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

Application of Noug (Guizotia abyssinica cass.) stalk activated carbon for the removal of lead (II) ions from aqueous solutions

Due to the rise of industries worldwide, huge amounts of pollutants including heavy metals are released into the surroundings. Disposal of effluents containing heavy metals in higher concentrations without proper treatment is common in industries; lead is one of them. This study aims to determine and optimize the efficiency of Noug (Guizotia abyssinica Cass.) stalk porous carbon (NSAC) for the elimination of lead (II) from aqueous solutions. For studying the adsorption characteristics of Noug stalk activated carbon (NSAC) an adsorbate of lead (II) ions was used. The interaction and effect of the following parameters on Pb(II) adsorption were investigated using Design Expert version 7.0 software (central composite design) to determine the optimum adsorption condition: pH, initial concentration of Pb(II) ion, adsorbent dose, and contact time. The optimized condition for the elimination of lead (II) using Noug stalk porous carbon (98.77 %) was achieved at pH [4.87], initial concentration of Pb(II) [84.66 mg/L], adsorbent dose [18.43 g/L], and contact time [2.04 h]. The pseudo-second-order kinetics and the Langmuir isotherm model which had a maximum adsorption capacity of 89.25 mg/g, provided the best-fit models for Pb(II) adsorption, with R2 values of 0.99 and 0.98, respectively. Efficient elimination of Pb(II) from wastewater can be performed through the use of NSAC. Future research should delve more into column adsorption under continuous wastewater flow.

Ecological disturbances and abundance of anthropogenic pollutants in the aquatic ecosystem: Critical review of impact assessment on the aquatic animals

Anthropogenic toxins are discharged into the environment and distributed through a variety of environmental matrices. Trace contaminant detection and analysis has advanced dramatically in recent decades, necessitating further specialized technique development. These pollutants can be mobile and persistent in small amounts in the environment, and ecological receptors will interact with it. Despite the fact that few researches have been undertaken on invertebrate exposure, accumulation, and biological implications, it is apparent that a wide range of pollutants can accumulate in the tissues of aquatic insects, earthworms, amphipod crustaceans, and mollusks. Due to long-term stability during long-distance transit, a number of chemical and microbiological agents that were not previously deemed pollutants have been found in various environmental compartments. The uptake of such pollutants by the aquatic organism is done through the process of bioaccumulation when dangerous compounds accumulate in living beings while biomagnification is the process of a pollutant becoming more hazardous as it moves up the trophic chain. Organic and metal pollution harms animals of every species studied so far, from bacteria to phyla in between. The environmental protection agency says these poisons harm humans as well as a variety of aquatic organisms when the water quality is sacrificed in typical wastewater treatment systems. Contrary to popular belief, treated effluents discharged into aquatic bodies contain considerable levels of Anthropogenic contaminants. This evolution necessitates a more robust and recent advancement in the field of remediation and their techniques to completely discharge the various organic and inorganic contaminants.

Innovative Materials and Processes for Removal of Biopersistent Pollutants

The aim of this Special Issue “Innovative Materials and Processes for Removal of Biopersistent Pollutants” (https://www [...]

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Statistical Analysis and Optimization of the Brilliant Red HE-3B Dye Biosorption onto a Biosorbent Based on Residual Biomass

Using various techniques, natural polymers can be successfully used as a matrix to immobilize a residual microbial biomass in a form that is easy to handle, namely biosorbents, and which is capable of retaining chemical species from polluted aqueous media. The biosorption process of reactive Brilliant Red HE-3B dye on a new type of biosorbent, based on a residual microbial biomass of Saccharomyces pastorianus immobilized in sodium alginate, was studied using mathematical modeling of experimental data obtained under certain conditions. Different methods, such as computer-assisted statistical analysis, were applied, considering all independent and dependent variables involved in the reactive dye biosorption process. The optimal values achieved were compared, and the experimental data supported the possibility of using the immobilized residual biomass as a biosorbent for the studied reference dye. The results were sufficient to perform dye removals higher than 70-85% in an aqueous solution containing around 45-50 mg/L of reactive dye, and working with more than 20-22 g/L of prepared immobilized microbial biosorbent for more than 9.5-10 h. Furthermore, the proposed models agreed with the experimental data and permitted the prediction of the dye biosorption behavior in the experimental variation field of each independent variable.

Artificial Intelligence-Based Tools for Process Optimization: Case Study—Bromocresol Green Decolorization with Active Carbon

This study highlights the benefits of optimizing the decolorization of bromocresol green (a colorant/pH indicator widely used in the industry, whose degradation produces toxic byproducts) by adsorption on active carbon. A set of experiments were planned and performed based on the design of experiments methodology for the following parameters: the colorant concentration (0.009-0.045 g/L), the amount of adsorbent (0.5-3 g/L), and the contact time (60-240 min). Modeling and optimization strategies were employed to determine the working conditions leading to efficiency maximization. Using the response surface methodology, the optimum values of the primary process parameters were established. In addition, a modified bacterial foraging optimization algorithm was applied as an alternative optimizer in combination with artificial neural networks in order to determine multiple combinations of parameters that can lead to maximum process efficiency. Different solutions were obtained with the considered strategies, and the maximum efficiency obtained was >99%. The study emphasizes that adsorption on active carbon is an effective method for bromocresol green decolorization in wastewater that can be further improved using advanced optimization methods.