Bioadsorption of Arsenic: An Artificial Neural Networks and Response Surface Methodological Approach

Adsorption of ibuprofen using waste coffee derived carbon architecture: Experimental, kinetic modeling, statistical and bio-inspired optimization

Pharmaceuticals in water are a growing environmental concern, as they can harm aquatic life and human health. To address this issue, an adsorbent made from coffee waste that effectively removes ibuprofen (a common pharmaceutical pollutant) from wastewater was developed. The experimental adsorption phase was planned using a Design of Experiments approach with Box-Behnken strategy. The relation between the ibuprofen removal efficiency and various independent variables, including adsorbent weight (0.01-0.1 g) and pH (3-9), was evaluated via a regression model with 3-level and 4-factors using the Response surface methodology (RSM) . The optimal ibuprofen removal was achieved after 15 min using 0.1 g adsorbent at 32.4 °C and pH = 6.9. Moreover, the process was optimized using two powerful bio-inspired metaheuristics (Bacterial Foraging Optimization and Virus Optimization Algorithm). The adsorption kinetics, equilibrium, and thermodynamics of ibuprofen onto waste coffee-derived activated carbon were modeled at the identified optimal conditions. The Langmuir and Freundlich adsorption isotherms were implemented to investigate adsorption equilibrium, and thermodynamic parameters were also calculated. According to the Langmuir isotherm model, the adsorbent's maximum adsorption capacity was 350.00 mg g-1 at 35 °C. The findings revealed that the ibuprofen adsorption was well-matched with the Freundlich isotherm model, indicating multilayer adsorption on heterogeneous sites. The computed positive enthalpy value showed the endothermic nature of ibuprofen adsorption at the adsorbate interface.

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Application of neural network in metal adsorption using biomaterials (BMs): a review

ANN models for predicting wastewater treatment efficacy of biomaterial adsorbents.

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

Modelling and process optimization for biodiesel production from Nannochloropsis salina using artificial neural network

In the present investigation, calcium oxide solid nanocatalyst derived from the egg shell and Nannochloropsis salina were used for the production of biodiesel. The morphological characteristics and functional groups of synthesized nanocatalyst was characterized by SEM and FTIR analysis. Process variables optimization for biodiesel production was studied using RSM and ANN. The R² values for RSM and ANN was found to be 0.8751 and 0.957 which showed that the model was significantly fit with the experimental data. The maximum FAME conversion for the synthesized nanocatalyst CaO was found to be 86.1% under optimum process conditions (nanocatalyst amount: 3% (w/v); oil to methanol ratio 1:6 (v/v); reaction temperature: 60 °C; reaction time 55 min). Concentration of FAME present in biodiesel was identified by GC-MS analysis.

Enhancing arsenic sequestration on ameliorated waste molasses nanoadsorbents using response surface methodology and machine-learning frameworks

The development of a novel nanobiosorbent derived from waste molasses for the adsorptive removal of arsenic (As) has been attempted in this study. Waste molasses were chemically ameliorated through a solvothermal route for the incorporation of iron oxide, thereby producing iron oxide incorporated carbonaceous nanomaterial (IOCN). Synthesis of IOCN was confirmed through transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and atomic emission spectroscopy (AES) analysis. The surface area and porous behavior of IOCN were elucidated by Brunauer-Emmett-Teller (BET) assessments. The experimental conditions for adsorption were first modeled using response surface methodology (RSM) based on the central composite design (CCD), considering the parameters: adsorbate dosage, adsorbent dosage, pH, and contact time. RSM optimizations were improved upon using a three-layer feed-forward multilayer perceptron (MLP) based Artificial Neural Network (ANN) model. Optimization through ANN model resulted in the increase of the maximal As adsorption efficiency to ~ 96% for IOCN. The IOCN isotherm plots show the best fit for the Sips isotherm, and the reaction kinetics follows the pseudo-second-order model, indicating the chemisorption mechanism for As adsorption. Evidence for direct coordination of As to the surface of adsorbents was further confirmed by FTIR spectroscopic studies before and after As adsorption. The high adsorption efficiencies and the low-cost facile synthesis of the IOCN nanosorbent from agro-industrial waste indicate their potential for commercial applications.

