sec−1. According to the statistical assessment of the model accuracy, the firefly algorithm (FFA) with a structure of 4-9-1 yielded the highest level of reliability with the mean squared, root mean squared, percentage errors and correlation coefficient values of test-data of 0.0038, 0.0617, 3.08% and 96.92%, respectively. These values were evidences of the consistency of the well-structured ANN method to simulate the process.

Metallurgical evaluation of copper ore flotation performance in the presence of Rhamnolipid biosurfactant produced from Pseudomonas aeruginosa. Part 1: Copper-bearing minerals

“…Then, using the model, one can draw plots of the individual and interaction effects of the parameters. The continuity of the points in these plots is the values predicted by the model, which sit between the experimental values obtained during the test work (Mahmoodabadi et al, 2019;Khoshdast et al, 2021). Considering that in the first part of this research, copper grade and recovery are considered as flotation responses, two prediction models for grade and recovery were developed, whose fitting parameters are presented in Table 8.…”

“…6, the performance of the frother at high concentrations of the collector is greatly reduced. This phenomenon can be attributed to the interaction of collector and frother molecules in the aqueous phase, which has been studied in detail in various references (e.g., Khoshdast et al, 2022;Asgari et al, 2023).…”

Effect of an acetonized pyrolysis oil recycled from spent-car tires on coal flotation performance

“…One of the effective tools for assessing the influence of each individual parameter on the process response is main effect plots. In these plots, the response is drawn for considered levels of parameters in the experimental design (Khoshdast et al, 2021). In order to calculate the response values for variables based on their experimental levels, the developed prediction model (Eqs.…”

Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods

“…Figure 7 shows the main effect plots for operating factors considered in the experimental design. Detailed explanations regarding the construction of main effect plots are addressed elsewhere [34,[65][66][67]. According to Figure 7, quartz recovery decreases as the air flow rate increases.…”