Equilibrium study of the binary mixture of cadmium–zinc ions biosorption by the Sargassum filipendula species using adsorption isotherms models and neural network

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.

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.

The current status, challenges and prospects of using biomass energy in Ethiopia

Despite enormous challenges in accessing sustainable energy supplies and advanced energy technologies, Ethiopia has one of the world's fastest growing economies. The development of renewable energy technology and the building of a green legacy in the country are being prioritized. The total installed capacity for electricity generation in Ethiopia is 4324.3 MW as on October, 2018. Renewable energy accounts for 96.5% of total generation; however, despite the county's enormous biomass energy potential, only 0.58% of power is generated using biomass. Ethiopia has surplus woody biomass, crop residue and animal dung resources which comprise about 141.8 million metric tons of biomass availability per year. At present the exploited potential is about 71.9 million metric tons per year. This review paper provides an in-depth assessment of Ethiopia's biomass energy availability, potential, challenges, and prospects. The findings show that, despite Ethiopia's vast biomass resource potential, the current use of modern energy from biomass is still limited. As a result, this study supports the use of biomass-based alternative energy sources without having a negative impact on the socioeconomic system or jeopardizing food security or the environment. This finding also shows the challenges, opportunities and possible solutions to tackle the problem to expand alternative energy sources. The most effective techniques for producing and utilizing alternate energy sources were also explored. Moreover, some perspectives are given based on the challenges of using efficient energy production and sustainable uses of biomass energy in Ethiopia as it could be also implemented in other developing countries. We believe that the information in this review will shed light on the current and future prospects of biomass energy deployment in Ethiopia.

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.

Human Immunoglobulin G Adsorption in Epoxy Chitosan/Alginate Adsorbents: Evaluation of Isotherms by Artificial Neural Networks

This work aimed to evaluate the interaction of human IgG in non-conventional adsorbents based on chitosan and alginate in the absence and presence of Reactive Green, Reactive Blue and Cibacron Blue immobilized as ligands. The adsorption was evaluated at 277, 288, 298 and 310 K using sodium phosphate buffer, pH 7.6, at 25 mmol L−1. The highest adsorption capacity was observed in the experiments performed with no immobilized dye, although all showed adsorption capacity higher than 120 mg g−1. Data modeling was done using Langmuir, Langmuir-Freundlich and Temkin classical nonlinear models, and artificial neural networks (ANN) for comparison. According to the parameters obtained, a possible adsorption in multilayers was observed due to protein-adsorbent and protein-protein interactions, concluding that IgG adsorption process is favorable and spontaneous. Using an ANN structure with 3 hidden neurons (single hidden layer), the MSE (RMSE) for training, test and validation were 13.698 (3.701), 11.206 (3.347) and 7.632 (2.763), respectively, achieving correlation coefficients of 0.999 in all steps. ANN modeling proved to be effective in predicting the adsorption isotherms in addition to overcoming the difficulties caused by experimental errors and/or arising from adsorption phenomenology.

Assessment of the Cu(II) and Pb(II) removal efficiency of aqueous solutions by the dry biomass Aguapé: kinetics of adsorption

Plant-based materials are promising adsorbents for treating liquid effluents. This study describes the kinetic and equilibrium parameters that best represent the copper(II) and lead(II) removal process by Eichhornia crassipes (Aguapé) dry biomass from aqueous solution, using a batch adsorption system. The plants were washed, dried, and reduced to small particles. The adsorption kinetics were assessed by varying the metal concentrations in 5, 10, and 20 mg L^-1 and a control treatment (without metals) with a mixture contact time of between 5 and 720 min. Equilibrium data were fitted to the Langmuir and Freundlich models. Kinetic assay revealed fast adsorption: kinetic equilibrium was attained within 2 h with a removal efficiency of ~ 60%. The results demonstrated a fast recovery cycle of metals using the biosorbent. The biomass of E. crassipes is low cost with potential for use as a biosorbent to remove metals from solutions.

New Prospects for Modified Algae in Heavy Metal Adsorption

Heavy metal pollution is one of the most pervasive environmental problems globally. Novel finely tuned algae have been proposed as a means to improve the efficacy and selectivity of heavy metal biosorption. This article reviews current research on selective algal heavy metal adsorption and critically discusses the performance of novel biosorbents. We emphasize emerging state-of-the-art techniques that customize algae for enhanced performance and selectivity, particularly molecular and chemical extraction techniques as well as nanoparticle (NP) synthesis approaches. The mechanisms and processes for developing novel algal biosorbents are also presented. Finally, we discuss the applications, challenges, and future prospects for modified algae in heavy metal biosorption.

