Introducing machine learning model to response surface methodology for biosorption of methylene blue dye using Triticum aestivum biomass

Zeolites in wastewater treatment: A comprehensive review on scientometric analysis, adsorption mechanisms, and future prospects

Zeolites possess a microporous crystalline structure, a large surface area, and a uniform pore size. Natural or synthetic zeolites are commonly utilized for adsorbing organic and inorganic compounds from wastewater because of their unique physicochemical properties and cost-effectiveness. The present review work comprehensively revealed the application of zeolites in removing a diverse range of wastewater contaminates, such as dyes, heavy metal ions, and phenolic compounds, within the framework of contemporary research. The present review work offers a summary of the existing literature about the chemical composition of zeolites and their synthesis by different methods. Subsequently, the article provides a wide range of factors to examine the adsorption mechanisms of both inorganic and organic pollutants using natural zeolites and modified zeolites. This review explores the different mechanisms through which zeolites effectively eliminate pollutants from aquatic matrices. Additionally, this review explores that the Langmuir and pseudo-second-order models are the predominant models used in investigating isothermal and kinetic adsorption and also evaluates the research gap on zeolite through scientometric analysis. The prospective efficacy of zeolite materials in future wastewater treatment may be assessed by a comparative analysis of their capacity to adsorb toxic inorganic and organic contaminates from wastewater, with other adsorbents.

Zero-waste biorefining co-products from ultrasonically assisted deep eutectic solvent-pretreated Chlorella biomass: Sustainable production of biodiesel and bio-fertilizer

Microalgal biomass is gaining increasing attention to produce high-value co-products. This study proposes integrating Chlorella microalgal biomass into a zero-waste biorefining system, aiming to produce biodiesel and biofertilizer. It investigates optimal conditions for ultrasound-assisted deep eutectic solvent (DES) pretreatment and lipid recovery to enhance the extraction of lipids. Optimal DES pretreatment was identified as a 1.6:1 acetic acid-to-choline chloride molar ratio, 0.36 g biomass loading, and 2.50 min of pretreatment. Lipid recovery succeeded with a 10-minute extraction time and a 1:3 methanol-to-butanol volume ratio. These conditions yielded biodiesel-quality lipids at 139.52 mg/g microalgal biomass with superior fuel characteristics. The de-oiled microalgal biomass residue exhibited promise as a lettuce biofertilizer, enhancing photosynthetic pigments but potentially reducing yields by 40 %. The study also notes changes in rhizosphere microbial communities, indicating both stimulatory and inhibitory effects on beneficial microbes. This study has the potential to enhance sustainability in energy, agriculture, and the environment.

Performance Evaluation of Benzyl Alcohol Oxidation with tert-Butyl Hydroperoxide to Benzaldehyde Using the Response Surface Methodology, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference System Model

The adaptive neuro-fuzzy inference system (ANFIS), central composite experimental design (CCD)-response surface methodology (RSM), and artificial neural network (ANN) are used to model the oxidation of benzyl alcohol using the tert-butyl hydroperoxide (TBHP) oxidant to selectively yield benzaldehyde over a mesoporous ceria-zirconia catalyst. Characterization reveals that the produced catalyst has hysteresis loops, a sponge-like structure, and structurally induced reactivity. Three independent variables were taken into consideration while analyzing the ANN, RSM, and ANFIS models: the amount of catalyst (A), reaction temperature (B), and reaction time (C). With the application of optimum conditions, along with a constant (45 mmol) TBHP oxidant amount, (30 mmol) benzyl alcohol amount, and rigorous refluxing of 450 rpm, a maximum optimal benzaldehyde yield of 98.4% was obtained. To examine the acceptability of the models, further sensitivity studies including statistical error functions, analysis of variance (ANOVA) results, and the lack-of-fit test, among others, were employed. The obtained results show that the ANFIS model is the most suited to predicting benzaldehyde yield, followed by RSM. Green chemistry matrix calculations for the reaction reveal lower values of the E-factor (1.57), mass intensity (MI, 2.57), and mass productivity (MP, 38%), which are highly desirable for green and sustainable reactions. Therefore, utilizing a ceria-zirconia catalyst synthesized via the inverse micelle method for the oxidation of benzyl alcohol provides a green and sustainable methodology for the synthesis of benzaldehyde under mild conditions.

Comparative analysis of the equilibrium, kinetics, and characterization of the mechanism of rapid adsorption of Congo red on nano-biosorbents based on agricultural waste in industrial effluents

This study aims to remove Congo red dye from industrial effluent using economical agriculturally-based nano-biosorbents like magnetic orange peel, peanut shells, and tea waste. The nano-biosorbents were characterized by various analytical techniques like SEM, FT-IR, BET and XRD. The highest adsorption capacity was obtained under the following ideal conditions: pH = 6 (orange peel and peanut shells), pH = 3 (tea waste), and dosages of nano-biosorbents with varying timeframes of 50 min for tea waste and peanut shells and 30 min for orange peel. The study found that tea waste had the highest removal rate of 94% due to its high porosity and responsible functional groups, followed by peanut shells at 83% and orange peel at 68%. The Langmuir isotherm model was found to be the most suitable, with R2 values of 0.99 for tea waste, 0.92 for orange peel, and 0.71 for peanut shells. On the other hand, a pseudo-second-order kinetic model was very feasible, showing an R2 value of 0.99 for tea waste, 0.98 for peanut shells and 0.97 for orange peel. The significance of the current study lies in its practical application, enabling efficient waste management and water purification, thereby preserving a clean and safe environment.

