Artificial Neural Network Modeling of Cr(VI) Biosorption from Aqueous Solutions

Bisphenol A adsorption using modified aloe vera leaf-wastes derived bio-sorbents from aqueous solution: kinetic, isotherm, and thermodynamic studies

Reactive-oxygen-species are produced more often in the body when bisphenol A (BPA), an endocrine-disrupting-substance, is present. In this investigation, bio-sorbents from an aqueous solution adapted from Aloe-vera were used to survey BPA removal. Aloe-vera leaf wastes were used to create activated carbon, which was then analyzed using Fourier transform infrared (FTIR), Field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Thermogravimetric analysis (TGA), Zeta potential, and Brunauer-Emmett-Teller (BET) techniques. It was revealed that the adsorption process adheres to the Freundlich isotherm model with R2>0.96 and the pseudo-second-order kinetic model with R2>0.99 under ideal conditions (pH = 3, contact time = 45 min, concentration of BPA = 20 mg.L-1, and concentration of the adsorbent = 2 g.L-1). After five-cycle, the efficacy of removal was greater than 70%. The removal of phenolic-chemicals from industrial-effluent can be accomplished with the assistance of this adsorbent in a cost-effective and effective-approach.

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

It is very well known that traditional artificial neural networks (ANNs) are prone to falling into local extremes when optimizing model parameters. Herein, to enhance the prediction performance of Cu(II) adsorption capacity, a particle swarm optimized artificial neural network (PSO-ANN) model was developed. Prior to predicting the Cu(II) adsorption capacity of modified pomelo peels (MPP), experimental data collected by our research group were used to build a consistent database. Then, a PSO-ANN model was established to enhance the model performance by optimizing the ANN's weights and biases. Finally, the performances of the developed ANN and PSO-ANN models were deeply evaluated. The results of this investigation revealed that the proposed hybrid method did increase both the generalization ability and the accuracy of the predicted data of the Cu(II) adsorption capacity of MPPs when compared to the conventional ANN model. This PSO-ANN model thus offers an alternative methodology for optimizing the adsorption capacity prediction of heavy metals using agricultural waste biosorbents.

Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.

Highly effective water hyacinth (Eichhornia crassipes) waste-based functionalized sustainable green adsorbents for antibiotic remediation from wastewater

Azithromycin (AZIM) is considered as one of the most frequently prescribed antibiotics (ABs) in the world by medical professionals. This study explored, two novel, cheap and environmentally beneficial adsorbents i.e., alkali treated water hyacinth powder (AT-WHP) and graphene oxide-water hyacinth-polyvinyl alcohol (GO-WH-PVA) composite, fabricated from water hyacinth (Eichhornia crassipes) waste to remediate AZIM from wastewater. Biosorption experiments were performed by batch and packed-bed column studies and the adsorbents were characterized using various instrumental methods. The morpho-chemical profile of the adsorbents suggested noteworthy AZIM adsorption. AZIM adsorption data can be reasonably explained by pseudo second order (PSO) kinetic model with maximum regression coefficient (R² > 0.99) and lowest Marquardt's present standard deviation (MPSD) and root mean squared error (RMSE) values. The isotherm models recommended Langmuir and Temkin to be the best-fitted, providing highest regression coefficient and lowest error values. Conferring to Langmuir model, the theoretical highest adsorption potentials (q_max) were accounted to be 244.498 and 338.115 mg/g for AT-WHP and GO-WH-PVA, correspondingly, very close to experimental values (q_e, exp). AZIM adsorption processes were governed by the chemisorption mechanisms. The adsorbents had excellent regeneration potential and could be reused several times. In order to scale-up application of the adsorbents, performance of a 100 L packed-bed reactor was assessed and a breakthrough time of adsorption for GO-WH-PVA was 15 min in 5000 mg/L AZIM concentration. Thus, the absorbents synthesized in this study can be considered highly effective at removal of AZIM from wastewater.

