Applied Chemical Engineering

Abstract Predicting the thermal performance of buildings is a key research area in the context of improving energy efficiency and reducing environmental impacts. Several approaches have been developed to model and predict thermal performance. Among these approaches, machine learning techniques are distinguished by their ability to exploit large amounts of data and model complex systems, but their effectiveness remains to be demonstrated in different contexts. This work therefore explores the application of hybrid machine learning models. Six different models, including ANN-LR, ANN-RR, ANN-RF, ANN-GB, ANN-DT, and ANN-ELM were evaluated and compared to the standalone model (ANN) based on the statistical metrics. Using the TRNSYS tool, the dynamic simulation of a building enhanced with PCM into the roof was performed to generate the data. The findings proved the effectiveness of the hybrid machine learning techniques, with ANN-LR and ANN-GB emerging as the most reliable hybrid approaches for accurate prediction, showcasing their robustness and suitability for complex prediction tasks, while ANN-RR model proved to be the least effective. Furthermore, the performance of the models varied considerably depending on the target, with total energy consumption appearing more complex and challenging for prediction.

Abstract With the increasing demand for automation and precision in chemical engineering processes, robotic arms play a crucial role in enhancing production efficiency and product quality. Traditional control methods often struggle to cope with the complex dynamic environments and unpredictable disturbances inherent in chemical engineering applications. This study presents an improved Nonlinear Active Disturbance Rejection Control (NLADRC) method for dynamic trajectory tracking of chemical engineering robotic arms. Leveraging the support of the Yunnan Province Major Science and Technology Project, the proposed NLADRC framework integrates an enhanced disturbance observer and adaptive control strategies to effectively mitigate unknown disturbances and parameter variations. Experimental results demonstrate that the NLADRC method significantly outperforms traditional PID and standard ADRC controllers in terms of tracking accuracy, response speed, and robustness. The findings provide a robust theoretical foundation and practical guidelines for the deployment of advanced control strategies in chemical engineering robotic systems.

Abstract As competition for water demand increases in all life sections, the agricultural sector has observed a gradual decrease in water consumption. In order to sustain or enhance agricultural productivity, innovative irrigation methods, like surface and subsurface drip irrigation systems, enhance the efficiency of water utilization compared to conventional systems. Multiple models have been established to forecast the dimensions of moisture distribution, which have significance for constructing an efficient drip irrigation system. This study evaluates the performance of surface and subsurface drip irrigation systems using the HYDRUS-2D model to predict soil moisture distribution under varying conditions of time, emitter spacing, and emitter depth. The results indicate a high level of agreement between simulated and observed moisture distributions, demonstrating the reliability of HYDRUS-2D as a predictive tool for modeling soil water dynamics. The study demonstrates the effectiveness of HYDRUS-2D in simulating soil moisture distribution for surface and subsurface drip irrigation systems under varying conditions of time, emitter spacing, and depth. Subsurface irrigation at 20 cm depth showed the highest simulation accuracy, with RMSE as low as 0.008798 and R² up to 0.9839, particularly at shorter intervals. Closer emitter spacing (20 cm) provided more uniform moisture distribution, while increased spacing (40 cm) led to less consistent patterns. Emitters placed at 20 cm depth achieved the optimal balance between precision and efficiency by minimizing evaporation and effectively targeting the root zone. These findings underline the utility of HYDRUS-2D as a reliable tool for optimizing drip irrigation design, improving water-use efficiency, and supporting sustainable agricultural practices in water-scarce regions.

Abstract The materials of this article are dedicated to studying the relationships between the structural and mechanical properties of porous polymeric materials for their control. The research revealed that as the pore diameter of the polymeric material decreases, the Young's modulus decreases, while the yield strength increases. With an increase in the thickness of cell walls, the Young's modulus decreases, and the yield strength increases. A higher Young's modulus was found in samples with lower density, while the highest yield strength was observed in samples with the highest density.