Challenges of arsenic removal from municipal wastewater by coagulation with ferric chloride and alum

Discharge of treated municipal wastewater containing arsenic (As) may cause adverse effects on the environment and drinking water sources. Arsenic concentrations were measured throughout the treatment systems at two municipal wastewater plants in New Jersey, USA. The efficiency of As removal by ferric chloride and alum coagulants were evaluated. Besides, the effects of suspended solids in the mixed liquor, pH, and orthophosphate (PO₄^3-) on As removal were investigated. The total recoverable As (TAs) concentrations in the influent and effluent of Plant A were in the ranges of 2.00-3.00 and 1.50-2.30 μg/L, respectively. The results indicated that <30% of the As was removed by the conventional biological wastewater treatment processes. The influent and effluent TAs concentrations at Plant B was below 1.00 μg/L. The bench-scale coagulation results demonstrated for the first time that the coagulation treatment could not effectively remove As from the municipal wastewater to <2.00 μg/L. Very high doses of the coagulants (8 and 40 mg/L of Fe(III) or Al(III)) were required to reduce the TAs from 2.84 and 8.61 μg/L in the primary clarifier effluent and arsenate-spiked effluent samples to <2.00 μg/L, respectively, which could be attributed to the high concentrations of PO₄^3- and dissolved organic matters (DOM) in the wastewater. The protein DOM in wastewater may negatively impact removal efficiencies more than the DOM in natural water, which mainly consists of humic substances. Furthermore, an artificial neural network was constructed to determine the relative importance of different parameters for As removal. Under the experimental conditions, the importance followed the order: coagulant dose>dissolved PO₄^3- > initial As concentration > pH. The findings of this study will help develop effective treatment processes to remove As from municipal wastewater.

Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica

Chlorogenic acid (CGA), a bioactive compound commonly found in plants, has been demonstrated possessing nutraceutical potential in recent years. However, the more critical issue concerning how to improve production efficacy of CGA is still limited. It is a challenge to harvest a large amount of CGA without prolonging extraction time. In this study, the feasibility of using ultrasound for CGA extraction from Lonicera japonica was investigated. A central composite design (CCD) was employed to evaluate the effects of the operation parameters, including temperature, ethanol concentration, liquid to solid ratio, and ultrasound power on CGA yields. Meanwhile, the process of ultrasound-assisted extraction was optimized through modeling response surface methodology (RSM) and artificial neural network (ANN). The data indicated that CGA was efficiently extracted from the flower of Lonicera japonica by ultrasound assistance. The optimal conditions for the maximum extraction of CGA were as follows: The temperature at 33.56 °C, ethanol concentration at 65.88%, L/S ratio at 46:1 mL/g and ultrasound power at 150 W. ANN possessed greater optimization capacity than RSM for fitting experimental data and predicting the extraction process to obtain a maximum CGA yield. In conclusion, the process of ultrasound-assisted extraction can be well established by a methodological approach using either RSM or ANN, but it is worth mentioning that the ANN model used here showed the superiority over RSM for predicting and optimizing.

Biosorptive removal of Zn(II) ions by Pongamia oil cake (Pongamia pinnata) in batch and fixed-bed column studies using response surface methodology and artificial neural network

