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

Enhanced phytoremediation of vanadium using coffee grounds and fast-growing plants: Integrating machine learning for predictive modeling

Vanadium (V) contamination posed a significant environmental challenge, while phytoremediation offered a sustainable solution. Phytoremediation performance was often limited by the slow growth cycles of traditional plants. A novel approach to enhancing V phytoremediation by integrating coffee grounds with fast-growing plants such as barley grass, wheat grass, and ryegrass was investigated in this study. The innovative use of coffee grounds leveraged not only their nutrient-rich composition but also their ability to reduce oxidative stress in plants, thereby significantly boosting phytoremediation efficiency. Notably, ryegrass achieved 48.7% V5+ removal within 6 d with initial 20 mg/L V5+ (0.338 mg/L·d·g ryegrass). When combined with coffee grounds, V5+ removal by using wheat grass increased substantially, rising from 30.51% to 62.91%. Gradient Boosting and XGBoost models, trained with optimized parameters including a learning rate of 0.1, max depth of 3, and n_estimators of 300, were employed to predict and optimize V5+ concentrations in the phytoremediation process. These models were evaluated using mean squared error (MSE) and coefficient of determination (R2) metrics, achieving high predictive accuracy (R2 = 0.95, MSE = 1.20). Feature importance analysis further identified the initial V5+ concentration and experimental duration as the most significant factors influencing the model's predictions. The addition of coffee grounds not only mitigated the stress of heavy metals on ryegrass, leading to significant reductions in CAT (87.2%), POD (98.8%), and SOD (39.2%) activities in ryegrass roots, but also significantly altered the microbial community abundance in the plant roots. This research provided an innovative enhancement to traditional phytoremediation methods, and established a new paradigm for using machine learning to predict and optimize V5+ remediation outcomes. The effectiveness of this technology in multi-metal polluted environments warrants further investigation in the future.

Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects

Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided.

A unified framework of response surface methodology and coalescing of Firefly with random forest algorithm for enhancing nano-phytoremediation efficiency of chromium via in vitro regenerated aquatic macrophyte coontail (Ceratophyllum demersum L.)

Nano-phytoremediation is a novel green technique to remove toxic pollutants from the environment. In vitro regenerated Ceratophyllum demersum (L.) plants were exposed to different concentrations of chromium (Cr) and exposure times in the presence of titania nanoparticles (TiO2NPs). Response surface methodology was used for multiple statistical analyses like regression analysis and optimizing plots. The supplementation of NPs significantly impacted Cr in water and Cr removal (%), whereas NP × exposure time (T) statistically regulated all output parameters. The Firefly metaheuristic algorithm and the random forest (Firefly-RF) machine learning algorithms were coalesced to optimize hyperparameters, aiming to achieve the highest level of accuracy in predicted models. The R2 scores were recorded as 0.956 for Cr in water, 0.987 for Cr in the plant, 0.992 for bioconcentration factor (BCF), and 0.957 for Cr removal through the Firefly-RF model. The findings illustrated superior prediction performance from the random forest models when compared to the response surface methodology. The conclusion is drawn that metal-based nanoparticles (NPs) can effectively be utilized for nano-phytoremediation of heavy metals. This study has uncovered a promising outlook for the utilization of nanoparticles in nano-phytoremediation. This study is expected to pave the way for future research on the topic, facilitating further exploration of various nanoparticles and a thorough evaluation of their potential in aquatic ecosystems.

Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges

Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies.

Assessment of Iron Biosorption Potential by Live and Dead Biomass of Bacillus subtilis (MN093305) from Aqueous Solution

Heavy metals polluted aquatic ecosystems and become a global environmental issue due to their toxic effect on all forms of ecosystems and further on all forms of life. Heavy metals are non- degradable and accumulated in different life forms by accumulating in the food chain; this increases the need for the development of a sustainable method for the removal of these metals. Biosorption is an eco-friendly and cost-effective convenient technique of heavy metal bioremediation from the contaminated aquatic ecosystem. The current investigation involves biosorption of iron using Bacillus subtilis strain (MN093305) isolated from Ganga river at different physical parameters with the highest rate of biosorption was 96.64%, 98.91%, 97.88%, and 99.44% at pH 5, 60 min incubation period, 35 °C temperature and 2.5 mg/ml of biomass respectively for dead biomass. Living biomass biosorption rate was 87.32%, 96.74%, 96.94% and 95.02% at pH 7, 72 h, 35 °C and 2.5 mg/ml respectively. Functional groups involved in the biosorption of iron by Bacillus subtilis were fitted to a second-order kinetic model. Langmuir and Freundlich's isotherm are used to evaluate data; both isotherms indicate iron absorption as a favorable process.

