Biosorption study of Ni2+ and Cr3+ by Sargassum filipendula: kinetics and equilibrium

Multi-pollutant biosorption of organic and inorganic pollutants by brown algae waste from alginate production: batch and fixed-bed investigation

The reuse of biomass waste has been gaining attention in adsorption processes to remove pollutants of emerging concern from water and wastewater. In this work, the potential of alginate-extracted macro-algae waste to uptake synthetic dyes and metal cations was evaluated in comparison with raw algae. In affinity assays, both materials were able to remove metal cations and cationic dyes up to maximum rates, and no significant removal was observed for an anionic dye in an acidic medium. Competition was observed in multi-component systems of metal cations and dyes. For binary samples containing organic and inorganic contaminants, kinetic modeling evidenced the distinct nature of both types of adsorbates. Pb(II) biosorption was best described as a first-order process, while second-order and Elovich models better fitted methyl blue (MB) uptake data. For equimolar binary samples, the Sips isothermal model fitted the experimental data more satisfactorily at room temperature. Isotherms for 20, 30, 40, and 60 °C exhibited favorable adsorption profiles with spontaneous ΔG values for both raw macro-algae and waste from alginate extraction. Maximum adsorption capacities were competitive with previous reports in the literature for a wide range of biomaterials, pointing to the slightly higher efficiency with algae waste in batch experiments. In elution tests, HNO3 (0.5 M) showed the best recovery rates of metal cations. Continuous biosorption operation revealed the performance of the brown algae waste was considerably more efficient than raw algae with breakthrough biosorption capacities up to 3.96 and 0.97 mmol.g-1 for the removal of Pb(II) and MB, respectively. A total of 3.0 g of algae and algae waste were able to deliver 1.20 and 1.62 L of contaminant-free water, respectively. XPS analyses corroborate previous assays that pointed to the prevalence of physisorption with evidence of complexation, ionic exchange, and hydrogen displacement mechanisms.

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.

Evaluation of a Dynamic Bioremediation System for the Removal of Metal Ions and Toxic Dyes Using Sargassum Spp

This work presents the results obtained in the design and manufacture of a simple, economic and ecological filter based on Sargassum spp. (Sspp), consisting of the species S. natans and S. fluitans, for the elimination of organic and inorganic toxic substances. The main objective is to make use of Sspp, as the massive amounts of this alga arriving at the Mexican Caribbean coast have caused serious problems over recent years. The toxic substances treated were organic dyes (methyl blue, methyl orange and methyl red) and the metal ion, lead (II). To obtain optimal removal conditions, grinding of the Sspp used, its mass and humidity were evaluated. In the design of the filter the area, flow rate and the number of layers were evaluated. Removal rates of almost 100%, 65% and 25% were obtained for methylene blue, methyl red and methyl orange respectively, and in the case of lead (II), values up to 95% were obtained. After the tests, the Sspp was characterized, using Fourier Transform Infrared (FTIR) spectroscopy and scanning electron microscopy, showing the presence of the dyes and the ionic species. These results demonstrate the efficiency of the dynamic Sspp-based filtration system proposed, which can be industrially scaled for the treatment of water contaminated with these kinds of substances.

Massive Influx of Pelagic Sargassum spp. on the Coasts of the Mexican Caribbean 2014–2020: Challenges and Opportunities

Since late 2014, the Mexican Caribbean coast has periodically received massive, atypical influxes of pelagic Sargassum spp. (sargasso). Negative impacts associated with these influxes include mortality of nearshore benthic flora and fauna, beach erosion, pollution, decreasing tourism and high management costs. To understand the dynamics of the sargasso influx, we used Landsat 8 imagery (from 2016 to mid-2020) to record the coverage of sargasso in the sea off the Mexican Caribbean coastline, with a maximum reported in September 2018. Satellite image analysis also showed local differences in the quantity of beached sargasso along the coastline. Over the years, good practice for collection on the beach and for off-shore collection of sargasso have been established through trial and error, and the Mexican Government and hotel industry have spent millions of dollars on removal and off-shore detention of sargasso. Notwithstanding, sargasso also has various properties that could be harnessed in local industries. The stimulation of local industrial growth would offer alternatives to the dependence on tourism, as a circular economy, based on sargasso, is developed.

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.

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.