Removing Heavy Metals: Cutting-Edge Strategies and Advancements in Biosorption Technology

This article explores recent advancements and innovative strategies in biosorption technology, with a particular focus on the removal of heavy metals, such as Cu(II), Pb(II), Cr(III), Cr(VI), Zn(II), and Ni(II), and a metalloid, As(V), from various sources. Detailed information on biosorbents, including their composition, structure, and performance metrics in heavy metal sorption, is presented. Specific attention is given to the numerical values of the adsorption capacities for each metal, showcasing the efficacy of biosorbents in removing Cu (up to 96.4%), Pb (up to 95%), Cr (up to 99.9%), Zn (up to 99%), Ni (up to 93.8%), and As (up to 92.9%) from wastewater and industrial effluents. In addition, the issue of biosorbent deactivation and failure over time is highlighted as it is crucial for the successful implementation of adsorption in practical applications. Such phenomena as blockage by other cations or chemical decomposition are reported, and chemical, thermal, and microwave treatments are indicated as effective regeneration techniques. Ongoing research should focus on the development of more resilient biosorbent materials, optimizing regeneration techniques, and exploring innovative approaches to improve the long-term performance and sustainability of biosorption technologies. The analysis showed that biosorption emerges as a promising strategy for alleviating pollutants in wastewater and industrial effluents, offering a sustainable and environmentally friendly approach to addressing water pollution challenges.

Machine learning approaches for the treatment of textile wastewater using sugarcane bagasse (Saccharum officinarum) biochar

Most dyes present in wastewater from the textile industry exhibit toxicity and are resistant to biodegradation. Hence, the imperative arises for the environmentally significant elimination of textile dye by utilising agricultural waste. The achievement of this objective can be facilitated through the utilisation of the adsorption mechanism, which entails the passive absorption of pollutants using biochar. In this study, we compare the efficacy of the response surface methodology (RSM), the artificial neural network (ANN), the k-nearest neighbour (kNN), and adaptive neuro-fuzzy inference system (ANFIS) in removing crystal violet (CV) from wastewater. The characterisation of biochar is carried out by scanning electron microscope (SEM) and Fourier transform infrared (FTIR). The impacts of the solution pH, adsorbent dosage, initial dye concentration, and temperature were investigated using a variety of models (RSM, ANN, kNN, and ANFIS). The statistical analysis of errors was conducted, resulting in a maximum removal effectiveness of 97.46% under optimised settings. These conditions included an adsorbent dose of 0.4 mg, a pH of 5, a CV concentration of 40.1 mg/L, and a temperature of 20 °C. The ANN, RSM, kNN, and ANFIS models all achieved R2 0.9685, 0.9618, 0.9421, and 0.8823, respectively. Even though all models showed accuracy in predicting the removal of CV dye, it was observed that the ANN model exhibited greater accuracy compared to the other models.

Integrating artificial neural networks and response surface methodology for predictive modeling and mechanistic insights into the detoxification of hazardous MB and CV dyes using Saccharum officinarum L. biomass

The presence of dye pollutants in industrial wastewater poses significant environmental and health risks, necessitating effective treatment methods. The optimal adsorption treatment of methylene blue (MB) and crystal violet (CV) dye-simulated wastewater utilising Saccharum officinarum L presents a key challenge in the selection of appropriate modelling approaches. While RSM and ANN models are frequently used, there is a noticeable knowledge gap when it comes to evaluating their relative strengths and weaknesses in this context. The study compared the predictive abilities of response surface methodology (RSM) and artificial neural network (ANN) for the adsorption treatment of MB and CV dye-simulated wastewater using Saccharum officinarum L. The process experimental variables were modelled and predicted using a three-layer artificial neural network trained using the Levenberg-Marquard backpropagation algorithm and 30 central composite designs (CCD). The adsorption study used a specific mechanism, which led to noteworthy maximum removals of 98.3% and 98.2% for dyes (MB and CV), respectively. The RSM model achieved an impressive R2 of 0.9417, while the ANN model achieved 0.9236 in MB. Adsorption is commonly used to remove colour from many different materials. Saccharum officinarum L., a byproduct of sugarcane processing, has shown potential as an efficient and ecological adsorbent in this environment. The purpose of this study is to evaluate sugarcane bagasse's potential as an adsorbent for the removal of dyes MB and CV from industrial wastewater, providing a long-term strategy for reducing dye pollution. Due to its beneficial economic and environmental characteristics, the Saccharum officinarum L. adsorbent has prompted research into sustainable resources with low pollutant indices.

Removal of carboxylated multi-walled carbon nanotubes (MWCNT-COOH) from the environment by Trametes versicolor: a simple, cost-effective, and eco-friendly method

Nanotechnology has increased the release of nanoparticles into the environment, which poses a risk to human health and the ecosystem. Therefore, finding ways to eliminate these hazardous particles from the environment is crucial. This research studied the ability of Trametes versicolor fungi to remove carboxylated multi-walled carbon nanotubes. The study analyzed the impact of pH, MWCNT-COOH concentration, and initial fungal growth time on the removal process. The properties of the adsorbent were measured before and after the biosorption process using SEM, FTIR, and EDS techniques. The results showed that the live biomass of T. versicolor was more effective in removing nanoparticles than dead biomass at 30 °C and pH 7. An increase in carbon nanotube concentration from 5 to 20 mg. mL-1 decreased biosorption potential from 100% to 28.55 ± 1.7%. The study also found that an increase in initial fungal growth time led to higher biomass production and adsorption capacity, increasing biosorption ability for concentrations > 5mg. ml-1. The biosorption kinetics followed a pseudo-second-order model and corresponded most closely to the Freundlich isotherm model. The adsorption capacity of live fungal biomass to remove multi-walled carbon nanotubes was 945.17 mg. g-1, indicating that T. versicolor fungi have significant potential for removing carbon nanostructures from the environment.