Kinetic analysis and mechanism of nitrate, calcium, and cadmium removal using the newly isolated Pseudomonas sp. LYF26

The co-existence of heavy metals and nitrate (NO3--N) pollutants in wastewater has been a persistent global concern for a long time. A strain LYF26, which can remove NO3--N, calcium (Ca(II)), and cadmium (Cd(II)) simultaneously, was isolated to explore the properties and mechanisms of synergistic contaminants removal. Different conditions (Cd(II) and Ca(II) concentrations and pH) were optimized by Zero-, Half-, and First-order kinetic analyses to explore the environmental parameters for the optimal effect of strain LYF26. Results of the kinetic analyses revealed that the optimal culture conditions for strain LYF26 were pH of 6.5, Cd(II) and Ca(II) concentrations of 3.00 and 180.00 mg L-1, accompanied by Ca(II), Cd(II), and NO3--N efficiencies of 53.10%, 90.03%, and 91.45%, respectively. The removal mechanisms of Cd(II) using strain LYF26 as a nucleation template were identified as biomineralization, lattice substitution, and co-precipitation. The differences and changes of dissolved organic matter during metabolism were analyzed and the results demonstrated that besides the involvement of extracellular polymeric substances in the precipitation of Cd(II) and Ca(II), the high content of humic acid-like species revealed a remarkable contribution to the denitrification process. This study is hopeful to contribute a theory for further developing microbially induced calcium precipitation used to treat complex polluted wastewater.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

ASPAD dynamic simulation and artificial neural network for atenolol adsorption in GGSWAC packed bed column

This study aimed to assess the dynamic simulation models provided by Aspen adsorption (ASPAD) and artificial neural network (ANN) in understanding the adsorption behavior of atenolol (ATN) on gasified Glyricidia sepium woodchips activated carbon (GGSWAC) within fixed bed columns for wastewater treatment. The findings demonstrated that increasing the bed height from 1 to 3 cm extended breakthrough and exhaustion times while enhancing adsorption capacity. Conversely, higher initial ATN concentrations resulted in shorter breakthrough and exhaustion times but increased adsorption capacity. Elevated influent flow rates reduced breakthrough and exhaustion times while maintaining constant adsorption capacity. The ASPAD software demonstrated competence in accurately modeling the crucial exhaustion points. However, there is room for enhancement in forecasting breakthrough times, as it exhibited deviations ranging from 6.52 to 239.53% when compared to the actual experimental data. ANN models in both MATLAB and Python demonstrated precise predictive abilities, with the Python model (R2 = 0.985) outperforming the MATLAB model (R2 = 0.9691). The Python ANN also exhibited superior fitting performance with lower MSE and MAE. The most influential factor was the initial ATN concentration (28.96%), followed by bed height (26.39%), influent flow rate (22.43%), and total effluent time (22.22%). The findings of this study offer an extensive comprehension of breakthrough patterns and enable accurate forecasts of column performance.

Artificial intelligence-based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L

Water bodies or aquatic ecosystem are susceptible to heavy metal accumulation and can adversely affect the environment and human health especially in underdeveloped nations. Phytoremediation techniques of water bodies using aquatic plants or macrophytes are well established and are recognized as eco-friendly world over. Phytoremediation of heavy metals and other pollutants in aquatic environments can be achieved by using Ceratophyllum demersum L. - a well-known floating macrophyte. In vitro regenerated plants of C. demersum (7.5 g/L) were exposed to 24, 72, and 120 h to 0, 0.5, 1.0, 2.0, and 4.0 mg/L of cadmium (CdSO₄·8H₂O) in water. Results revealed significantly different relationship in terms of Cd in water, Cd uptake by plants, bioconcentration factor (BCF), and Cd removal (%) from water. The study showed that Cd uptake by plants and BCF values increased significantly with exposure time. The highest BCF value (3776.50) was recorded for plant samples exposed to 2 mg/L Cd for 72 h. Application of all Cd concentrations and various exposure duration yielded Cd removal (%) between the ranges of 93.8 and 98.7%. These results were predicted through artificial intelligence-based models, namely, random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). The tested models predicted the results accurately, and the attained results were further validated via three different performance metrics. The optimal regression coefficient (R²) for the models was recorded as 0.7970 (Cd water, mg/L), 0.9661 (Cd plants, mg/kg), 0.9797 bioconcentration factor (BCF), and 0.9996 (Cd removal, %), respectively. These achieved results suggest that in vitro regenerated C. demersum can be efficaciously used for phytoremediation of Cd-contaminated aquatic environments. Likewise, the proposed modeling of phytoremediation studies can further be employed more comprehensively in future studies aimed at data prediction and optimization.