Design of experiment and artificial neural networks (ANN) have been effectively employed to predict the rate of uptake of Zn(II) ions onto defatted pongamia oil cake. Four independent variables such as, pH (2.0-7.0), initial concentration of Zn(II) ions (50-500 mg/L), temperature (30ºC-50 °C), and dosage of biosorbent (1.0-5.0 g/L) were used for the batch mode while the three independent variables viz. flowrate, initial concentration of Zn(II) ions and bed height were employed for the continuous mode. Second-order polynomial equations were then derived to predict the Zn(II) ion uptake rate. The optimum conditions for batch studies was found to be pH: 4.45, metal ion concentration: 462.48 mg/L, dosage: 2.88 g/L, temperature: 303 K and on the other hand the column studies flow rate: 5.59 mL/min, metal ion concentration: 499.3 mg/L and bed height: 14.82 cm. Under these optimal condition, the adsorption capacity was 80.66 mg/g and 66.29 mg/g for batch and column studies, respectively. The same data was fed to train a feed-forward multilayered perceptron, using MATLAB to develop the ANN based model. The predictive capabilities of the two methodologies were compared, by means of the absolute average deviation (AAD) (4.57%), model predictive error (MPE) (4.15%), root mean square error (RMSE) (3.19), standard error of prediction (SEP) (4.23) and correlation coefficient (R) (0.99) for ANN and for RSM AAD (16.27%), MPE (21,25%), RMSE (13.15%), SEP and R (0.96) by validation data. The findings suggested that compared to the prediction ability of RSM model, the properly trained ANN model has better prediction ability. In batch studies, equilibrium data was used to determine the isotherm constants and first and second order rate constants. In column, bed depth service time (BDST) and Thomas model was used to fit the obtained column data.

Development of graphene oxide/chitosan composite membrane on ceramic support for atrazine remediation by MBR process

Graphene oxide (GO)-based composite ultrafiltration (UF) membranes were prepared on macroporous ceramic support tubes following a new way. Chitosan was used as an intermediate matrix between the substrate and GO coating. It has hydroxyl and amine groups, which enhances its film forming capacity with hydrophilic GO. This led us to use them as precursors for membrane development. Process efficiency of the prepared UF membrane was assessed in terms of the removal of toxic pesticide atrazine in side-stream membrane bioreactor (MBR) processes. Response surface methodology (RSM) was used to optimize the atrazine biodegradation efficiency. Enhanced atrazine removal of > 95% was obtained in the MBR treatment at the optimized conditions. Hermia's model equations were applied to analyze the mechanism of membrane fouling in the UF-MBR system. The influencing parameters were studied in details and pneumatic backpulsing was applied to minimize fouling in the UF-MBR system by statistical analysis. Mixed liquor suspended solids (MLSS) was found to affect both atrazine biodegradation and membrane fouling; hence, its effect was thoroughly analyzed. The developed process hence proved to be highly proficient in terms of such organic pesticides removal for long-term operations.

Characterization, preparation, and uses of nanomagnetic Fe3O4 impregnated onto fish scale as more efficient adsorbent for Cu2+ ion adsorption

In this research, the Cu²⁺ ion adsorption from aqueous solution was investigated by fish scale (FS) and nanomagnetic (Fe₃O₄) loaded fish scale (MFS) from fishery biomass. We characterized the structure and morphology of synthesized magnetic adsorbent by Fourier transform infrared spectroscopy (FTIR), FESEM, and XRD. The FTIR and XRD tests confirmed the collagen fibers, apatite crystals, and nanomagnetite particles presence in the MFS structure. The isotherm models of Langmuir, Freundlich, and Dubinin-Radushkevich were exerted to the empirical equilibrium data, by which was found that the Langmuir equation have the best fit to the experimental data in comparison to the other isotherm equations. The maximum capacities of monolayer coverage of FS and MFS for adsorption of Cu²⁺ ions were achieved, respectively, 61.73 and 103.1 mg g^-1 based on Langmuir isotherm at 45 °C. It was also discovered that the Cu²⁺ ion adsorption onto MFS was totally a physisorption-controlled process. It was perceived that the model of pseudo-second order rate kinetics also could be applied for predicting of studied adsorption processes. Here, the adsorption was a spontaneous and endothermic process because of the negative and the positive values of ∆G⁰ and ∆H⁰, respectively. The reusability potential of the used adsorbents was studied, so that the results showed an efficiency of 76.5 and 83.92% for FS and MFS, respectively, after four adsorption-desorption cycles.