Abstract Noise negatively impacts human health and the environment, making it a pressing issue in the context of industrial development and urbanization. The objectives of this research include the production of durable panels from waste materials with gypsum as the binder, the study of their acoustic properties and sound absorption coefficient compared to traditional materials, as well as the analysis of mechanical properties and resistance to compressive forces. To conduct acoustic and mechanical studies, 36 special samples in the form of panels were manufactured from a mixture of gypsum with rubber and cork waste in various combinations, with the addition of polymer material and maintaining a specific water-to-powder ratio. Acoustic properties were determined using a device operating on the principle of "transmitter-receiver," while mechanical properties were assessed through compression testing. The results showed that all samples containing rubber and cork waste had a sound absorption coefficient higher than 0.35, which increased with frequency and decreased with increasing material density. An increase in the proportion of waste contributed to greater porosity and, consequently, improved sound absorption. Mechanical testing of the samples under compression demonstrated that their failure limit was reached at loads of up to 15 kN. Comparison with benchmark studies confirmed the effectiveness of utilizing recycled materials.

Abstract In this study, 2- [2-(5-Chloro carboxy phenyl) azo] 1-methyl imidazole (1-MeCPAI) was synthesized and used in developing hydrogels via free radical polymerization with acrylic acid (AA) and N, N-methylene bisacrylamide. The synthesized P(AA-co-1-MeCPAI) hydrogels were evaluated for their effectiveness in adsorbing R6G dye from aqueous solutions. The characterization of these hydrogels included techniques as Fourier Transform Infrared Spectroscopy (FTIR), Thermal Gravimetric Analysis (TGA), BET (Brunauer-Emmett-Teller) and BJH (Barrett-Joyner-Halenda) analysis, X-ray diffraction (XRD), and Field Emission Scanning Electron Microscopy (FESEM). The study revealed that adsorption efficiency is influenced by pH, temperature, contact time, and adsorbent dose, with adsorption following a pseudo-second-order kinetic model and best fitting the Temkin isotherm, indicating a multilayer adsorption process. Thermodynamic analysis confirmed that the process is exothermic and spontaneous, underscoring the potential of P(AA-co-1-MeCPAI) hydrogels as effective adsorbents for dye removal.

Abstract Switchgrass is a highly promising bioenergy feedstock due to high biomass yield and ability to thrive on marginal lands. Enhancing switchgrass for biofuel production through data-driven breeding and selection is essential to meeting the growing need for sustainable and renewable energy sources. This review critically analyses current approaches and future directions in identifying key phenotypic traits, exploring genetic diversity, and developing predictive models to improve switchgrass. It underscores the importance of high-throughput phenotyping technologies and standardized protocols in pinpointing traits that enhance biofuel yield and conversion efficiency. The review discusses the necessity of comprehensive genotyping and sequencing to understand genetic diversity better and utilize beneficial traits in breeding programs. Moreover, the study highlights the potential of advanced machine learning algorithms and multi-dimensional data integration in creating strong predictive models for breeding decisions. This review provides a roadmap for future research and practical breeding strategies to optimize switchgrass as a bioenergy feedstock.

Abstract Coumarin-derived compounds have garnered extensive interest due to their diverse applications in medicinal chemistry, pharmacology, food, and cosmetics. There are pros and cons to both in vitro and in silico methods used in coumarin-based research. The focus is on their roles, pros, and cons in finding out the biomedical properties of these compounds. In vitro studies, conducted in controlled environments, enable detailed investigations into cellular mechanisms, enzyme interactions, and cytotoxic effects. These studies are valuable for elucidating coumarin’s biological activity and therapeutic potential. Although these studies are accurate, morally acceptable, and repeatable, their inability to fully replicate complex biological systems necessitates extrapolation to real-life situations. In contrast, in silico studies leverage computational tools to model molecular interactions, predict pharmacokinetic behaviors, and simulate biological pathways. These techniques are time- and cost-efficient, capable of high-throughput screening, and useful for hypothesis generation. However, their reliability depends on the accuracy of input data and assumptions, which can limit their predictive power in real-world scenarios. Integrating these two study types provides a synergistic framework that enhances coumarin-based research. In silico models can guide the design of experiments, while in vitro assays validate computational predictions. Emerging technologies, such as machine learning, organ-on-a-chip systems, and 3D cell cultures, promise to further refine this integration, enabling faster, more accurate, and ethical research. We conducted an investigation and a literature review, utilizing PubMed data and limiting the publication period from 2000 to mid-2024. This study demonstrates the effective combination of in vitro and in silico methods to advance coumarin-based research and unlock its full therapeutic potential.

