Applied Chemical Engineering

Abstract In the modern era, there has been a growing focus among researchers on the transition from fossil fuels to renewable energy sources, particularly photovoltaic (PV) energy, which is gaining popularity worldwide. As the development and installation of PV systems accelerate globally, it is essential to address the various faults and failures these systems may encounter. Consequently, fault diagnosis and evaluation have emerged as critical areas of study aimed at enhancing performance, improving system efficiency, and reducing maintenance costs and repair times. This paper proposes the use of a Random Forest classifier (RF) for diagnosing short circuit and open circuit faults in PV systems. The classifier is trained using machine learning algorithms to accurately identify different fault types based on real measured data from an experimental PV setup. This data encompasses weather conditions such as cell temperature and solar irradiation, as well as system parameters like current and voltage at the maximum power point, alongside performance metrics. The Random Forest classifier serves as a proactive tool for maintenance and fault diagnosis in PV systems, contributing to better overall performance and reliability. Testing on real-world data from a PV system demonstrates that this approach achieves remarkable accuracy in fault diagnosis, with a precision of 100% for current classification and around 97% for voltage classification, all within a few seconds for each parameter.

Abstract The management of marine dredged sediments is a critical environmental and economic issue, particularly in port cities where dredging is a necessary activity to maintain navigability. These sediments are typically viewed as waste products and often require costly and environmentally challenging disposal methods. However, repurposing dredged sediments as a component in concrete production presents a promising solution for both waste management and the creation of sustainable construction materials. Despite this potential, determining the optimal percentage of sediment incorporation and accurately predicting the mechanical properties, such as compressive strength, remain significant challenges. This study proposes an artificial intelligence (AI)-based approach to predict the optimal incorporation percentage of marine dredged sediments from Moroccan ports into concrete and to forecast the resulting compressive strength. A dataset consisting of 104 samples, including dune sand and port sediments from JEBHA, was used. The data includes key properties such as granulometry, cleanliness, fineness modulus, and the compressive strength of the concrete mixtures. These experimental data were employed to train and validate several machine learning models, including linear regression, Random Forest, Gradient Boosting, and XGBoost, chosen for their ability to model complex, non-linear relationships between sediment characteristics and concrete performance. The performance of these models was evaluated using two key metrics: the coefficient of determination (R²) and the root mean square error (RMSE). Among the models tested, the Random Forest Regressor delivered the best results, with an R² value greater than 0.98 and an RMSE of less than 0.20 MPa, indicating highly accurate predictions of both the optimal sediment incorporation rate and the compressive strength of the concrete. This model’s exceptional performance underscores its potential as a reliable tool for optimizing the use of dredged sediments in concrete production. The findings of this study demonstrate the considerable potential of AI in optimizing the incorporation of marine dredged sediments into concrete. By accurately predicting the mechanical properties of the resulting material, this approach enables the development of more sustainable construction practices while reducing the environmental burden associated with sediment disposal. Moreover, this work illustrates the broader applicability of AI in addressing environmental challenges, offering a pathway to valorize waste materials in the construction industry. The study not only advances our understanding of sediment utilization in concrete but also contributes to the growing field of sustainable material science, offering promising avenues for future research and development. Nevertheless, further research is needed to validate the model’s scalability to other sediment types and assess practical limitations in industrial applications.

Abstract Fused Deposition Modeling (FDM)-based rapid prototyping is a key technology in sustainable manufacturing, offering cost-effective solutions aligned with the United Nations Sustainable Development Goals (SDGs 1–6) by promoting affordable production, resource efficiency, and environmental sustainability. However, optimizing mechanical performance and energy efficiency in bio-based thermoplastic composites remains a challenge. This study explores PLA–walnut wood fiber composites, leveraging machine learning (ML) to optimize tensile, compression, and flexural properties while minimizing energy consumption. A dataset incorporating nozzle temperature, layer height, infill density, and print speed was trained using ML, achieving prediction accuracy above 95%. State-of-the-art studies highlight bio-based composite advantages, yet ML-driven multi-objective optimization for mechanical strength and sustainability remains unexplored. Experimental results indicate that an optimal nozzle temperature of 200–210°C, an infill density of 60–80%, and a layer height of 0.2 mm led to a 15% increase in tensile strength (38 MPa), a 12% improvement in flexural strength (62 MPa), and a 10% enhancement in compression strength (49 MPa). SEM analysis confirms improved fiber-matrix adhesion, enhancing structural integrity. Additionally, energy consumption was reduced by 18%, supporting cost-effective and low-carbon production. These findings contribute to poverty reduction (SDG 1), agricultural waste valorization (SDG 2), biocompatible materials for healthcare (SDG 3), STEM education accessibility (SDG 4), gender inclusivity in engineering (SDG 5), and clean water protection through reduced plastic waste (SDG 6). This study underscores the potential of ML-driven sustainable rapid prototyping to advance material efficiency, waste reduction, and resource-conscious manufacturing.