Removal of Zinc from Aqueous Solution by Optimized Oil Palm Empty Fruit Bunches Biochar as Low Cost Adsorbent

This study aims to produce optimized biochar from oil palm empty fruit bunches (OPEFB), as a green, low cost adsorbent for uptake of zinc from aqueous solution. The impact of pyrolysis conditions, namely, highest treatment temperature (HTT), heating rate (HR), and residence time (RT) on biochar yield and adsorption capacity towards zinc, was investigated. Mathematical modeling and optimization of independent variables were performed employing response surface methodology (RSM). HTT was found to be the most influential variable, followed by residence time and heating rate. Based on the central composite design (CCD), two quadratic models were developed to correlate three independent variables to responses. The optimum production condition for OPEFB biochar was found as follows: HTT of 615°C, HR of 8°C/min, and RT of 128 minutes. The optimum biochar showed 15.18 mg/g adsorption capacity for zinc and 25.49% of yield which was in agreement with the predicted values, satisfactory. Results of the characterization of optimum product illustrated well-developed BET surface area and porous structure in optimum product which favored its sorptive ability.

Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process

A review on the application of response surface methodology (RSM) and artificial neural networks (ANN) in biosorption modelling and optimization is presented. The theoretical background of the discussed methods with the application procedure is explained. The paper describes most frequently used experimental designs, concerning their limitations and typical applications. The paper also presents ways to determine the accuracy and the significance of model fitting for both methodologies described herein. Furthermore, recent references on biosorption modelling and optimization with the use of RSM and the ANN approach are shown. Special attention was paid to the selection of factors and responses, as well as to statistical analysis of the modelling results.

Development of experimental design approach and ANN-based models for determination of Cr(VI) ions uptake rate from aqueous solution onto the solid biodiesel waste residue

In the present work, the evaluation capacities of two optimization methodologies such as RSM and ANN were employed and compared for predication of Cr(VI) uptake rate using defatted pongamia oil cake (DPOC) in both batch and column mode. The influence of operating parameters was investigated through a central composite design (CCD) of RSM using Design Expert 8.0.7.1 software. The same data was fed as input in ANN to obtain a trained the multilayer feed-forward networks back-propagation algorithm using MATLAB. The performance of the developed ANN models were compared with RSM mathematical models for Cr(VI) uptake rate in terms of the coefficient of determination (R(2)), root mean square error (RMSE) and absolute average deviation (AAD). The estimated values confirm that ANN predominates RSM representing the superiority of a trained ANN models over RSM models in order to capture the non-linear behavior of the given system.

Plate column biosorption of Cu(II) on membrane-type biosorbent (MBS) of Penicillium biomass: optimization using statistical design methods

Based on the coupling of biosorption and membrane separation, a low cost membrane-type biosorbent (MBS) of Penicillium biomass was prepared. The surface morphology, pore properties and functional groups were studied via the characterization of MBS. Batch biosorption experiments indicated the maximum biosorption capacity of Cu(II) on MBS was 126.58 mg/g and about 90% of that on chitosan membrane. A plate column reactor filled with multi-layer of MBS was built for treatment of wastewater contaminated by Cu(II). The biosorption process factors were screened using Plackett-Burman design and three significant variables were selected for further optimization via response surface methodology (RSM) based on Box-Behnken model. A statistically second-order polynomial model was constructed with the error below 1.22%, on the basis of which the three-dimensional response surfaces were plotted. The prepared membrane-type biosorbent could be used successfully for 10 biosorption-desorption-regeneration cycles without decreasing its biosorption ability obviously.

Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin

In this study, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to develop an approach for the evaluation of heavy metal biosorption process. A batch sorption process was performed using Nigella sativa seeds (black cumin), a novel and natural biosorbent, to remove lead ions from aqueous solutions. The effects of process variables which are pH, biosorbent mass, and temperature, on the sorbed amount of lead were investigated through two-levels, three-factors central composite design (CCD). Same design was also utilized to obtain a training set for ANN. The results of two methodologies were compared for their predictive capabilities in terms of the coefficient of determination-R(2) and root mean square error-RMSE based on the validation data set. The results showed that the ANN model is much more accurate in prediction as compared to CCD.