Abstract The greater development in the computational tools and the presence of a huge number of established chemical libraries emerge the inquiry about the ability to integrate the computational techniques in the drug discovery process. This review emphasizes the role of computational tools and software to accelerate the steps in the drug discovery pipeline. The data were gathered from the trendiest research articles and reviews that were indexed in the well-established scholarly search engines. Different vital techniques, such as virtual screening, pharmacophore modeling, molecular docking, molecular dynamic simulations, and quantitative structure activity relationship, are widely employed for target recognition, lead refining, and forecasting binding behavior at the atomic level. The advancement in artificial cognitive computing software significantly expanded the capabilities for rapid analysis of substantial data sets and the generation of toxicity and bioactivity predictive models of drugs. The contributions of these software tools tackled global problems, like drug repurposing during the coronavirus outbreak, and the direction toward personalized medicaments highlight their crucial role in the swift discovery of new treatments. In addition, computational tools are widely employed to enhance the drug formulations, which leads to discovering drugs with optimum pharmacokinetic and toxicity profiles. The difficulties in analyzing the multi-target binding behaviors and the limited success rates of the generated candidates in the experimental validations were the main existing limitations. However, the steep development in the field of artificial intelligence and the hybrid biochemical-computational approaches provided promising horizons to tackle these limitations. Chemical computational tools significantly affect the future of pharmaceutical research by boosting the drug discovery process toward affordable and efficient medicaments.

Abstract Coumarin and its derivatives are intriguing to researchers in both chemical and pharmaceutical fields because they have a unique benzopyrone scaffold and a lot of bioactive properties. This review delves into the significance of the coumarin scaffold in the design and engineering of bioactive molecules, offering insights into its chemical, biological, and pharmacological roles. Coumarins are praised for their many medical uses, such as their ability to fight cancer, reduce inflammation, kill microbes, stop blood clots, and act as an antioxidant. This is possible because they have a special chemical structure with α,β-unsaturated α-lactones and electron-rich aromatic rings. The flexibility of this scaffold is amazing; it can be changed chemically in many ways, which lets different derivatives with different biological activities be made. The historical significance of coumarins is underpinned by their natural occurrence in various plants and their pivotal role in therapeutic applications since the 19th century. Their synthetic versatility has led to advancements in drug development, particularly in creating anticoagulants, antivirals, and neuroprotective agents. In addition, coumarins have been shown to work well in cosmetic formulations, cardiovascular health, and diabetes treatments, showing that they can be used for many things. The efficient synthesis, purification, and functionalization of coumarins still face problems. This shows the need for new methods to get around problems like harsh reaction conditions and high costs. New computer techniques, such as 3D-QSAR and pharmacophore modeling, have made it easier and faster to study compounds that are built on a coumarin scaffold. These techniques have also made these compounds more therapeutically useful. This narrative review underscores the coumarin scaffold's prominence in medicinal chemistry and its future prospects as a platform for developing novel bioactive molecules. Coumarins are important for advancing science and health because they can change chemical forms easily and have a wide range of biological effects.

Abstract This research explored the creation and application of a composite material, SA-g-poly (AAc-IA)/bentonite, for removing Norfloxacin (NOR) from water. Synthesized through copolymerization, the composite's properties were characterized using FTIR, TGA, FESEM, and AFM. FTIR analysis confirmed essential functional groups, with shifts observed in the 1500–1750 cm⁻¹ range post-NOR adsorption. FESEM images showed a porous surface, with particle sizes between 50–200 nm, that became less porous after adsorption. TGA analysis indicated thermal stability up to 300°C, followed by significant decomposition between 300–550°C due to organic component breakdown. Adsorption studies revealed equilibrium within 120 minutes, with a maximum adsorption capacity of 9.99 mg/g. Thermodynamic analysis confirmed a spontaneous and endothermic process, with ΔH of +23.76 kJ/mol. Kinetic modeling showed a pseudo-second-order mechanism, indicating chemisorption. These findings suggest the SA-g-poly (AAc-IA)/bentonite composite is a potentially efficient and cost-effective adsorbent for pharmaceutical wastewater treatment.