Equilibrium study of binary mixture biosorption of Cr(III) and Zn(II) by dealginated seaweed waste: investigation of adsorption mechanisms using X-ray photoelectron spectroscopy analysis

The alginate extraction residue (RES) from the Brazilian Sargassum filipendula was successfully employed as biosorbent in this binary equilibrium study, revealing a greater affinity and selectivity for Cr(III) than for Zn(II). Experimental results also revealed that the process is of endothermic nature and well adjusted by Langmuir-Freundlich binary model. The X-ray photoelectron spectroscopy (XPS) analysis revealed that coordination with hydroxyl groups of RES prevailed in Cr removal, followed by carboxyl-metal complexation. As far as Zn(II) is concerned, ion exchange with carboxylate groups of RES was the largest contributor. Nevertheless, scanning electron microscopy coupled with Fourier transform infrared spectroscopy indicated the participation of sulfate functions in a minor degree.

Use of non-living lyophilized Phanerochaete chrysosporium cultivated in various media for phenol removal

The biosorption of phenol on non-living lyophilized mycelial pellets of Phanerochaete chrysosporium cultivated in liquid medium of various compositions was studied in batch biosorption system. The fungal cell surfaces were characterized by FTIR spectroscopy and specific surface charge determination. The sorption kinetics and equilibrium were evaluated using linear and non-linear regression. For adsorption equilibrium, a comparative evaluation is also presented using non-linear least-square estimation and linearization of the Langmuir and anti-Langmuir equations. The presence of mineral and vitamin materials in the liquid medium enhanced the adsorption capacity of fungal biomass for phenol. At optimum pH 5-6, the values of specific surface charge were 0.023 and 0.069 meq g^-1 for various cultivations, and the maximum amounts of phenol can be adsorbed at these pH values. The maximum adsorbed phenol amounts by cells cultivated in simple and complex media were 4.53 and 13.48 mg g^-1, respectively, at an initial phenol concentration of 100 mg l^-1. Graphical abstract ᅟ.

Interpretation of single and competitive adsorption of cadmium and zinc on activated carbon using monolayer and exclusive extended monolayer models

In this work, a modeling analysis based on experimental tests of cadmium/zinc adsorption, in both single-compound and binary systems, was carried out. All the experimental tests were conducted at constant pH (around neutrality) and temperature (20 °C). The experimental results showed that the zinc adsorption capacity was higher than that of cadmium and it does not depend on cadmium presence in binary system. Conversely, cadmium adsorption is affected by zinc presence. In order to provide good understanding of the adsorption process, two statistical physics models were proposed. A monolayer and exclusive extended monolayer models were applied to interpret the single-compound and binary adsorption isotherms of zinc and cadmium on activated carbon. Based on these models, the modeling analysis demonstrated that zinc is dominant in solution and more favorably adsorbed on activated carbon surface. For instance, in single-compound systems, the number of ions bound per each receptor site was n (Zn²⁺) = 2.12 > n (Cd²⁺) = 0.98. Thus, the receptor sites of activated carbon are more selective for Zn²⁺ than for Cd²⁺. Moreover, the determination of adsorption energy through the adopted models confirmed that zinc is more favored for adsorption in single-compound system (adsorption energies equal to 12.12 and 7.12 kJ/mol for Zn and Cd, respectively) and its adsorption energy does not depend on the cadmium presence in binary system. Finally, the adsorption energy values suggested that single-compound and binary adsorption of zinc and cadmium is a physisorption.

Single and multi-component adsorption of psychiatric pharmaceuticals onto alternative and commercial carbons

This work describes the adsorptive removal of three widely consumed psychiatric pharmaceuticals (carbamazepine, paroxetine and oxazepam) from ultrapure water. Two different adsorbents were used: a commercial activated carbon and a non-activated waste-based carbon (PS800-150-HCl), produced by pyrolysis of primary paper mill sludge. These adsorbents were used in single, binary and ternary batch experiments in order to determine the adsorption kinetics and equilibrium isotherms of the considered pharmaceuticals. For the three drugs and both carbons, the equilibrium was quickly attained (with maximum equilibrium times of 15 and 120 min for the waste-based and the commercial carbons, respectively) even in binary and ternary systems. Single component equilibrium data were adequately described by the Langmuir model, with the commercial carbon registering higher maximum adsorption capacities (between 272 ± 10 and 493 ± 12 μmol g^-1) than PS800-150-HCl (between 64 ± 2 and 74 ± 1 μmol g^-1). Multi-component equilibrium data were also best fitted by the single component Langmuir isotherm, followed by the Langmuir competitive model. Overall, competitive effects did not largely affect the performance of both adsorbents. Binary and ternary systems maintained fast kinetics, the individual maximum adsorption capacities were not lower than half of the single component systems and both carbons presented improved total adsorption capacities for multi-component solutions.

Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks

A two-stage biological waste gas treatment system consisting of a first stage biotrickling filter (BTF) and second stage biofilter (BF) was tested for the removal of a gas-phase methanol (M), hydrogen sulphide (HS) and α-pinene (P) mixture. The bioreactors were tested with two types of shock loads, i.e., long-term (66h) low to medium concentration loads, and short-term (12h) low to high concentration loads. M and HS were removed in the BTF, reaching maximum elimination capacities (EC_max) of 684 and 33 gm^-3h^-1, respectively. P was removed better in the second stage BF with an EC_max of 130 gm^-3h^-1. The performance was modelled using two multi-layer perceptrons (MLPs) that employed the error backpropagation with momentum algorithm, in order to predict the removal efficiencies (RE, %) of methanol (RE_M), hydrogen sulphide (RE_HS) and α-pinene (RE_P), respectively. It was observed that, a MLP with the topology 3-4-2 was able to predict RE_M and RE_HS in the BTF, while a topology of 3-3-1 was able to approximate RE_P in the BF. The results show that artificial neural network (ANN) based models can effectively be used to model the transient-state performance of bioprocesses treating gas-phase pollutants.

The use of artificial neural network for modelling of phycoremediation of toxic elements As(III) and As(V) from wastewater using Botryococcus braunii

In the present study, a thorough investigation has been done on the removal efficiency of both As(III) and As (V) from synthetic wastewater by phycoremediation of Botryococcus braunii algal biomass. Artificial neural networks (ANNs) are practised for predicting % phycoremediation efficiency of both As(III) and As(V) ions. The influence of several parameters for example initial pH, inoculum size, contact time and initial arsenic concentration (either As(III) or As(V)) was examined systematically. The maximum phycoremediation of As(III) and As(V) was found to be 85.22% and 88.15% at pH9.0, equilibrium time of 144h by using algal inoculum size of 10% (v/v) and initial arsenic concentration of 50mg/L. The data acquired from laboratory scale experimental set up was utilized for training a three-layer feed-forward back propagation (BP) with Levenberg-Marquardt (LM) training algorithm having 4:5:1 architecture. A comparison between the experimental data and model outputs provided a high correlation coefficient (R(2)all_ANN equal to 0.9998) and exhibited that the model was capable for predicting the phycoremediation of both As(III) and As(V) from wastewater. The network topology was optimized by changing number of neurons in hidden layers. ANNs are efficient to model and simulate highly non-liner multivariable relationships. Absolute error and Standard deviation (SD) with respect to experimental output were calculated for ANN model outputs. The comparison of phycoremediation efficiencies of both As(III) and As(V) between experimental results and ANN model outputs exhibited that ANN model can determine the behaviour of As(III) and As(V) elimination process under various circumstances.

A comprehensive review on biosorption of heavy metals by algal biomass: materials, performances, chemistry, and modeling simulation tools

Heavy metals contamination has become a global issue of concern due to their higher toxicities, nature of non-biodegradability, high capabilities in bioaccumulation in human body and food chain, and carcinogenicities to humans. A series of researches demonstrate that biosorption is a promising technology for removal of heavy metals from aqueous solutions. Algae serve as good biosorbents due to their abundance in seawater and fresh water, cost-effectiveness, reusability and high metal sorption capacities. This article provides a comprehensive review of recent findings on performances, applications and chemistry of algae (e.g., brown, green and red algae, modified algae and the derivatives) for sequestration of heavy metals. Biosorption kinetics and equilibrium models are reviewed. The mechanisms for biosorption are presented. Biosorption is a complicated process involving ion-exchange, complexation and coordination. Finally the theoretical simulation tools for biosorption equilibrium and kinetics are presented so that the readers can use them for further studies.

Zinc and cadmium removal by biosorption on Undaria pinnatifida in batch and continuous processes

Zn(II) and Cd(II) removal by biosorption using Undaria pinnatifida was studied in batch and dynamic systems. The kinetic uptake follows a pseudo second order rate equation indicating that the rate limiting step is a chemical reaction. The equilibrium data are described by the Langmuir isotherm in mono-component solutions. In binary solutions, the Jain and Snowyink model shows that most of the active sites are exclusively accessible to cadmium ions without competition with the zinc ions. The dynamic studies show that the biosorbent has higher retention and affinity for Cd(II) than for Zn(II) in both mono- and bi-component systems. SEM-EDX analysis indicates that the active sites are heterogeneously distributed on the cell wall surface. FT-IR spectrometry characterization shows that carboxylic groups and chemical groups containing N and S contribute to Zn(II) and Cd(II) uptake by U. pinnatifida. According to these results calcium-treated U. pinnatifida is a suitable adsorbent for Zn(II) and Cd(II) pollutants.