Big data and machine learning driven bioprocessing - Recent trends and critical analysis

Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.

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.

The performance and mechanism of simultaneous removal of calcium and heavy metals by Ochrobactrum sp. GMC12 with the chia seed (Salvia hispanica) gum as a synergist

A bacterium Ochrobactrum sp. GMC12, capable of biomineralization and denitrification, was employed to investigate the performance and mechanism of heavy metals removal. A chia seeds (Salvia hispanica) gum was proposed as a synergist for the first time. The results showed that strain GMC12 reduced Ca²⁺, Cd²⁺, Zn²⁺, and nitrate by 83.38, 98.89, 98.95, and 100% (2.09, 0.29, 0.55, and 0.79 mg L^-1 h^-1), respectively, over 96 h continuous determination experiments. The concentration gradient test revealed that strain GMC12 would effectively remove Cd²⁺ and Zn²⁺ by 99.80 and 99.91% (0.67 and 1.35 mg L^-1 h^-1), respectively, under the synergistic effect of gum (1.0%, w/v). The SEM-EDS and XRD manifested that Ca²⁺, HMs ions, and anionic groups coated on the bacteria surface to form CaCO₃, Ca₅(PO₄)₃OH, CdCO₃, Cd₅(PO₄)₃OH, ZnCO₃, and Zn₂(PO₄)OH. The fluorescence spectrometry and fourier transform infrared (FTIR) spectra illustrated that extracellular polymeric substance (EPS) was the key product for the nucleation site of bacteria, and the gum promoted the accumulation of bio-precipitates and accelerated the removal of HMs. In this research, Ochrobactrum sp. GMC12 exhibited great potential in wastewater treatment and chia seeds gum would go deep into material preparation and wastewater treatment due to its non-toxic nature, high viscosity, and advantageous morphology.

Optimization of Heavy Metals Biosorption via Artificial Neural Network: A Case Study of Cobalt (II) Sorption by Pseudomonas alcaliphila NEWG-2

The definitive screening design (DSD) and artificial neural network (ANN) were conducted for modeling the biosorption of Co(II) by Pseudomonas alcaliphila NEWG-2. Factors such as peptone, incubation time, pH, glycerol, glucose, K2HPO4, and initial cobalt had a significant effect on the biosorption process. MgSO4 was the only insignificant factor. The DSD model was invalid and could not forecast the prediction of Co(II) removal, owing to the significant lack-of-fit (P < 0.0001). Decisively, the prediction ability of ANN was accurate with a prominent response for training (R2 = 0.9779) and validation (R2 = 0.9773) and lower errors. Applying the optimal levels of the tested variables obtained by the ANN model led to 96.32 ± 2.1% of cobalt bioremoval. During the biosorption process, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and scanning electron microscopy confirmed the sorption of Co(II) ions by P. alcaliphila. FTIR indicated the appearance of a new stretching vibration band formed with Co(II) ions at wavenumbers of 562, 530, and 531 cm-1. The symmetric amino (NH2) binding was also formed due to Co(II) sorption. Interestingly, throughout the revision of publications so far, no attempt has been conducted to optimize the biosorption of Co(II) by P. alcaliphila via DSD or ANN paradigm.

