ChemEngineering

Prolonged exposure to landfill leachate can weaken the impermeability of liner systems, leading to leachate leakage and the contamination of surrounding soil and water. To improve loess impermeability to enable its use as a liner material, this study uses synthetic landfill leachate to investigate its effects on loess permeability via a series of laboratory tests. This study focused on the influence of varying dosages of calcium lignosulfonate (CLS) on loess permeability, along with its capacity to adsorb and immobilize heavy metal ions. Microscale characterization techniques, including Zeta potential analysis, X-ray fluorescence spectroscopy (XRF), and scanning electron microscopy (SEM), were employed to investigate the impermeability mechanisms of CLS-modified loess and its adsorption behavior toward heavy metals. The results indicate that the permeability coefficient of loess decreases significantly with increasing compaction, while higher leachate concentrations lead to a notable increase in permeability. At a compaction degree of 0.90, the permeability coefficient was reduced to 8 × 10⁻⁸ cm/s. In contrast, under conditions of maximum leachate concentration, the permeability coefficient rose markedly to 1.5 × 10⁻⁴ cm/s. Additionally, increasing the dosage of the compacted loess stabilizer (CLS) effectively reduced the permeability coefficient of the modified loess to 7.1 × 10⁻⁵ cm/s, indicating improved impermeability and enhanced resistance to contaminant migration. With the prolonged infiltration time of landfill leachate, the removal efficiency of Pb²⁺ gradually decreases and stabilizes, while the Pb²⁺ removal efficiency of the modified loess increased by approximately 40%. CLS-modified loess, through multiple mechanisms, reduces the fluid flow pathways and enhances its adsorption capacity for Pb²⁺, thereby improving the soil’s protection against heavy metal contamination. While these results demonstrate the potential of CLS-modified loess as a sustainable landfill liner material, the findings are based on controlled laboratory conditions with Pb²⁺ as the sole target contaminant. Future work should evaluate long-term performance under field conditions, including seasonal wetting–drying and freeze–thaw cycles, and investigate multi-metal systems to validate the broader applicability of this modification technique. Full article

As academic institutions expand, the proliferation of laboratories dealing with hazardous chemicals has risen. While the physicochemical characterization equipment employed in these academic chemical laboratories is widely recognized, its usage presents a notable risk to researchers at various levels. This paper presents a simplified approach for evaluating the effects of the implementation of prevention investments in regard to working with nanomaterials on a lab scale. The evaluation is based on modeling the benefits (avoided accident costs) and costs (safety training), as opposed to an alternative (not investing in safety training). Each scenario analyzed in the economic evaluation reflects a different level of risk. The novelty of this study lies in its objective to provide an economic assessment of the benefits and returns from safety investments—specifically training—in a chemical laboratory, using a framework that integrates qualitative insights to explore and define the context alongside quantitative data derived from a cost–benefit analysis. The Net Present Value (NPV) was evaluated. The results of the cost–benefit analysis demonstrated that the benefits exceed the cost of the investment. The findings from the sensitivity analysis highlight the significant influence of insurance benefits on safety investments in the specific case study. In this case study, the deterministic analysis yielded a Net Present Value (NPV) of €280,414.67, which aligns closely with the probabilistic results. The probabilistic NPV indicates 90% confidence that the investment will yield a positive NPV ranging from €283,053 to €337,356. The cost–benefit analysis results demonstrate that the benefits outweigh the costs, showing that with an 87% training success rate, this investment would generate benefits of approximately €6328 by preventing accidents in this study. To the best of the researchers’ knowledge, this is the first study to evaluate the influence of safety investment through an economic evaluation of laboratory accidents with small-angle X-ray scattering during the physicochemical characterization process of engineered nanomaterials. The proposed approach and framework are relevant not only to academic settings but also to industry. Full article

Process designs based on deterministic simulations without considering parameter uncertainty or variability have a high probability of failing to meet specifications. In this work, uncertainty and global sensitivity analyses were applied to a biogas upgrading membrane process implemented in the COCO simulator (CAPE-OPEN to CAPE-OPEN), considering both controlled and non-controlled scenarios. A user-defined model code was developed to simulate gas separation membrane stages, and a preliminary study of membrane parameter uncertainty was performed. In addition, a unit generating combinations of uncertainty factors was developed to interact with the simulator’s parametric tool. Global sensitivity analyses were carried out using the Morris method and Sobol’ indices obtained by Polynomial Chaos Expansion, allowing for the ranking and quantification of the influence of feed variability and membrane parameter uncertainty on product streams and process utilities. Results showed that when feed variability was ±10%, its effect exceeded the uncertainty of the membrane parameters. Uncertainty analysis using the Monte Carlo propagation method provided lower and upper tolerance limits for the main responses. Relative gaps between tolerance limits and mean product flows were 8–9% at a feed variability of 5% and 14–18% at a feed variability of 10%, while relative tolerance gaps resulting from composition were smaller (0.4–1.2%). Full article