Remoção dos metais pesados Cd(II), Cu(II) e Zn(II) pelo processo de biossorção utilizando a macrófita Eicchornia crassipes

A capacidade de remoção dos íons Cd(II), Cu(II) e Zn(II), de uma solução aquosa, pela macrófita Eichhornia crassipes foi investigada, em sistema batelada. Foram obtidos dados experimentais da cinética e do equilíbrio de biossorção, para os sistemas monocomponentes. Os testes cinéticos mostraram que o tempo de equilíbrio foi de 45, 60 e 120 min., para Cd(II), Cu(II) e Zn(II) ions, respectivamente. O modelo cinético de pseudo-segunda-ordem foi o que melhor representou os dados experimentais. Para descrever o equilíbrio de biossorção, foram utilizadas as isoterma de Langmuir, Freundlich e Temkim. A isoterma de Langmuir foi a que melhor descreveu os dados experimentais de equilíbrio, com valores da capacidade de adsorção de 0,667, 0,615 e 0,633 mequiv.g-1 para Cd(II), Cu(II) e Zn(II), respectivamente. Contudo acredita-se que a macrófita E. crassipes tem grande potencial para ser utilizada, no tratamento de águas residuárias contaminadas com metais pesados.

Zinc and cadmium biosorption by untreated and calcium-treated Macrocystis pyrifera in a batch system

Zinc and cadmium can be efficiently removed from solutions using the brown algae, Macrocystis pyrifera. Treatment with CaCl(2) allowed stabilization of the biosorbent. The maximum biosorption capacities in mono-component systems were 0.91 mmol g(-1) and 0.89 mmol g(-1) and the Langmuir affinity coefficients were 1.76 L mmol(-1) and 1.25 L mmol(-1) for Zn(II) and Cd(II), respectively. In two-component systems, Zn(II) and Cd(II) adsorption capacities were reduced by 50% and 40%, respectively and the biosorbent showed a preference for Cd(II) over Zn(II). HNO(3) (0.1M) and EDTA (0.1M) achieved 90-100% desorption of both ions from the loaded biomass. While HNO(3) preserved the biomass structure, EDTA destroyed it completely. Fourier transform infrared spectra identified the contribution of carboxylic, amine and sulfonate groups on Zn(II) and Cd(II) biosorption. These results showed that biosorption using M. pyrifera-treated biomass could be an affordable and simple process for cadmium and zinc removal from wastewaters.

Equilibrium of Cu(II) and Ni(II) biosorption by marine alga Sargassum filipendula in a dynamic system: competitiveness and selectivity

The study focuses on the equilibrium of dynamic biosorption in single and binary systems containing Cu(II) and Ni(II) ions using Sargassum filipendula (a marine alga). The experiments were performed in fixed-bed columns with both single-component and bi-component metal solutions (using different molar concentrations). Experimental data were fitted with different equilibrium models such as Langmuir, Langmuir with inhibition, Jain and Snowyink and Langmuir-Freundlich equations. The biosorption of pure metal ions in solution presented adequate capacities both for Cu(II) and Ni(II). In binary solutions the preferential sorption of Cu(II) over Ni(II) was demonstrated by the displacement of Ni(II) (marked overshoot on the breakthrough curves).

Mono-component versus binary isotherm models for Cu(II) and Pb(II) sorption from binary metal solution by the green alga Pithophora oedogonia

The sorption of Cu(II) and Pb(II) by Pithophora markedly decreased as the concentration of the secondary metal ion, Cu(II) or Pb(II), increased in the binary metal solution. However, the test alga showed a greater affinity to sorb Cu(II) than Pb(II) from the binary metal solution. Mono-component Freundlich, Langmuir, Redlich-Peterson and Sips isotherms successfully predicted the sorption of Cu(II) and Pb(II) from both single and binary metal solutions. None of the tested binary sorption isotherms could realistically predict Cu(II) and Pb(II) sorption capacity and affinity of the test alga for the binary metal solutions of varying composition, which mono-component isotherms could very well accomplish. Hence, mono-component isotherm modeling at different concentrations of the secondary metal ion seems to be a better option than binary isotherms for metal sorption from binary metal solution.