Applied Chemical Engineering

Abstract The rapid growth of bio-energy production is closely aligned with global sustainability agendas, particularly the Sustainable Development Goals (SDGs) related to Affordable and Clean Energy (SDG 7) , Industry, Innovation and Infrastructure (SDG 9) , and Climate Action (SDG 13) . Effective pollution monitoring across the bio-energy production chain is essential to ensure that renewable energy expansion does not lead to unintended environmental burdens. Current research largely treats artificial intelligence (AI) applications in environmental monitoring and bio-energy systems as separate domains, creating a research gap in integrated, process-wide frameworks that connect emission sources, sensor networks, data pipelines, and AI models across all production stages. The objective of this study is to critically review AI-based pollution monitoring approaches for bio-energy systems and to assess their capability to support sustainable and responsible energy production in line with SDG targets. The methodology involves a structured synthesis of recent literature on sensing technologies, data acquisition and preprocessing, machine learning and deep learning models, and hybrid physics-informed approaches applied from biomass handling to biofuel refining. The key findings show that AI-enabled monitoring improves real-time emission estimation, early detection of abnormal events, and short-term forecasting, supporting cleaner production pathways. At the same time, challenges related to sensor drift, data scarcity, model transferability, and interpretability limit large-scale adoption. The implications of this review highlight the need for open benchmark datasets, robust calibration strategies, and explainable AI models to strengthen regulatory trust, promote sustainable industrial practices, and contribute directly to achieving SDG-linked environmental and energy objectives.

Abstract The increasing complexity of bio-energy systems is a reason for the need of advanced analytical methods to enhance resource utilization, process stability and environmental performance. AI methods are popular in this domain, but many papers neglect concerns around data quality, interpretability, scalability and the low generalization potential of models toward plants and feedstocks different from those they were trained on. This paper intends to offer a systematic review on AI techniques available for biomass resource assessment, conversion-process optimization, and supply-chain planning and emission management. The review is structured adopting a rigorous review approach focusing on model, data set, optimization framework and hybrid method developed in the scope of bio-energy value chain. Highlights – The key findings are that AI improves the prediction of biomass availability and biogas/syngas yields, of feedstock properties and emission behavior; surrogate and hybrid models result in expedited simulation time and facilitate real-time decision making. The review also highlights an emerging trend with digital twins, remote sensing with application of machine learning, and federated learning in multi-plant optimization. These findings also have important implications for researchers, engineers and policy-makers who aim to develop robust low emissions bio-energy systems that are economically feasible.

Abstract This study presents a novel, one-pot synthetic strategy for preparing triazole and oxadiazoline derivatives directly from naproxen. This approach aligns with the principles of green chemistry, aiming to enhance synthetic efficiency by minimizing reaction steps and reducing waste. By eliminating the need for multiple isolation and purification stages, this method offers a sustainable alternative to conventional multi-step procedures. The synthesized compounds underwent structural confirmation using a suite of spectroscopic techniques, specifically Fourier-Transform Infrared  spectroscopy, Proton Nuclear Magnetic Resonance  spectroscopy, and Carbon-13 Nuclear Magnetic Resonance spectroscopy and CNMR dept 135 and CNMR dept 90. Further analysis supported their potential as anti-inflammatory agents through molecular docking studies. These studies demonstrated strong binding affinities of the compounds to the cyclooxygenase-2 (COX-2) enzyme, suggesting a favorable mechanism of action for anti-inflammatory activity. Additionally, their acute toxicity was assessed by determining the LD50 values, providing preliminary data on their safety profile. Collectively, the new derivatives exhibited promising multi-target activity. The synthesized compounds exhibit potent broad-spectrum antimicrobial effects, demonstrating significant efficacy against both “Gram-positive bacteria, Staphylococcus aureus  and Staphylococcus epidermidis” , and “Gram-negative bacteria, including Klebsiella  species and Escherichia coli . Furthermore, they show promising antifungal activity against the pathogenic yeast Candida albicans” . This research demonstrates that a sustainable, one-pot synthesis can efficiently generate new compounds with valuable biological properties. Sustained-release naproxen derivatives show significant potential for future development in medicinal chemistry. This work highlights the constructive collaboration between green chemistry principles and the discovery of novel therapeutic agents.

