Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste

The presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, and industrial chemicals from runoff and discharges, poses critical public health and environmental risks leading to severe health issues and ecosystem damage; education plays a crucial role in mitigating these effects by enhancing awareness, promoting sustainable practices, and integrating environmental science into curricula to empower individuals to address and advocate for effective solutions to water pollution. However, the educational transformation should be accompanied with a technical process which can be eventually transferred to society to empower environmental education. In this study, carbonaceous material derived from Haematoxylum campechianum (CM-HC) was utilized for removing 3-nitrophenol (3-Nph) from aqueous solutions. The novelty of this research utilizes Haematoxylum campechianum bark and coconut shell, abundant agricultural wastes in Campeche, Mexico, for toxin removal, enhancing the adsorption process through artificial neural networks and genetic algorithms to optimize conditions and maximize the absorption efficiency. CM-HC's surface morphology was analyzed using scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and pHpzc. Kinetic models including pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich were applied to fit the data. Adsorption isotherms were determined at varying pH (3-8), adsorbent dosages (2-10 g/L), and temperatures (300.15-330.15 K), employing Langmuir, Freundlich, Temkin, and Redlich-Peterson models. PSO kinetics demonstrated a good fit (R2 > 0.98) for Ci = 50-100 mg/L, indicating a chemical adsorption mechanism. The Langmuir isotherm model exhibited the best fit, confirming chemical adsorption, with a maximum adsorption capacity (Qm) of 236.156 mg/g at T = 300.15 K, pH = 6, contact time = 3 h, and 2 g/L adsorbent dosage. Lower temperatures favored exothermic adsorption. Artificial neural networks (ANNs) were employed for deep learning, optimizing the predictive model for removal percentage. Correlation heat maps highlighted positive correlations between time, dosage, and removal percentage, emphasizing the impact of initial concentration on efficiency. ANN modeling, incorporating iterative optimization, yielded highly accurate predictions, aligned closely with experimental results. The study showcases the success of deep learning in optimizing adsorption processes, emphasizing the importance of diverse correlation algorithms for comprehensive insights into competitive adsorption dynamics. The 5-14-14-1 deep learning architecture, fine-tuned over 228 epochs, demonstrated strong performance with mean squared error (MSE) values of 4.07, 18.406, and 6.2122 for training, testing, and total datasets, respectively, and high R-squared values. Graphical analysis showed a solid linear correlation between experimental and simulated removal percentages, emphasizing the need to consider more than just testing data for optimization. Experimental validation confirmed a 98.77% removal efficiency, illustrating the effectiveness of combining deep learning with genetic algorithms, and highlighting the necessity of experimental trials to verify computational predictions. It is concluded that the carbonaceous material from Haematoxylum campechianum waste (CM-HC) is an effective, low-cost adsorbent for removing 3-nitrophenol from aqueous solutions, achieving optimal removal at pH 6 and 300.15 K with a maximum adsorption capacity of 236.156 mg/g, following Langmuir model and pseudo-second order kinetics. The validated ANN model offers a reliable tool for practical applications in environmental remediation, advancing both environmental science and educational innovation by integrating artificial neural networks and data science methodologies into student learning experiences.

Biomass-based activated carbons produced by chemical activation with H3PO4 as catalysts for the transformation of α-pinene to high-added chemicals

In the era of ecology and careful care for the environment, it becomes important to use renewable raw materials of plant origin, which are often more easily available and cheaper. One of the important and rapidly developing directions of research are works related to the use of waste plant biomass; an example of this trend is the production of activated carbons from food industry waste. One of the examples of the application of derived from biomass activated carbons can be using them as catalysts for the isomerization of terpene compounds. Carbons based on waste biomass are characterized by the minimal amount of waste formation during their manufacture, and their use in the isomerization reaction allows to obtain high conversion of organic raw material and high selectivities of transformation to the desired products, making these carbons environmentally friendly substitutes for the catalysts used usually in this process. In this work, obtained carbonaceous catalysts were tested in the process of isomerization of α-pinene to high value chemicals (mainly camphene and limonene). Under the most favorable conditions (activated carbon from sunflower husks content in reaction mixture 5 wt%, temperature 180 °C, and reaction time 100 min), α-pinene was completely converted (conversion 100 mol%) with high selectivity towards camphene (54 mol%). To prepare activated carbons, biomass precursors (orange peels, sunflower husks, spent coffee grounds) were activated with 85% H3PO4 through the chemical activation. The obtained materials were characterized by such methods as sorption N2 at - 196 °C, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), and X-ray fluorescence (XRF) to determine the relationship between their textural-chemical properties and catalysts activity in isomerization process. The synthesized materials were characterized by a specific surface area in the range of 930-1764 m2, total pore volume in the range of 0.551-1.02 cm3/g, and total acid-site concentrations in the range of 1.47-2.33 mmol/g. These results showed that textural parameters of the obtained activated carbons have the important role in the isomerization of α-pinene.