Abstract The aim of this article is to develop a "switchable hydrophilicity solvent liquid phase microextraction" (SHS-LPME) for the effectual extraction of fast green FCF. Three "switchable hydrophilicity solvents" (SHSs) were practiced for the extraction of fast green FCF. The attained extract phase afterward phase separation was evaluated by UV-VIS spectrophotometry. The extraction parameters such as, SHS volume, HNO3 volume and NaOH volume, were enhanced using central composite design and desirability function. Under optimized conditions, the linear range 0.50-5.00 µg/ mL with R2 = 0.9886, limit of detection 0.341 µg/ mL, limit of quantitation 1.026 µg/ mL. The method showed a relative standard deviation (RSD) of 1.16% for 7 replicate measurements. Preconcentration and Enrichment factors were determined to be 20 and 35 respectively, indicating the method’s efficiency in enhancing fast green detection. The proposed method was applied in real samples successfully.

Abstract The central nervous system (CNS) is one of the primary targets of alcohol-induced damage. Chronic alcohol consumption leads to cognitive deficits, motor impairments, anxiety-like behaviors, and even irreversible neuronal degeneration and death. However, therapeutic strategies for alcohol-related neurotoxicity remain limited, posing a significant public health concern. Ursolic acid (UA), with its antioxidant, anticancer, anti-inflammatory, hepatoprotective, and immunomodulatory properties, may confer protective effects against neurological damage. In this study, we established a zebrafish model of alcohol-induced neurotoxicity and investigated the potential of UA to mitigate neural injury. Using confocal live imaging in transgenic zebrafish lines, we observed that UA significantly alleviated alcohol-induced reductions in neuronal and dopaminergic neuron populations. Behavioral assays further demonstrated that UA restored normal locomotor activity in zebrafish embryos, indicating functional recovery of the nervous system. Transcriptomic sequencing revealed that UA ameliorated alcohol-induced neurotoxicity potentially by modulating the MAPK signaling pathway and promoting extracellular matrix (ECM) remodeling. This study provides experimental evidence for UA as a therapeutic candidate against alcohol-related neural damage and identifies potential molecular targets for clinical interventions.

Abstract Borophosphate glasses with compositions xNa 2 O-(45-x) B 2 O 3 -45P 2 O 5 -10MnO, where x ranges from 5 to 25 mol%, have been prepared using the conventional melt quenching technique. Several methods including X-ray diffraction, Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC) have been used to characterize the produced materials. The absence of crystalline structure in the prepared phosphate glasses was confirmed by X-ray diffraction (XRD) studies. The chemical resistance of these glasses increases with the Na 2 O content. Glasses containing more than 15 mol% Na 2 O have excellent chemical resistance. The relationship between structural changes and composition was investigated by measuring density and glass transition temperature Tg. The results obtained show that the glass transition temperature and chemical properties increase with increasing sodium oxide composition in all the glasses studied. These experimental results indicate that Na 2 O lowers the melting point and increases the strength of the glasses.

Abstract The increase in global energy consumption is paralleled by an increase in the waste generated from it, especially those related to used batteries which constitute a source of contamination of the environment and a great attaint for the human health. Therefore, it has become more necessary to work on recycling batteries to revalue their active materials and to preserve the environment. The aim of this work was the synthesis and characterization of nanostructured PbO obtained from spent lead acid batteries negative plate. The negative plates of used battery are made up of large amounts of PbSO4 and smaller amounts of Pb. The PbSO 4  was desulfated with (NH 4 ) 2 CO 3  to obtain PbCO 3  which is then calcined in air at different temperatures.in this work we are interested in studying the effect of temperature on the nature and the morphology of the products of the calcination process. The results show that at a 450°c we obtain α-PbO, at 500°c β-PbO, after these temperature we get a mixture of lead oxides α-PbO, β-PbO, and minium Pb 3 O 4 . α-PbO granules have sizes around 26 nm with a mesoporous materials and BET surface area was equal to about 4 m 2 /g.