Batch and continuous adsorption of Cu(II) and Zn(II) ions from aqueous solution on bi-functionalized sugarcane-based biosorbent

A new one-pot synthesis method optimized by a 2³ experimental design was developed to prepare a biosorbent, sugarcane bagasse cellulose succinate pyromellitate (SBSPy), for the removal of Cu(II) and Zn(II) from single-component aqueous solutions, in batch and continuous modes. The bi-functionalization of the biosorbent with ligands of different chemical structures increased its selectivity, improving its performance for removing pollutants from contaminated water. The succinate moiety favored Cu(II) adsorption, while the pyromellitate moiety favored Zn(II) adsorption. Sugarcane bagasse (SB) and SBSPy were characterized using several techniques. Analysis by ¹³C Multi-CP SS NMR and FTIR revealed the best order of addition of each anhydride that maximized the chemical modification of SB. The maximum adsorption capacities of SBSPy for Cu(II) and Zn(II), in batch mode, were 1.19 and 0.95 mmol g^-1, respectively. Homogeneous surface diffusion, intraparticle diffusion, and Boyd models were used to determine the steps involved in the adsorption process. Isothermal titration calorimetry was used to assess changes in enthalpy of adsorption as a function of SBSPy surface coverage. Fixed-bed column adsorption of Cu(II) and Zn(II) was performed in three cycles, showing that SBSPy has potential to be used in water treatment. Breakthrough curves were well fitted by the Thomas and Bohart-Adams models.

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.

Assessment of heavy metal accumulations and health risk potentials in tomatoes grown in the discharge area of a municipal wastewater treatment plant

Some heavy metals were detected in organs of the tomatoes grown in the discharge area of effluents of a municipal wastewater treatment plant. Also, the health risk potentials of heavy metals in the tomatoes consumed by human were investigated. The highest concentrations for Cu, Ni, Cr, Mn and Pb were followed the order of root>leaf>stem>fruit. When the bioconcentration factors values calculated for bioconcentration of metals from effluent to stem and root were examined, the highest values were determined for Cu. When translocation factors values are examined, the highest translocation from root to leaf was determined for Cd. The highest translocation from stem to leaf was determined for Pb. The estimated total exposure dose for male, female and children was listed as Zn>Mn>Cu>Cr>Ni>Pb>Cd. In terms of dietary, we can list the non-carcinogenic risks of heavy metals as children> female> male. The highest carcinogenic risk was calculated for Cr via dietary intake.

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

ANN models for predicting wastewater treatment efficacy of biomaterial adsorbents.

Definitive Screening Design and Artificial Neural Network for Modeling a Rapid Biodegradation of Date Palm Fronds by a New Trichoderma sp. PWN6 into Citric Acid

Generally, the bioconversion of lignocellulolytics into a new biomolecule is carried out through two or more steps. The current study used one-step bioprocessing of date palm fronds (DPF) into citric acid as a natural product, using a pioneer strain of Trichodermaharzianum (PWN6) that has been selected from six tested isolates based on the highest organic acid (OA) productivity (195.41 µmol/g), with the lowest amount of the released glucose. Trichoderma sp. PWN6 was morphologically and molecularly identified, and the GenBank accession number was MW78912.1. Both definitive screening design (DSD) and artificial neural network (ANN) were applied, for the first time, for modeling the bioconversion process of DPF. Although both models are capable of making accurate predictions, the ANN model outperforms the DSD model in terms of OA production, as ANN is characterized by a higher value of R² (0.963) and validation R² (0.967), and lower values of the RMSE (13.44), MDA (11.06), and SSE (9749.5). Citric acid was the only identified OA as was confirmed by GC-MS and UPLC, with a total of 1.5%. In conclusion, DPF together with T. harzianum PWN6 is considered an excellent new combination for citric acid biosynthesis, after modeling with artificial intelligence procedure.

A multilayer perceptron-based prediction of ammonium adsorption on zeolite from landfill leachate: Batch and column studies

In this study, multilayer perceptron (MLP) artificial neural network was used to predict the adsorption rate of ammonium on zeolite. pH, inlet ammonium concentration, contact time, temperature, dosage of adsorbent, agitation speed, and particle size in the batch experiments were used as independent variables while flow rate and particle size in column mode were investigated. In MLP application, different architecture structures were tried and the architecture structures with the highest predictive performance were determined. To comparatively evaluate the predictive capabilities of MLP based prediction tool, Response Surface Methodology (RSM) was utilized. When the results were evaluated with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values (<1%) for almost all experiments, it was seen that MLP-based prediction tool produces better predictions than RSM. The scatter plots showed that predictions and actual values were quite compatible. Both regression and determination coefficients were interpreted by creating a regression of the predictions against the actual values and these coefficients were obtained as pretty close to 1. The outstanding performance of MLP in out-of-sample data sets without the need for additional experiment demonstrate that MLP can be effectively and reliably used in cases where experimental setups are difficult or costly.