Bioethanol from sugarcane bagasse is a promising second-generation biofuel due to its abundance as a sugar industry by-product. Herein, enzymatic hydrolysate obtained from sugarcane bagasse pretreated with optimized hydrothermal alkaline sulfite (HAS) was evaluated for its fermentability using Saccharomyces cerevisiae PE-2 and Scheffersomyces stipitis CBS 5773. The HAS pretreatment achieved a high delignification rate (63%), resulting in a cellulose- and hemicellulose-enriched substrate (55% and 27%, respectively). While the cellulose content remained relatively constant, hemicellulose content was reduced by 25%, with significant removal of acetyl groups (80%) and arabinan groups (39%). The pretreated bagasse exhibited high digestibility, applying 10 FPU (filter paper unit) cellulase together with 10 CBU (cellobiose unit) β-glucosidase per gram of dry bagasse in the hydrolysis step, yielding 72% glucan and 66% xylan conversion within 72 h. The resulting hydrolysate was efficiently fermented by S. cerevisiae and S. stipitis, achieving ethanol yields of 0.51 and 0.43 g/g of sugars, respectively. The fermentation kinetics were comparable to those observed in a synthetic medium containing pure sugars, demonstrating the effectiveness of HAS pretreatment in generating readily fermentable, carbohydrate-rich substrates. HAS pretreatment enabled improved conversion of sugarcane bagasse into fermentation-ready sugars, constituting a potential resource for bioethanol synthesis applying both S. cerevisiae and S. stipitis in the future. Full article

This study proposes considering the effective re–benefication of coal middlings and other such considered waste materials as a way to ensure that clean coal in coal by–products can be extracted and effectively utilized, saving costs and reducing coal waste. To quantify the clean–coal yield and ash reduction that can be achieved by re–beneficiating four typical by–product streams from the Guobei Coal Preparation Plant (6 Mt a⁻¹) were used for the study. Coking–coal middlings, flotation tailings, and pressure–filter cakes from preparation plants still contain 30–60% combustible matter. Re–beneficiation techniques have been considered to recover this often-wasted coal, reduce waste rock disposal, and cut greenhouse–gas emissions per ton of clean coal produced. Representative samples (n = 4) were collected, sample size–classified as (fine coal particles ≤0.5 mm and coarse particles ≥) and subjected to (i) magnetite removal, (ii) laboratory froth flotation (diesel 507 g t⁻¹, sec–octanol 103 g t⁻¹), and (iii) fine and large particle density separation at 1.3–1.8 g cm⁻³ ZnCO₃ media. Clean–coal yield and ash were measured for each stream and the coal’s particle liberation was examined by SEM. Crushing, grinding and liberation equipment and techniques that aid in the treatment of coal and the re–beneficiation of coal middlings and tailings. The key findings recorded during the experiment are as follows: Flotation of <0.5 mm fractions delivered 46.9–58.3% clean–coal yield at 10.3–17.0% ash. Density separation of 0.5–1.0 mm middlings peaked at 1.4–1.5 g cm⁻³, yielding 34.2% clean coal at 15–18.4% ash. Scanning Electron Microscope analysis confirmed partial liberation as results from re–grinding + second flotation which increased yield by a further 8–12%. A calculated theoretical examination of the preliminary cost–benefit analysis indicates ≈36 CNY t⁻¹≈9 million CNY a⁻¹ in saved disposal costs alone. savings in disposal and 0.25 Mt a⁻¹ additional clean coal for the Guobei plant. The research presented in this paper highlights the current work by Anhui University of Science and technology in collaboration with Guobei coal preparation plant and the results therein achieved. Full article