Abstract A highly sensitive approach for separating and determining the micro amount of nickel (II) was conducted. It has been achieved after the formation of chelation complexes with 4-((4-hydroxyquinolin-3-yl) diazenyl) benzenesulfonamide (HQDBS) and 3-((1H-indol-5-yl)diazenyl)quinolin-4-ol (IDQ) as complexing agents (which are examined by using UV-Vis., FT-IR, and 1 HNMR spectrum), including joint cloud point extraction with liquid ion exchange methods in the presence of the nonionic surfactant Triton X–100. The study is based on the wavelength values of maximum absorbance, λ max  = 480 and 484 nm, respectively. The study optimized the extraction conditions, including the reagent concentration, temperature, heating duration, and surfactant volume. The concentration of reagents for achieving higher extraction efficiency is 1×10 -3  M in the presence of 100 µg Ni (II)/mL of aqueous solution. The solutions should be heated at 80°C and 90°C HQDBS and IDQ respectively, for 15 minutes. The optimal volume of surfactants is 0.8 mL of Triton X-100 with HQDBS and 0.5 mL with IDQ. The study also includes an analysis of the impact of electrolytes and interferences and the spectrophotometric identification of Ni (II) in various samples.  

Abstract Coumarins, a class of benzopyrone derivatives predominantly found in plants, have garnered extensive scientific interest for their broad-spectrum biological activities and promising applications across pharmaceutical, agricultural, and cosmetic sectors. Their historical use in traditional medicine, combined with modern evidence supporting their therapeutic potential, positions coumarins as valuable natural scaffolds for drug development and sustainability-driven innovation. This review explores the natural diversity, biosynthesis, biological activities, and sustainable development strategies associated with coumarins. Emphasis is placed on their role in modern pharmacology, the advances in synthetic biology, and their applications within the context of environmental conservation and green chemistry. A comprehensive analysis was conducted using peer-reviewed literature obtained from major databases including PubMed, Scopus, and Web of Science. Key topics include coumarin biosynthesis, plant and microbial sources, traditional and modern applications, and sustainability practices related to their extraction and commercialization. Coumarins demonstrate potent antimicrobial, antioxidant, anti-inflammatory, and anticancer properties, many of which are linked to structural variations in their core scaffold. Advances in metabolic engineering and synthetic biology have enabled scalable production and derivatization. Coumarin-based compounds are increasingly being applied in skincare formulations, eco-friendly agrochemicals, and as templates in drug discovery. Ethical sourcing, conservation strategies, and regulatory frameworks play critical roles in ensuring sustainable utilization. Coumarins exemplify the convergence of natural product chemistry and sustainable innovation. Their structural diversity, bioactivity, and multifaceted applications underscore their importance in both traditional and modern contexts. Future research should focus on biosynthetic optimization, novel therapeutic targeting, and integration into circular bioeconomy frameworks to maximize their scientific and societal impact.

Abstract Heterocyclic compounds, characterized by rings containing non-carbon atoms like nitrogen, oxygen, or sulfur, are fundamental in diverse fields. This review provides an overview of heterocyclic chemistry, with a focused examination of benzimidazoles. It covers their structure, chemical properties, synthetic methods (including classical and modern techniques emphasizing efficiency and sustainability), and broad therapeutic applications across various disease areas, highlighting their significance in drug development and materials science. From (2018-2024)

Abstract Coumarins, a diverse class of benzopyrone derivatives, have long captivated researchers due to their broad spectrum of pharmacological activities and synthetic versatility. In recent years, the convergence of artificial intelligence (AI) with pharmaceutical sciences has redefined how researchers approach the synthesis, molecular docking, and pharmacological profiling of such bioactive compounds. This review explores the transformative potential of AI in the context of coumarin research, presenting a holistic view of how machine learning algorithms, deep learning models, and data-driven design strategies are reshaping drug discovery. Traditional synthesis of coumarins, often constrained by multistep protocols and environmental concerns, is now being revolutionized through AI-assisted reaction predictions and retrosynthetic analyses. AI enables the generation of synthetically accessible molecules with optimized structural features, significantly reducing time and resource investment. Furthermore, molecular docking, critical to understanding structure-activity relationships, is increasingly benefiting from AI-enhanced scoring functions and predictive modeling, thus improving the accuracy of ligand-receptor interaction predictions. Pharmacological profiling, both in vitro and in vivo, is becoming more streamlined with AI models capable of predicting bioactivity, toxicity, and pharmacokinetics, making the lead optimization process more efficient and reliable. Public databases, curated datasets, and integrative cheminformatics platforms now provide a rich foundation for data mining and drug-target interaction studies. This review not only highlights the successes of AI in coumarin-based drug design but also discusses existing challenges, including algorithm interpretability, data quality, and regulatory considerations. Ultimately, the synergy between AI and coumarin research presents an exciting frontier that holds immense promise for accelerating drug discovery and advancing personalized therapeutics.