Chitosan nanocomposites for water treatment by fixed-bed continuous flow column adsorption: A review

Nowadays, access to clean water sources worldwide and particularly in Southern Africa is inadequate because of its pollution by organic, inorganic, and microorganism contaminants. A range of conventional water treatment techniques has been used to resolve the problem. However, these methods are currently facing the confronts posed by new emerging contaminants. Therefore, there is a need to develop simple and lower cost-effective water purification methods that use recyclable bio-based natural polymers such as chitosan modified with nanomaterials. These novel functional chitosan-based nanomaterials have been proven to effectively eliminate the different environmental pollutants from wastewater to acceptable levels. This paper aims to present a review of the recent development of functional chitosan modified with carbon nanostructured and inorganic nanoparticles. Their application as biosorbents in fixed-bed continuous flow column adsorption for water purification is also discussed.

Rotatable central composite design versus artificial neural network for modeling biosorption of Cr6+ by the immobilized Pseudomonas alcaliphila NEWG-2

Heavy metals, including chromium, are associated with developed industrialization and technological processes, causing imbalanced ecosystems and severe health concerns. The current study is of supreme priority because there is no previous work that dealt with the modeling of the optimization of the biosorption process by the immobilized cells. The significant parameters (immobilized bacterial cells, contact time, and initial Cr6+ concentrations), affecting Cr6+ biosorption by immobilized Pseudomonas alcaliphila, was verified, using the Plackett-Burman matrix. For modeling the maximization of Cr6+ biosorption, a comparative approach was created between rotatable central composite design (RCCD) and artificial neural network (ANN) to choose the most fitted model that accurately predicts Cr6+ removal percent by immobilized cells. Experimental data of RCCD was employed to train a feed-forward multilayered perceptron ANN algorithm. The predictive competence of the ANN model was more precise than RCCD when forecasting the best appropriate wastewater treatment. After the biosorption, a new shiny large particle on the bead surface was noticed by the scanning electron microscopy, and an additional peak of Cr6+ was appeared by the energy dispersive X-ray analysis, confirming the role of the immobilized bacteria in the biosorption of Cr6+ ions.

Statistical modeling-approach for optimization of Cu2+ biosorption by Azotobacter nigricans NEWG-1; characterization and application of immobilized cells for metal removal

Heavy metals are environmental pollutants affect the integrity and distribution of living organisms in the ecosystem and also humans across the food chain. The study targeted the removal of copper (Cu²⁺) from aqueous solutions, depending on the biosorption process. The bacterial candidate was identified using 16S rRNA sequencing and phylogenetic analysis, in addition to morphological and cultural properties as Azotobacter nigricans NEWG-1. The Box-Behnken design was applied to optimize copper removal by Azotobacter nigricans NEWG-1 and to study possible interactive effects between incubation periods, pH and initial CuSO₄ concentration. The data obtained showed that the maximum copper removal percentage of 80.56% was reached at run no. 12, under the conditions of 200 mg/L CuSO₄, 4 days’ incubation period, pH, 8.5. Whereas, the lowest Cu²⁺ removal (12.12%) was obtained at run no.1. Cells of Azotobacter nigricans NEWG-1 before and after copper biosorption were analyzed using FTIR, EDS and SEM. FTIR analysis indicates that several functional groups have participated in the biosorption of metal ions including hydroxyl, methylene, carbonyl, carboxylate groups. Moreover, the immobilized bacterial cells in sodium alginate-beads removed 82.35 ± 2.81% of copper from the aqueous solution, containing an initial concentration of 200 mg/L after 6 h. Azotobacter nigricans NEWG-1 proved to be an efficient biosorbent in the elimination of copper ions from environmental effluents, with advantages of feasibility, reliability and eco-friendly.

Evolving multilayer perceptron, and factorial design for modelling and optimization of dye decomposition by bio-synthetized nano CdS-diatomite composite