This study explores the physicochemical and toxicological properties of six G-series and A-series chemical warfare agents (Sarin, Soman, Tabun, A230, A232, and A234) using an integrated computational approach combining molecular dynamics (MD) simulations and Quantitative Structure–Activity Relationship (QSAR) modeling. For the A-series nerve agents, both Ellison–Hoenig and Mirzayanov structural proposals were examined. MD simulations (10 ns, NPT ensemble) provided key thermodynamic properties, including density, molar heat capacity, and diffusivity. Simulated densities for G-agents (e.g., Sarin: 1.09 g/cm³, Soman: 1.03 g/cm³) and A-agents (e.g., A230: 1.608 g/cm³, Ellison–Hoenig model) closely matched experimental data. Heat capacities ranged from 258 to 462 J/mol·K, and self-diffusion coefficients revealed lower mobility for A-agents, especially under the Ellison–Hoenig configurations. QSAR modeling focused on lipophilicity (LogP) and acute toxicity (LD₅₀). Predicted LD₅₀ values ranged from 0.012 to 0.017 mg/kg for G-agents and up to 1.23 mg/kg for A-agents. A-234 showed the highest lipophilicity (LogP = 2.97) and toxicity (LD₅₀ = 0.51 mg/kg) within its group. Additional descriptors, such as molecular weight and polar surface area, supported toxicity predictions. Strong correlations emerged between MD-derived properties and QSAR outputs, validating the integrated approach. The combined use of MD and QSAR techniques provided a comprehensive view of the agents’ environmental behavior and toxicological impact, supporting safer assessment strategies and reinforcing the importance of multidisciplinary modeling for chemical threat mitigation. Full article

Piezoelectric catalytic technology has attracted much attention in the field of dye wastewater treatment, in which inorganic piezoelectric materials have been widely studied. Its core mechanism involves utilizing the piezoelectric effect to generate positive and negative charges, which react with oxygen ions and hydroxyl radicals, respectively, to generate reactive oxygen species to degrade organic pollutants. Currently, while organic piezoelectric catalysts theoretically offer significant advantages such as low cost and high processability, there has been a notable lack of research in this area, which presents an innovative opportunity for the exploration of new organic piezoelectric catalytic materials. In this study, new research using natural nanocellulose (FC) suspension as an efficient organic piezoelectric catalyst is reported for the first time. The experimental results showed that the catalyst exhibited excellent degradation performance for Rhodamine B (RhB), Acid Orange 7 (AO7), and Methyl Orange (MO) under ultrasonic vibration (40 kHz, 200 W): the degradation rates reached 95.4%, 72.4%, and 31.2%, respectively, for 150 min, and the corresponding first-order reaction kinetic constants were 0.0205, 0.00858, and 0.00249 min⁻¹, respectively. It is noteworthy that the RhB solution can achieve the optimal degradation efficiency without adjustment under neutral initial pH conditions, which significantly enhances the practical application feasibility. The experimental results showed that the catalyst, with a measurable piezoelectric coefficient (d33 = 4.4 pm/V), exhibited excellent degradation performance for Rhodamine B (RhB), Acid Orange 7 (AO7), and Methyl Orange (MO) under ultrasonic vibration (40 kHz, 200 W). This organic piezoelectric catalyst, based on renewable biomass, innovatively converts mechanical vibration energy in the environment into the power to degrade pollutants. It not only expands the application boundaries of organic piezoelectric materials but also provides a new solution for sustainable water treatment technology, demonstrating extremely promising application prospects in the field of green and environmentally friendly water treatment. Full article

This study analyzes the dynamics of temperature and moisture in a cylindrical silo with a conical roof and floor used for storing corn in the Bajío region of Mexico, considering conditions both with and without aeration. The model incorporates external temperature fluctuations, solar radiation, grain moisture equilibrium with air humidity through the sorption isotherm (water activity), and grain respiration to simulate real storage conditions. The model is based on continuity, momentum, energy, and moisture conservation equations in porous media. This model was solved using the finite element method (FEM) to evaluate temperature and interstitial humidity variations during January and May, representing cold and warm environmental conditions, respectively. The simulations show that, without aeration, grain temperature progressively accumulates in the center and bottom region of the silo, reaching critical values for safe storage. In January, the low ambient temperature favors the natural dissipation of heat. In contrast, in May, the combination of high ambient temperatures and solar radiation intensifies thermal accumulation, increasing the risk of grain deterioration. However, implementing aeration periods allowed for a reduction in the silo’s internal temperature, achieving more homogeneous cooling and reducing the threats of mold and insect proliferation. For January, an airflow rate of 0.15 m³/(min·ton) was optimal for maintaining the temperature within the safe storage range (≤17 °C). In contrast, in May, neither this airflow rate nor the accumulation of 120 h of aeration was sufficient to achieve optimal storage temperatures. This indicates that, under warm conditions, the aeration strategy needs to be reconsidered, assessing whether a higher airflow rate, longer periods, or a combination of both could improve heat dissipation. The results also show that interstitial relative humidity remains stable with nocturnal aeration, minimizing moisture absorption in January and preventing excessive drying in May. However, it was identified that aeration period management must be adaptive, taking environmental conditions into account to avoid issues such as re-wetting or excessive grain drying. Full article

