Chemical Engineering Commons^™

Fluorinated Electrolytes For Lithium–Sulfur And Beyond-Lithium Metal–Sulfur Batteries, Avinash Raulo, Saheed Lateef, Hunter Mcray, Kaushek Rahul Ilancheran, Fabio Albano, Golareh Jalilvand

Faculty Publications

Metal–sulfur batteries, particularly lithium–sulfur (Li–S) systems, have attracted significant attention due to their high theoretical energy densities, low cost, and sustainability benefits arising from sulfur’s abundance and non-toxicity. Despite extensive research, their practical deployment remains limited by persistent challenges such as polysulfide shuttling and metal anode degradation, which collectively lead to poor coulombic efficiency and limited cycle life. These issues are further intensified in emerging systems employing sodium, potassium, magnesium, calcium, and siliconbased anodes. Fluorinated electrolytes have emerged as a promising approach to address these limitations. Fluorination enhances oxidative stability, suppresses polysulfide dissolution, promotes stable solid–electrolyte interphase (SEI) formation, and …

Novel Biomolecule-Infused Gelatin Injectable For Treatment Of Recurrent Laryngeal Nerve Injury, Ananya Tadikonda, Sunjay Anekal, Lena W. Chen, Gabriel Sobczak, Troy Wesson, Carmilya Jackson, Marisa A. Egan, Julie C. Liu, Patrick R. Finnegan, Stacey Halum

School of Chemical Engineering Faculty Publications

Objective

Unilateral vocal fold paralysis (UVFP) due to recurrent laryngeal nerve injury (RLN) is a major cause of voice disorders. We have recently identified three biomolecules (agrin, acetylcholine, and neuregulin) with the potential to promote reinnervation after RLN injury. This study aimed to determine if a gelatin injectable with the reinnervating biomolecules will induce site-specific amplification of neurotrophic factor release and reinnervation after RLN injury in unilateral vocal fold paralysis.

Methods

C57BL/6 mice underwent RLN transection with the following treatment allocations: saline control (N = 16), biomolecule cocktail only (N = 16), and biomolecule-infused gelatin (N = …

Coumarin/Nitrogen-Bearing Heterocyclic Hybridloaded Electrospun Pmma/Pvp Nanofibrous Scaffolds For Accelerating Topical Wound Healing Rates: Synthesis And In Vitro Bio-Evaluation, Samar A. Salim, Mohamed Ali, Tasneem Abed, El Badawy Kamoun

Nanotechnology Research Centre

Coumarin-nitrogen heterocyclic compounds (e.g. quinoline, acridine, and phthalazine) were synthesized by facile reactions and elucidated by FTIR, 1H, and 13C-NMR analyses, which showed consistency with the expected structures. Coumarin-quinoline (drug A)- and coumarin-acridine (drug B)-loaded electrospun PMMA/PVP nanofibrous scaffolds were fabricated using electrospinning techniques. Results showed a successful PMMA/PVP blend, which formed the matrix that was used as the main scaffold for drug loading/release. The drugs (A and B) were encapsulated within the matrix as verified by IR and SEM results. Bio-evaluation through cytotoxicity and anticancer screening was conducted using the MTT assay against lung fibroblast (Wi-38), colon carcinoma (Caco-2), …

Superhydrophobic Cerium-Based Metal–Organic Frameworks/Polymer Nanofibers For Water Treatment, Samar A. Salim, Hani Nasser Abdelhamid,

Nanotechnology Research Centre

No abstract provided.

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Modeling Of Urea Drying And Granulation Process In Fluidized Bed, Jalolitdin Pakhritdinovich Mukhitdinov, Aleksey Viktorovich Schulz

Chemical Technology, Control and Management

This article is devoted to the mathematical modeling of urea drying and granulation processes in a fluidized bed. A brief description is provided for the functional blocks included in the mathematical model, along with the required parameters that form an integrated representation of the technological process. The SR-POLAR model is used to describe a phase equilibrium between the components involved. The interconnections between functional blocks are shown in the process flow diagram. Block diagrams for modeling a multi-chamber granulation unit and the cooling system for the resulting granules are presented. Granule growth in the fluidized bed is described using a …

Increasing The Robustness Of A Control System For A Complex Dynamic Plant By Correcting Nonlinearity In The Warping Process, Tukhtamurod Khayitmurodovich Avezov, Zokhid Ergashboyevich Iskandarov

Chemical Technology, Control and Management

The paper discusses the challenges of enhancing the robustness of a control system for a complex dynamic plant by addressing nonlinearity in the warping process. Devices that ensure the stability of the control system against parameter non-stationarity on the warping machine are referred to as state controllers. The operating principle of these devices relies on providing artificial nonlinearity to the rear connection circuit of the control system's actuator. However, this nonlinearity is implemented using components that consider the parameters of low control quality. Therefore, it is necessary to continuously adjust the nonlinearity parameters to, on one hand, reduce the load …

Optimizing Beer Fermentation Through Intelligent Control, Azizbek Nodirbekovich Yusupbekov, Mirjalol Yusupov

Chemical Technology, Control and Management

This paper presents an intelligent control approach for optimizing the beer fermentation process using fuzzy logic and adaptive neuro-fuzzy inference systems. By incorporating multivariable inputs—temperature error and pH deviation—the proposed system effectively handles the nonlinear dynamics and biological variability inherent in fermentation. Simulation results demonstrate improved control accuracy, responsiveness, and robustness compared to conventional methods, making the approach suitable for integration in modern brewery automation systems.