Design of experiment and hybrid genetic algorithm optimized multilayer perceptron (GA-MLP) artificial neural network have been employed to model and predict dye decomposition capacity of the biologically synthesized nano CdS diatomite composite. Impact of independent variables such as, light (UV: on-off), solution pH (5-8), composite weight (CW: 0.5-1 mg), initial dye concentration (DC: 10-20 mg/l) and contact time (0-120 min), mainly in two levels, were examined to evaluate dye removal efficiency of the composite. According to the developed response surface based on the factorial design, all independent variables shown positive interactive effect on dye removal (UV > CW > pH > DC), as well as the pH-CW mutual interaction, while both UV-DC and CW-DC had antagonistic effect. The pH-CW interaction was more influential than pH and DC. Incorporation of the intermediate measurements of dye removal between the start and final contact times in GA-MLP approach, had found to improve the accuracy and predictability of the GA-MLP model. Based on the closeness of the R² (0.98), root mean square error (1.03), variance accounted for (98.23%), mean absolute error (0.61) and model predictive error (9.46%) to their desirable levels, proposed GA-MLP model outperformed the factorial design model. Finally, optimal parameter choice for maximum dye removal using factorial design and GA-MLP were found as: UV (on), pH (9), CW (1 g) and DC (10 mg/l) and UV (on), pH (8.85), CW (0.92 g), DC (12.3 mg/l) and T (117 0.6 min), respectively.

The performance and mechanism of simultaneous removal of fluoride, calcium, and nitrate by calcium precipitating strain Acinetobacter sp. H12

A calcium precipitating and denitrifying bacterium H12 was used to investigate the F^- removal performance and mechanism. The results showed that the strain H12 reduced 85.24% (0.036 mg·L^-1·h^-1) of F^-, 62.43% (0.94 mg·L^-1·h^-1) of Ca²⁺, and approximately 100% of NO₃^- over 120 h in continuous determination experiments. The response surface methodology analysis demonstrated that the maximum removal efficiency of F^- was 88.98% (0.062 mg·L^-1·h^-1) within 72 h under the following conditions: the initial Ca²⁺ concentration of 250.00 mg·L^-1, pH of 7.50, and the initial C₄H₄Na₂O₄·6H₂O concentration of 800.00 mg·L^-1. The scanning electron microscopy images, the X-ray photoelectron spectroscopy, and X-ray diffraction results suggested the following removal mechanism of F^-: (1) the bacteria, as the nucleation site, were encapsulated by bioprecipitation to form biological crystal seeds; (2) Biological crystal seeds adsorbed F^- to form Ca₅(PO₄)₃F and CaF₂; (3) Under the induction of bacteria, calcium, fluoride and phosphate coprecipitated to form Ca₅(PO₄)₃F and CaF₂. In addition, the gas chromatography data indicated that F^- had little or no effect on the gas composition during denitrification, and the fluorescence spectroscopy analysis also proved that the extracellular polymeric substance (protein) is the site of bioprecipitation nucleation.

Multi-elemental concentration in different body parts of Sepiella inermis by inductively coupled plasma mass spectrometry

The present study examined the accumulation of metal on Sepiella inermis from the Mudasalodai Landing Center, from southeast coastal region of India. Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine metals including aluminium, boron, cadmium, cobalt, chromium, copper, iron, manganese, magnesium, nickel, lead and zinc. The results showed that metal concentration in S. inermis detected in the head, arm, mantle, eye, ink, liver and nidamental gland with higher concentration of magnesium up to 992.78 mg/kg, and tentacle showed maximum concentration of aluminium 306.72 mg/kg. Further, copper found in low concentration ranges from 0.04 to 0.55 mg/kg in different parts of S. inermis. Heavy metal like cadmium detected high in tentacle with 0.24 mg/kg, and the manganese present in eye was 0.55 mg/kg. However, no accumulation of nickel was found in the tentacle part.

Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology

Biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS) and total suspended solids (TSS) are the most commonly regulated wastewater effluent parameters. The measurement and prediction of these parameters are essential for assessing the performance and upgrade of wastewater treatment facilities. In this study, a new methodology, combining a linear stochastic model (ARIMA) and nonlinear outlier robust extreme learning machine technique (ORELM) with various preprocesses, is presented to model the quality parameters of effluent wastewater (ARIMA-ORELM). For each of the studied parameters, 144 different (144 × 8 models) linear models (ARIMA) are presented, with the superior model of each parameter being selected based on statistical indices. Moreover, 48 nonlinear models (ORELM) and 48 hybrid models (ARIMA-ORELM) were considered. The use of linear and nonlinear approaches to model the linear and nonlinear terms (respectively) of each time series in the hybrid model increased the efficiency and accuracy of the predictions for all of the time series. The influent wastewater nonlinear TSS model and the effluent COD and BOD models attained the best performance with a high correlation coefficient of 0.95. The use of hybrid models improved the prediction capability of all quality parameters with the best performance being achieved for the effluent BOD model (R² = 0.99).