Electrochemical sensors have been developed based on polyethylene terephthalate track-etched membranes (PET TeMs) modified by photograft copolymerization of N-vinylformamide (N-VFA) and trimethylolpropane trimethacrylate (TMPTMA). The modification, structure and properties of the modified PET TeMs were thoroughly characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM), thermogravimetric analysis (TGA), Fourier-transform infrared (FTIR) spectroscopy, gas permeability measurements and contact angle analysis. Optimal membrane modification was achieved using C = 10% (N-VFA), 60 min of UV irradiation and a UV lamp distance of 10 cm. Furthermore, the modified membranes were implemented in a two-electrode configuration for the determination of Eu³⁺, Gd³⁺, La³⁺ and Ce³⁺ ions via square-wave anodic stripping voltammetry (SW-ASV). The sensors exhibited a linear detection range from 10⁻⁷ M to 10⁻³ M, with limits of detection of 1.0 × 10⁻⁶ M (Eu³⁺), 6.0 × 10⁻⁶ M (Gd³⁺), 2.0 × 10⁻⁴ M (La³⁺) and 2.5 × 10⁻⁵ M (Ce³⁺). The results demonstrated a significant enhancement in electrochemical response due to the grafted PET TeMs-g-N-PVFA-TMPTMA structure, and the sensor showed practical applicability and consistent performance in detecting rare earth ions in tap water. Full article

This paper proposes the development of a robust nonlinear soft sensor for online estimation of product compositions in a Heat-Integrated Distillation Column (HIDiC). Traditional composition analyzers, such as gas chromatographs, are costly and suffer from long measurement delays, making them inefficient for real-time monitoring and control. To address this, data-driven soft sensors are developed using tray temperature data obtained from a high-fidelity dynamic HIDiC simulation. The study investigates both linear and nonlinear modeling strategies for composition estimation, including principal component regression (PCR), artificial neural networks (ANNs), and, for the first time in HIDiC modeling, a Bidirectional Long Short-Term Memory (BiLSTM) network. The objective is to evaluate the capability of each method for accurate estimation of product compositions in a HIDiC. The results demonstrate that the BiLSTM-based soft sensor significantly outperforms conventional methods and offers strong potential for enhancing real-time composition estimation and control in HIDiC systems. Full article

In this study, a novel magnetic nanocatalyst based on tin ferrite (SnFe₂O₄) was synthesized via a chemical co-precipitation method and thoroughly characterized using XRD, SEM, TGA-DTG, BET, FTIR, and FTIR-pyridine techniques. The catalyst exhibited high crystallinity, a mesoporous structure with a specific surface area of 79.7 m²/g, and well-defined Lewis and Brønsted acid sites. Its catalytic performance was assessed in the esterification of glycerol with acetic acid to produce monoacetin (MAG), diacetin (DAG), and triacetin (TAG). A Box–Behnken experimental design was employed to evaluate the influence of temperature, catalyst loading, and the acetic-acid-to-glycerol molar ratio on product distribution and glycerol conversion. Statistical analysis and regression modeling revealed excellent predictive accuracy (R² > 0.99), confirming the robustness of the model. Optimal reaction conditions (110 °C, 2 wt.% catalyst, and AA/GLY ratio of 3.6) yielded a maximum glycerol conversion of 93.2% and a combined DAG and TAG yield of ~59.1%. These results demonstrate the high efficiency and selectivity of the synthesized SnFe₂O₄ catalyst, making it a promising candidate for sustainable glycerol valorization. Full article