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process

This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from Haematoxylum campechianum waste (ABHC). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar. The surface morphology of the adsorbent (before and after dye adsorption) was characterized by scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) and, lastly, pHpzc was also determined. Batch studies were carried out in the following intervals of pH = 4-10, temperature = 300.15-330.15 K, the dose of adsorbent = 1-10 g/L, and isotherms evaluated the adsorption process to determine the maximum adsorption capacity (Qmax, mg/g). Kinetic studies were performed starting from two different initial concentrations (25 and 50 mg/L) and at a maximum contact time of 48 h. The reusability potential of activated biochar was evaluated by adsorption-desorption cycles. The maximum adsorption capacity obtained with the Langmuir adsorption isotherm model was 114.8 mg/g at 300.15 K, pH = 5.4, and a dose of activated biochar of 1.0 g/L. This study also highlights the application of advanced machine learning techniques to optimize a chemical removal process. Leveraging a comprehensive dataset, a Gradient Boosting regression model was developed and fine-tuned using Bayesian optimization within a Python programming environment. The optimization algorithm efficiently navigated the input space to maximize the removal percentage, resulting in a predicted efficiency of approximately 90.47% under optimal conditions. These findings offer promising insights for enhancing efficiency in similar removal processes, showcasing the potential of machine learning in process optimization and environmental remediation.

A Critical Review of the Removal of Radionuclides from Wastewater Employing Activated Carbon as an Adsorbent

Radionuclide-contaminated water is carcinogenic and poses numerous severe health risks and environmental dangers. The activated carbon (AC)-based adsorption technique has great potential for treating radionuclide-contaminated water due to its simple design, high efficiency, wide pH range, quickness, low cost and environmental friendliness. This critical review first provides a brief overview of the concerned radionuclides with their associated health hazards as well as different removal techniques and their efficacy of removing them. Following this overview, this study summarizes the surface characteristics and adsorption capabilities of AC derived from different biomass precursors. It compares the adsorption performance of AC to other adsorbents, such as zeolite, graphene, carbon nano-tubes and metal-organic frameworks. Furthermore, this study highlights the different factors that influence the physical characteristics of AC and adsorption capacity, including contact time, solution pH, initial concentration of radionuclides, the initial dosage of the adsorbent, and adsorption temperature. The theoretical models of adsorption isotherm and kinetics, along with their fitting parameter values for AC/radionuclide pairs, are also reviewed. Finally, the modification procedures of pristine AC, factors determining AC characteristics and the impact of modifying agents on the adsorption ability of AC are elucidated in this study; therefore, further research and development can be promoted for designing a highly efficient and practical adsorption-based radionuclide removal system.

Biocarbons Obtained from Fennel and Caraway Fruits as Adsorbents of Methyl Red Sodium Salt from Water System

The aim of this study was to prepare biocarbons by biomass activation with carbon(IV) oxide. Fennel and caraway fruits were used as the precursors of bioadsorbents. The impact of the precursor type and temperature of activation on the physicochemical properties of the obtained biocarbons and their interaction with methyl red sodium salt upon adsorption process have been checked. The obtained bioadsorbents were characterized by determination of-low temperature nitrogen adsorption/desorption, elemental analysis, ash content, Boehm titration, and pH of water extracts. The biocarbons have surface area varying from 233-371 m2/g and basic in nature with acidic/basic oxygen-containing functional groups (3.23-5.08 mmol/g). The adsorption capacity varied from 63 to 141 mg/g. The influence of different parameters, such as the effectiveness of methyl red sodium salt adsorption, was evaluated. The adsorption kinetics was well fitted using a pseudo-second-order model. The Freundlich model best represented the equilibrium data. The amount of adsorbed dye was also found to increase with the increasing temperature of the process.

Synthesis and Characterization of Activated Carbon from Agrowastes for the Removal of Acetic Acid from an Aqueous Solution

In this study, activated carbons prepared from agrowastes by chemical activation were used to remove acetic acid from an aqueous solution through a batch process. The prepared adsorbents were characterized by SEM, XRD, FT-IR, and point of zero charge (pHpzc). The effects of adsorbent dosage, initial concentration, and contact time were considered. Equilibrium data was tested using Langmuir, Freundlich, Temkin, and Frenkel–Halsey–Hill models. The degree of adsorption of acetic acid increased for both adsorbents as contact time, and adsorbent dosage and initial concentration were increased. The adsorption data were described well by the (Freundlich=Frenkel–Halsey–Hill) models with the highest regression coefficient of $R^2=0.9961$ and $R^2=0.9951$ for Rice Husk Activated Carbon (RH-AC) and Potato Peels Activated Carbon (PP-AC), respectively. This suggests a multilayer through the existence of a heterogeneous pore distribution in the adsorbent surface. Kinetic data agreed well with pseudosecond-order ( $R^2=0.999$ and $R^2=0.994$ ) RH-AC and PP-AC, correspondingly. This indicates that the adsorption process was chemisorption in nature. The regeneration studies showed that the adsorbents prepared could be renewed and reused before losing their adsorbing affinity for acetic acid.