Abstract The adsorption of the potentially toxic industrial dyes, Acid Red 18 (AR18), Acid Yellow 23 (AY23), Reactive Yellow 84 (RY84), and Reactive Black 5 (RB5). was assessed in this work using non-activated and activated sunflower seed husks as environmentally sound adsorbents. Hydrochloric, phosphoric and sulfuric acids activated the sunflower seed husk, and the best chemical activation method was determined by surface morphology and the adsorption performance. The optimal temperature for thermal treatment to convert biomass into activated carbon has been established. The structural and chemical characteristics of the adsorbents were investigated by the characterization techniques such as Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray Spectroscopy (EDX), Transmission Electron Microscopy (TEM), and Fourier-Transform Infrared Spectroscopy (FTIR). The percentage removals of Reactive Yellow, Reactive Black 5, Acid Red, and Acid Yellow 23 were determined to be (95.02%, 90.00%, 85.00% and 70.00% for the acid-activated sunflower seed husks. These values were significantly higher than the adsorption capacities of non-activated husks, 80.02%, 75.27%, 55.70%, and 45.00%, respectively, for the corresponding dyes. The findings demonstrate an improvement in the properties of acid-activated sunflower seed husks in the adsorption of dyes (Reactive Black 5 and Acid Red), reflecting the influence of acid activation on expansion of the specific surface area and availability of functional groups. Adsorption efficiency was highest under acidic conditions due to enhanced electrostatic attraction between the adsorbent surface and dye molecules. Reactive dyes showed greater sensitivity to pH variation, with efficiency dropping sharply in alkaline media. In contrast, Acid dyes—particularly Acid Yellow—retained higher performance across the pH range, indicating additional binding mechanisms beyond electrostatic interactions.

Abstract The release of volatile organic compounds (VOCs) during fused deposition modeling additive manufacturing (FDM-AM) has become a critical concern due to its implications for occupational health, indoor air quality, and material performance. In this study, a quantitative chromatographic analysis was conducted to characterize and evaluate VOC emissions from commonly used thermoplastic filaments during FDM-AM. Gas chromatography coupled with mass spectrometry (GC–MS) was employed to separate and identify volatile fractions, while flame ionization detection (FID) provided quantitative assessment of emission concentrations. Representative results revealed the presence of styrene, ethylbenzene, formaldehyde, acetaldehyde, and other low-molecular-weight aldehydes and ketones, with emission profiles varying significantly across polymer types such as ABS, PLA, and PETG. Peak intensities correlated strongly with extrusion temperature, suggesting that process parameters directly influence VOC release. Comparative analysis indicated that ABS exhibited the highest emission intensity, dominated by aromatic hydrocarbons, while PLA produced lower total VOCs but higher proportions of lactide-derived species. The findings underscore the necessity of systematic monitoring of VOCs in FDM-AM environments and provide quantitative evidence for optimizing process conditions and implementing adequate ventilation systems. This work establishes a framework for linking chromatographic signatures of volatile compounds with material choice and processing parameters, contributing to safer and more sustainable additive manufacturing practices.

Abstract Waste-to-energy (WtE) technologies are increasingly important for sustainable waste management and circular economy practices, as they enable recovery of energy from municipal, agricultural, and industrial wastes while reducing landfill use and associated emissions. Despite this relevance, existing research on machine learning (ML) applications in WtE systems remains fragmented, with most studies addressing individual processes, specific algorithms, or isolated performance metrics, and lacking an integrated perspective across the full value chain. The objective of this work is to provide a comprehensive review of machine learning applications in WtE systems, covering resource evaluation, conversion efficiency, and environmental effects within a unified framework. The study is based on a systematic analysis of recent peer-reviewed literature reporting experimental validation or applied modeling in incineration, gasification, pyrolysis, and anaerobic digestion processes. The review indicates that machine learning models successfully capture the nonlinear and time-varying behavior of WtE systems, allowing accurate prediction of waste generation and composition, heating value, biogas yield, process efficiency, and pollutant emissions. Tree-based ensembles and neural networks show strong performance in feedstock assessment and conversion modeling, while data-driven soft sensors and surrogate models support real-time emission prediction and life-cycle impact evaluation. These findings demonstrate that machine learning offers practical benefits for improving operational stability, energy recovery, and environmental compliance in WtE plants, while also highlighting persistent challenges related to data quality, model transferability, and interpretability that should guide future research and deployment.

Abstract Biomass resource mappings are essential tasks for sustainable energy planning, since it offers information on the potential supply, geographical availability of resources, plant sitting, transportation opportunities and roadmap towards long term renewable energy concepts that have policy relevance. Its relevance is increasing as countries are embracing low carbon economy’s roadmaps which demand for reliable spatial quantitative estimations of forest and waste residues – based biomass potentials. Despite the significant headway, there are still some loopholes in applying remote sensing and machine learning techniques. These limitations comprise scarcity of good quality field data for model calibration, poor integration of socio–economic drivers and difficulties in representing fine–scale spatial variability that hinder accurate estimate of yields at different spatial levels. This paper surveys recent machine learning methods for AGB estimation, discusses their methodological limitations, and proposes future research avenues toward scalable and robust forest biomass mapping. A combination of satellite observations, GIS–based layers and ground inventory data sets are included in the analysis as well as a variety of regression, tree based, kernel based, neural network, deep learning and hybrid modelling approaches over various land coverage areas. According to the previous works, evidence is gathered from the surveyed studies that ensemble and deep learning approaches can enhance prediction performance on multi–source data; GIS–machine learning integration contributes to better site selection and logistics analysis. The results also demonstrate the potential for a combined framework that exploits transfer learning approaches and digital twin methodologies to reduce prediction uncertainty, especially in low–data areas. Such information could help support rational decision–making activities for policymakers, planners and industry actors that consider the role of bioenergy in national energy security, climate change mitigation strategies, resource sustainability and long–term renewable energy planning.