The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently large to train complex eGNN models effectively. However, certain material groups with significant industrial relevance, such as aromatic compounds, remain underrepresented in these large datasets. In this work, we aim to bridge the gap between limited, domain-specific DFT datasets and large-scale pretrained eGNNs. Our methodology involves creating a specialized dataset by segregating aromatic compounds after a targeted ensemble extraction process, then fine-tuning a pretrained model via approaches that include full retraining and systematically freezing specific network sections. We demonstrate that these approaches can yield accurate energy and force predictions with minimal domain-specific training data and computation. Additionally, we investigate the effects of augmenting training datasets with chemically related but out-of-domain groups. Our findings indicate that incorporating supplementary data that closely resembles the target domain, even if approximate, would enhance model performance on domain-specific tasks. Furthermore, we systematically freeze different sections of the pretrained models to elucidate the role each component plays during adaptation to new domains, revealing that relearning low-level representations is critical for effective domain transfer. Overall, this study contributes valuable insights and practical guidelines for efficiently adapting deep learning models for accurate adsorption energy predictions, significantly reducing reliance on extensive training datasets. Full article

Ternary choline chloride-based deep eutectic solvents (TDESs) exhibit unique physicochemical properties, including lower viscosities, lower melting points, higher thermal stabilities, and enhanced solvations compared to binary deep eutectic solvents (BDESs). Although BDESs have been widely studied, the addition of a third component in TDESs offers opportunities to further optimize their performance. This review aims to evaluate the physicochemical properties of TDESs and highlight their potential applications in sustainable industrial processes compared to BDESs. A comprehensive analysis of the existing literature was conducted, focusing on TDES properties, such as phase behavior, density, viscosity, pH, conductivity, and the effect of water, along with their applications in various fields. TDESs demonstrated superior physicochemical characteristics compared to BDESs, including improved solvation and thermal stability. Their applications in biomass conversion, CO₂ capture, heavy oil upgrading, refrigeration gases, and as solvents/catalysts in organic reactions show significant promise for enhancing process efficiency and sustainability. Despite their advantages, TDESs face challenges including limited predictive models, potential instability under certain conditions, and scalability hurdles. Overall, TDESs offer significant potential for advancing sustainable and efficient chemical processes for industrial applications. Full article

The contamination of drinking water by per- and polyfluoroalkyl substances (PFASs) presents a global concern due to their extreme persistence, driven by strong C–F bonds. This study investigated the potential of graphene as a filtration material for PFAS removal, focusing on six key compounds regulated by the U.S. EPA: PFOA, PFNA, GenX, PFBS, PFOS, and PFHxS. Using molecular simulations, adsorption energy, diffusion coefficients, and PFAS-to-graphene distances were analyzed. The results showed that adsorption strength increased with molecular weight; PFOS (500 g/mol) exhibited the strongest adsorption (−171 kcal/mol). Compounds with sulfonic acid head groups (e.g., PFOS) had stronger interactions than those with carboxylate groups (e.g., PFNA), highlighting the importance of head group chemistry. Shorter graphene-to-PFAS distances also aligned with higher adsorption energies. PFOS, for example, had the shortest distance at 8.23 Å (head) and 6.15 Å (tail) from graphene. Diffusion coefficients decreased with increasing molecular weight and carbon chain length, with lower molecules like PFBS (four carbon atoms) diffusing more rapidly than heavier ones like PFOS and PFNA. Interestingly, graphene enhanced PFAS mobility in water, likely by disrupting the water structure and lowering intermolecular resistance. These results highlight graphene’s promise as a high-performance material for PFAS removal and future water purification technologies. Full article

Current work investigates the fabrication and performance of nanocomposite membranes, modified with varying concentrations of hybrid nanostructures comprising titanium nanowires coated with iron nanoparticles (TiO₂ NWs-Fe₂O₃), for the removal of Naphthol Blue Black (NBB) dye from industrial wastewater. A series of analytical tools were employed to confirm the successful modification including scanning electron microscopy and EDX analysis, porosity and hydrophilicity measurements, Fourier-transform infrared spectroscopy, and X-Ray Diffraction. The incorporation of TiO₂ NWs-Fe₂O₃ has enhanced membrane performance significantly by increasing the PWF and improving dye retention rates of nanocomposite membranes. At 0.7 g of nanostructure content, the modified membrane (M8) achieved a PWF of 93 L/m²·h and NBB dye rejection of over 98%. The flux recovery ratio (FRR) analysis disclosed improved antifouling properties, with the M8 membrane demonstrating a 73.4% FRR. This study confirms the potential of TiO₂ NWs-Fe₂O₃-modified membranes in enhancing water treatment processes, offering a promising solution for industrial wastewater treatment. These outstanding results highlight the potential of the novel PES-TiO₂ NWs-Fe₂O₃ membranes for dye removal and present adequate guidance for the modification of membrane physical properties in the field of wastewater treatment. Full article

