A sustainable Decision Support System for soil bioremediation of toluene incorporating UN sustainable development goals.

Decision Support System (DSS) is a novel approach for smart, sustainable controlling of environmental phenomena and purification processes. Toluene is one of the most widely used petroleum products, which adversely impacts on human health. In this study, Fusarium Solani fungi are utilized as the engine of the toluene bioremediation procedure for the monitoring part of DSS. Experiments are optimized by Central Composite Design (CCD) - Response Surface Methodology (RSM), and the behavior of the mentioned fungi is estimated by M5 Pruned model tree (M5P), Gaussian Processes (GP), and Sequential Minimal Optimization (SMOreg) algorithms as the prediction section of DSS. Finally, the control stage of DSS is provided by integrated Petri Net modeling and Failure Modes and Effects Analysis (FMEA). The findings showed that Aeration Intensity (AI) and Fungi load/Biological Waste (F/BW) are the most influential mechanical and biological factors, with P-value of 0.0001 and 0.0003, respectively. Likewise, the optimal values of main mechanical parameters include AI, and the space between pipes (S) are equal to 13.76 m3/h and 15.99 cm, respectively. Also, the optimum conditions of biological features containing F/BW and pH are 0.001 mg/g and 7.56. In accordance with the kinetic study, bioremediation of toluene by Fusarium Solani is done based on a first-order reaction with a 0.034 s-1 kinetic coefficient. Finally, the machine learning practices showed that the GP (R2 = 0.98) and M5P (R2 = 0.94) have the most precision for predicting Removal Percentage (RP) for mechanical and biological factors, respectively. At the end of the present research, it is found that by controlling seven possible risk factors in bioremediation operation through the FMEA- Petri Net technique, efficiency of the process can be adjusted to optimum value.

Cadmium and copper heavy metal treatment from water resources by high-performance folic acid-graphene oxide nanocomposite adsorbent and evaluation of adsorptive mechanism using computational intelligence, isotherm, kinetic, and thermodynamic analyses.

In this paper, folic acid-coated graphene oxide nanocomposite (FA-GO) is used as an adsorbent for the treatment of heavy metals including cadmium (Cd2+) and copper (Cu2+) ions. As such, graphene oxide (GO) is modified by folic acid (FA) to synthesize FA-GO nanocomposite and characterized by the atomic force microscopy (AFM), Fourier transform-infrared (FT-IR) spectrophotometry, scanning electron microscopy (SEM), and C/H/N elemental analyses. Also, computational intelligence tests are used to study the mechanism of the interaction of FA molecules with GO. Based on the results, FA molecules formed a strong π-π stacking, chemical, and hydrogen bond interactions with functional groups of GO. Main parameters including pH of the sample solution, amounts of adsorbent, and contact time are studied and optimized by the Response Surface Methodology Based on Central Composite Design (RSM-CCD). In this study, the equilibrium of adsorption is appraised by two (Langmuir and Freundlich and Temkin and D-R models) and three parameter (Sips, Toth, and Khan models) isotherms. Based on the two parameter evaluations, Langmuir and Freundlich models have high accuracy according to the R2 coefficient (more than 0.9) in experimental curve fittings of each pollutant adsorption. But, multilayer adsorption of each contaminant onto the FA-GO adsorbent (Freundlich equation) is demonstrated by three parameter isotherm analysis. Also, isotherm calculations express maximum computational adsorption capacities of 103.1 and 116.3 mg g-1 for Cd2+ and Cu2+ ions, correspondingly. Kinetic models are scrutinized and the outcomes depict the adsorption of both Cd2+ and Cu2+ followed by the pseudo-second-order equation. Meanwhile, the results of the geometric model illustrate that the variation of adsorption and desorption rates do not have any interfering during the adsorption process. Finally, thermodynamic studies show that the adsorption of Cu2+ and Cd2+ onto the FA-GO nanocomposite is an endothermic and spontaneous process.

Experimental investigation and mathematical modeling for microbial removal using potassium permanganate as an oxidant-case study: water treatment plant No. 1, Mashhad, Iran.

Preoxidation is an important unit process which can partially remove organic and microbial contaminations. Due to the high concentrations of organic matter entering the water treatment plant, originating from surface water resources, preoxidation by using chlorinated compounds may increase the possibility of trihalomethane (THM) formation. Therefore, in order to reduce the concentration of THMs, different alternatives such as injection of potassium permanganate are utilized. The present study attempts to investigate the efficiency of the microbial removal from raw water entering the water treatment plant No. 1 in Mashhad, Iran, through various doses of potassium permanganate. Then, an examination of the predictive models is done in order to indicate the residual Escherichia coli and total coliform resulted from injecting the potassium permanganate. Finally, the coefficients of the proposed models were optimized using the genetic algorithm. The results of the study show that 0.5 mg L-1 of potassium permanganate would remove 50% of total coliform as well as 80% of Escherichia coli in the studied water treatment plant. Also, assessing the performance of different models in predicting the residual microbial concentration after injection of potassium permanganate suggests the Gaussian model as the one resulting the highest conformity. Moreover, it can be concluded that employing smart models leads to an optimization of the injected potassium permanganate at the levels of 27% and 73.5%, for minimum and maximum states during different seasons of a year, respectively.