Abstract The two laser dyes used were methyl blue and methylene blue, which exhibited favourable optical properties. The linear optical properties were enhanced by incorporating methyl blue or methylene blue dyes into the copper oxide nanoparticles added to polymethyl methacrylate dissolved in Acetone, improving the optical properties important for future applications in laser materials. The prepared samples were examined by a UV-Vis spectrophotometer at wavelengths ranging from 200 to 800 nm. The results showed that the incorporation of methylene blue dye into the (CuO-PMMA) nanocomposite thin films significantly increased some optical properties, including absorbance, absorption coefficient, extinction coefficient, and refractive index. Meanwhile, the transmittance and energy gap decreased with increasing dye concentration. The addition of methyl blue dye had less effect on these properties and yielded values close to those of the nanocomposites that did not contain the dye. The structural properties of the nanocomposites were studied. The structural properties included Field emission scanning electron microscopy (FE-SEM) images and XRD analysis. The SEM images showed the distribution of copper oxide nanoparticles within the PMMA polymer and demonstrated the diffusion of laser dyes within the nanocomposites. Methyl blue dye forms rough, irregular aggregates on the surface, creates darker areas and reduces solubility. In contrast, methylene blue forms a uniform thin layer that covers the nanoparticles and enhances surface contact through hydrogen bonds, which masks the material crystallinity and affects its mechanical and optical properties.

Abstract We present a computational investigation of Gefitinib, a clinically approved EGFR inhibitor, and Quercetin, a bioactive flavonoid with anticancer potential, through an integrated density functional theory (DFT), time-dependent DFT (TD-DFT), and molecular docking framework. Geometry optimization at the B3LYP/6-31G(d,p) level yielded electronic and global reactivity descriptors, providing insights into molecular stability and interaction potential. Quercetin exhibited higher electronic stability, with a HOMO–LUMO gap of 5.22 eV compared to 4.24 eV for Gefitinib. TD-DFT simulations predicted key absorption bands at ~369 nm for Gefitinib and ~310 nm for Quercetin, correlating with their distinct electronic transitions. Docking studies against the EGFR tyrosine kinase domain (PDB: 4HJO) revealed stronger predicted binding for Quercetin (−8.9 kcal/mol) than Gefitinib (−5.5 kcal/mol), supported by hydrogen bonding and π–π stacking interactions. These findings highlight how computational chemistry techniques, widely applied in materials design and process optimization, can also provide multiscale insights into drug–target interactions. By integrating electronic structure analysis with molecular docking, this study demonstrates a transferable approach relevant to applied chemical engineering, bridging quantum chemistry with molecular interaction studies in pharmaceutical and materials science contexts.

Abstract The performance and longevity of polymer filaments in Fused Deposition Modeling Additive Manufacturing (FDM-AM) are highly dependent on their chemical and thermal stability, which can be significantly enhanced by the incorporation of functional additives. This study explores the role of stabilizers, plasticizers, nanofillers, and antioxidant agents in improving the structural integrity and printability of common polymers such as polylactic acid (PLA), polyethylene terephthalate glycol (PETG), and acrylonitrile butadiene styrene (ABS). Analytical chemistry techniques, including Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and gel permeation chromatography (GPC), are reviewed and applied as critical tools to evaluate molecular interactions, degradation kinetics, and additive dispersion within the polymer matrix. Results highlight that additives not only suppress thermo-oxidative degradation and moisture sensitivity but also influence glass transition behavior, crystallinity, and filament rheology, ultimately leading to improved dimensional accuracy and mechanical performance in printed parts. This work provides a comprehensive framework for correlating chemical stability with processing reliability in FDM-AM, offering insights for the development of next-generation, high-performance, and durable polymer filaments tailored for sustainable and industrial applications.

Abstract The creation, delivery, and dissemination of chemical engineering knowledge in a globally interconnected setting are being redefined by the digital transformation of higher education. Massive Open Online Courses (MOOCs) have emerged as important venues for increasing access to engineering education while fostering interdisciplinary cooperation and cross-cultural learning. In order to investigate how MOOCs promote digital transformation and Intercultural Communicative Competence (ICC) in chemical engineering education, this study systematically reviews 24 peer-reviewed articles (2020-2025) using the PRISMA methodology. According to research, MOOCs foster educational innovation by incorporating flipped learning, simulation-based experimentation, and group projects that improve students' technical and intercultural competencies. In order to solve sustainability and process-design issues, they also enable interdisciplinary integration by linking chemical, computational, and environmental disciplines. However, enduring obstacles include unequal access to technology, a lack of established ICC evaluation tools, and poor cultural contextualisation limit wider impact. This review offers a conceptual framework for a Global Chemical Engineering Education Ecosystem based on constructivist, connectivist, and intercultural communication theories. MOOCs are positioned as socio-technical environments that connect digital, interdisciplinary, and intercultural learning processes. The study comes to the conclusion that pedagogical intentionality, institutional backing, and inclusive digital infrastructures that train internationally competent and morally conscious engineers are necessary for MOOCs to have their revolutionary potential.