Platinum has emerged as an optimal catalyst for the selective hydrogenation of nitroarenes owing to its high hydrogenation activity, selectivity, and stability. In this study, we report the fabrication of platinum nanoparticles stabilized on a composite support consisting of gum acacia polymer (GAP) and TiO₂. It was engineered for the targeted reduction of nitroarenes to arylamines via selective hydrogenation in methanol at ambient temperature. The non-toxic and biocompatible properties of GAP enable it to act as a reducing and stabilizing agent during synthesis. The synthesized nanocatalyst was characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Morphological and structural analyses revealed that the fabricated catalyst consisted of minuscule Pt nanoparticles integrated within the GAP framework, accompanied by the corresponding TiO₂ nanoparticles. Inductively coupled plasma optical emission spectrometry (ICP-OES) was employed to ascertain the Pt content. The mild reaction conditions, decent yields, trouble-free workup, and facile separation of the catalyst make this method a clean and practical alternative to nitroreduction. Selective hydrogenation yielded an average arylamine production of 97.6% over five consecutive cycles, demonstrating the stability of the nanocatalyst without detectable leaching. Full article

The global atmospheric concentration of carbon dioxide (CO₂) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO₂ uptake presents limitations due to the need for case-specific parameter fitting. To address this, the present study introduces a universal machine learning (ML) framework using multiple algorithms—Generalized Linear Model (GLM), Feed-forward Multilayer Perceptron (DL), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—to reliably predict CO₂ adsorption capacities across diverse zeolite structures and conditions. By compiling over 5700 experimentally measured adsorption data points from 71 independent studies, this approach systematically incorporates critical factors including pore size, Si/Al ratio, cation type, temperature, and pressure. Rigorous Cross-Validation confirmed superior performance of the GBT model (R² = 0.936, RMSE = 0.806 mmol/g), outperforming other ML models and providing comparable performance with classical Langmuir model predictions without separate parameter calibration. Feature importance analysis identified pressure, Si/Al ratio, and cation type as dominant influences on adsorption performance. Overall, this ML-driven methodology demonstrates substantial promise for accelerating material discovery, optimization, and practical deployment of zeolite-based CO₂ capture technologies. Full article

Metakaolin-based geopolymers with different molar ratios of Si/Al were synthesized and utilized as an efficient adsorbent for the removal of methylene blue (MB) as a model cationic dye from aqueous solution. Various analytical techniques were employed to characterize the synthesized geopolymers. The influence of the main operation conditions on the adsorption kinetics of MB onto the geopolymer was examined under various operating conditions. Results showed a significant maximum MB adsorption capacity at the temperature of 30 °C for all four types of geopolymers studied (designated as A, B, C, and D) up to 35.3, 23.6, 25.5, and 19.0 mg g⁻¹, respectively. The corresponding order of Si/Al ratio was A < C < B < D. Adsorption kinetics was so fast and reached equilibrium in 10 min, and the experimental results were described using the adsorption dynamic intraparticle model (ADIM). The equilibrium data for MB removal was in agreement with the Langmuir isotherm. Full article

In the context of industrial digitalization, the Industry 5.0 paradigm introduces digital twins as a cutting-edge solution. This study explores the concept of digital twins and their integration with the Industrial Internet of Things (IIoT), offering insights into how these technologies bring intelligence to industrial settings to drive both process optimization and sustainability. Industrial digitalization connects products and processes, boosting the productivity and efficiency of people, facilities, and equipment. These advancements are expected to yield broad economic and environmental benefits. As connected systems continuously generate data, this information becomes a vital asset, but also introduces new challenges for industrial operations. The work presented in this article aims to demonstrate the possibility of generating advanced tools for process optimization. This, which ultimately impacts the environment and empowers people in the processes, is achieved through data integration and the development of a digital twin using open tools such as NodeRed v4.0.9 and Python 3.13.5 frameworks, among others. The article begins with a conceptual analysis of IIoT and digital twin integration and then presents a case study to demonstrate how these technologies support the principles of the Industry 5.0 framework. Specifically, it examines the requirements for applying the golden batch concept within a biological production environment. The goal is to illustrate how digital twins can facilitate the achievement of quality standards while fostering a more sustainable production process. The results from the case study show that biomaterial concentration was optimized by approximately 10%, reducing excess in an initially overdesigned process. In doing so, this paper highlights the potential of digital twins as key enablers of Industry 5.0—enhancing sustainability, empowering operators, and building resilience throughout the value chain. Full article