Valorization of pyrolysis oils recycled from waste car tires as potential collector in coal flotation: Production, characterization, and collecting mechanism.

The present research study investigates the performance of pyrolysis oils recycled from waste tires as a collector in coal flotation. Three different types of pyrolysis oils (namely, POT1, POT2, and POT3) were produced through a two-step pressure pyrolysis method followed by an oil rolling process. The characteristics of POTs were adjusted using various oil-modifying additives such as mineral salts and organic solvents. The chemical structure of POTs was explored by employing necessary instrumental analysis techniques, including microwave-assisted acid digestion (MAD), inductively coupled plasma atomic emission spectroscopy (ICP-AES), Fourier-transform infrared spectroscopy (FT-IR), and gas chromatography-mass spectrometry (GC-MS). The collecting performance of POTs in coal flotation was evaluated using an experimental design based on Response Surface Methodology (RSM), considering the ash content and yield of the final concentrate. The effect of the type and dosage of POTs was evaluated in conjunction with other important operating variables, including the dosage of frother, dosage of depressant, and the type of coal. Results of POTs characterization revealed that the pyrolysis oils were a complex composition of light and heavy hydrocarbon molecules, including naphthalene, biphenyl, acenaphthylene, fluorene, and pyrene. Statistical analysis of experimental results showed that among different POTs, POT1 exhibited remarkable superiority, achieving not only a 15% higher coal recovery but also a 12% lower ash content. The outstanding performance of POT1 was attributed to its unique composition, which includes a concentrated presence of carbon chains within the optimal range for efficient flotation. Additionally, the FT-IR spectra of POT1 reveal specific functional groups, including aromatic and aliphatic compounds, greatly enhancing its interaction with coal surfaces, as confirmed by contact angle measurement. This research provides valuable insights into the specific carbon chains and functional groups that contribute to the effectiveness of POT as a collector, facilitating the optimization of coal flotation processes and underscoring the environmental advantages of employing pyrolysis oils as sustainable alternatives in the mining industry.

Applying hybrid genetic and artificial bee colony algorithms to simulate a bio-treatment of synthetic dye-polluted wastewater using a rhamnolipid biosurfactant.

The present work aims at optimization and advanced simulation of removal efficiency of dye material from a synthetic wastewater using a locally generated rhamnolipid (RL) biosurfactant. For this purpose, bio-treatment of dye polluted synthetic wastewater was experimentally, kinetically, and statistically investigated by the ion flotation process in the presence of the RL. The removal rate of methylene blue (MB) as the dye material was assessed by the ultraviolet (UV)-visible absorbance measurements. The impact of operating variables including RL concentration (as a dye collector, 5-50 ppm), methyl isobutyl carbinol (MIBC) dosage (as a frother, 10-70 ppm), solution pH (2-12) and aeration rate (1-5 l/min) were assessed through one-way analysis of variance (ANOVA) and Anderson-Darling as the normality analysis strategy. The process was simulated using two artificial neural network (ANN) optimization algorithms, i.e., genetic algorithm (GA) and artificial bee colony (ABC) as a novel approach. The statistical results indicated that the dye removal process was significantly influenced by all operating variables (pvalue<0.05) while their relative intensity followed the order of aeration rate > solution pH > RL concentration > MIBC dosage. Anderson-Darling approach disclosed that the all factors were perfectly followed the normal trend with A2 less than unity and p-value of greater than 0.05 at 95% confidence level. Main effect plots revealed that except MIBC dosage with nonlinear trend, the rest of factors had an ascending influence on the removal efficiency. The process was optimized by interpreting the interaction effect among various variables to reach the maximum dye bioflotation. The maximum removal of 97 ± 0.13% was achieved at pH 12, airflow rate of 5 l/min, MIBC and rhamnolipid concentrations of 30 and 40 ppm, respectively with a flotation kinetic rate of 0.015 sec-1. Finally, the intelligent simulation results showed that the process could be modelled using an artificial bee colony algorithm of 4-7-1 structure with 99% and 98.8% accuracies in the training and testing steps, respectively. Further, we found that the artificial bee colony algorithm was superior to the genetic algorithm in terms of complexity analysis.

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

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.

Efficient cadmium removal from aqueous solutions using a sample coal waste activated by rhamnolipid biosurfactant.

A sample coal waste activated by rhamnolipid biosurfactants was used as an efficient adsorbent for the adsorption of cadmium from aqueous solution. The effects of three factors, namely, initial solution pH (3-11), absorbent to cadmium ratio (12.5-162.5) and contact time (3-31 h), on cadmium removal were studied and optimized using a central composite type response surface methodology. The two factors that play a key role in the adsorption process are pH and absorbent dosage. Optimum adsorption conditions achieved at pH 9, absorbent to cadmium ratio of 125 and equilibrium contact time of 10 h, resulted in more than 99% cadmium removal. Kinetic studies revealed that a maximum removal can be achieved before 10 min of adsorption process following a pseudo-second order model. The selectivity study in bimetal aqueous systems using copper, lead and zinc metals showed the adsorption order of Cu2+ > Cd2+ > Zn2+ > Pb2+. The cadmium adsorption on activated coal waste was also found to follows the Temkin isotherm model with a correlation coefficient of 92.43%.