Modeling the Biosorption Process of Heavy Metal Ions on Soybean-Based Low-Cost Biosorbents Using Artificial Neural Networks

Pollution of the environment with heavy metals requires finding solutions to eliminate them from aqueous flows. The current trends aim at exploiting the advantages of the adsorption operation, by using some low-cost sorbents from agricultural waste biomass, and with good retention capacity of some heavy metal ions. In this context, it is important to provide tools that allow the modeling and optimization of the process, in order to transpose the process to a higher operating scale of the biosorption process. This paper capitalizes on the results of previous research on the biosorption of heavy metal ions, namely Pb(II), Cd(II), and Zn(II) on soybean biomass and soybean waste biomass resulting from biofuels extraction process. The data were processed by applying a methodology based on artificial neural networks (ANNs) and evolutionary algorithms (EAs) capable of evolving ANN parameters. EAs are represented in this paper by the differential evolution (DE) algorithm, and a simultaneous training and determination of the topology is performed. The resulting hybrid algorithm, hSADE-NN was applied to obtain optimal models for the biosorption process. The expected response of the system addresses biosorption capacity of the biosorbent (q, mg/g), the biosorption efficiency (E, %), as functions of input parameters: pH, biosorbent dose (DS, mg/g), the initial concentration of metal in the solution (c0, mg/L), contact time (tc, h), and temperature (T, °C). Models were developed for the two output variables, for each metal ion, finding a high degree of accuracy. Furthermore, the combinations of input parameters were found which can lead to an optimal output in terms of biosorption capacity and biosorption efficiency.

Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/persulfate: Modeling by response surface methodology and artificial neural network

The present study investigates the synergistic performance of the sonophotolytic-activated ZnO/persulfate (US/UV/ZnO/PS) process in the decolorization of acid blue 113 (AB113) dye from aqueous solution and its feasibility for the treatment of real textile wastewater. Decolorization of AB113 solution was modeled by central composite design-response surface methodology (CCD-RSM) and artificial neural network (ANN) approaches and optimized by CCD-RSM and genetic algorithm (GA) approaches. Statistical metrics indicated that both CCD-RSM and ANN approaches seemed satisfactory. However, the results of statistical fit measures indicated a relative superiority of CCD-RSM as compared to the ANN approach. The results of optimization of the process parameters by CCD-RSM and GA approaches appeared to be similar as follows: pH = 6.1, reaction time = 25 min, US power density = 300 W/L, ZnO = 0.88 g/L and PS = 2.43 mmol/L. The synergistic effect of the hybrid US/UV/ZnO/PS process in comparison with its individual processes (US, UV, ZnO, and PS) was found to be 54.3%. Quenching experiments discovered that and HO are the main oxidizing radicals in a mildly acidic condition of the reaction solution. The removal efficiency of AB113 in the presence of some anions decreased in the order of bicarbonate > sulfate > phosphate > nitrate > chloride. Further, the reusability feasibility of ZnO showed that the ZnO material retained its photocatalytic property after five successive cycles of reusability test, while Zn²⁺ ion concentration in the reaction solution was measured to be 2.81 mg/L. The findings also indicated that the integrated process application suppresses extremely chemical and electrical costs. The study of the feasibility of the US/UV/ZnO/PS process in the treatment of real textile wastewater was done by determining COD, TOC and BOD₅/COD ratio. Results demonstrated that the 96.6 and 97.1% reduction of COD and TOC was achieved after 5 and 7 h reaction time, respectively. The obtained BOD₅/COD ratio changed from about 0.15 (for non-treated wastewater) to about 0.61 with increasing reaction time from zero to 90 min. In conclusion, the hybrid US/UV/ZnO/PS system can be proposed as a novel and promising approach to be utilized as a pretreatment technique before a biological treatment process to facilitate the biological treatment of recalcitrant textile wastewater.