A C++ Library For Turbulent Mixing Simulation Using Hierarchical Parcel Swapping (Hips), Masoomeh Behrang, Tommy Starick, Heiko Schmidt, David O. Lignell

Faculty Publications

Turbulence models are crucial for simulating flows at all scales, capturing both large-scale structures and small-scale mixing. Software libraries that implement such models should support modular integration, customization, and scalability across different simulation frameworks. This paper presents Hierarchical Parcel Swapping (HiPS), a C++ library documented with Doxygen and available on GitHub. HiPS supports both mixing and reactions and can be used as a standalone model or as a subgrid model in CFD simulations. The code includes examples for users to run it as a standalone model. Additionally, considerations for using it as a subgrid model are provided.

Algorithms For The Synthesis Of Adaptive Control Systems Based On The Speed-Gradient Method, Oxunjon Boborayimov

Chemical Technology, Control and Management

This paper discusses the synthesis algorithms for adaptive control systems based on the speed-gradient method. Adaptive control systems with implicit reference and adjustable models are synthesized using speed-gradient techniques, which reduce the requirements for the main control loop structure and the completeness of measurement data. Stable adaptive decentralized control algorithms are developed for a class of interconnected systems with nonlinear local dynamics and uncertainties, ensuring the stability of individual subsystems and the overall system while accounting for their interactions. To incorporate inter-subsystem interactions into the overall control law, an adaptation algorithm based on the speed-gradient method is introduced. A synthesis …

Modern Methods Of Self-Monitoring, Diagnostics, And Fault Tolerance In Flow Measurement Systems, Elbek Ortiqov

Chemical Technology, Control and Management

This article investigates various methods and tools for self-monitoring and fault tolerance in flow measurement transducers used in industrial processes. The study focuses on key techniques such as the use of redundancy, generation of reference values, analysis of measurement signals, and control of disturbance variables. These methods allow transducers to detect potential faults, ensure reliable operation, and maintain measurement accuracy even under adverse conditions. The article highlights how self-monitoring contributes to improving system safety, increasing reliability, and reducing downtime. It also discusses the integration of intelligent monitoring systems that support predictive maintenance and real-time diagnostics. Fault-tolerant sensors with self-monitoring capabilities …

Finding The Shortest And Optimal Path With Metaheuristic And Madm Methods, Narmin Ibrahim Hasanli

Chemical Technology, Control and Management

The article had described a method for finding the shortest and most optimal path among cities. The data had been taken from the TSPLIB library, which had provided standard examples for the Traveling Salesman Problem. This approach had integrated the advantages of the meta-heuristic technique and the Multi Attribute Decision Making method to solve the problem effectively. In the first stage, the population based meta-heuristic method ACO (Ant Colony Optimization) had found optimal solutions in large search spaces. The use of pheromone trails, heuristic information and an iterative search process had given the opportunity to find the best or near-best …

Research And Development Of Intelligent Measurement Systems, Odil Abdujalilovich Jumaev, Mahmudov Giyosjon Baqoyevich

Chemical Technology, Control and Management

The article discusses modern methods for developing intelligent measuring systems. Intelligent measuring systems are systems based on intelligent technologies that not only accurately measure physical or chemical quantities, but also have the ability to self-analyze, diagnose and make management decisions. The article comprehensively examines the architecture, components of such systems, the organization of their software and hardware, the relationship of sensors and artificial intelligence algorithms. It also analyzes the practical application and prospects of intelligent measuring systems in such areas as industry, medicine, energy, ecology, transport.

Evaluation Of Deep Learning Techniques In Road Sign Recognition, Latafat Abbas Gardashova, Haji Fakhraddin Hajiyev

Chemical Technology, Control and Management

Deep learning has transformed the computer vision field and greatly improved the performance and efficiency of road sign recognition systems. This research compares different deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, in terms of their ability to effectively detect and classify road signs under various conditions. The study compares performance measures such as accuracy, processing speed, and robustness to environmental conditions like low lighting, occlusion, and adverse weather. The results show that CNN-based methods, especially those with transfer learning and ensemble techniques, have better performance in real-time scenarios. Problems like computational …

Application Of Neural Networks For Intelligent Processing Of Sensor Signals In The Control Of Technological Process Parameters, N.R. Yusupbekov, Yu.Sh. Avazov, G.Kh. Rashidov

Chemical Technology, Control and Management

This scientific article investigates the problem of analyzing technological process parameters in the fields of chemistry, energy, and metallurgy based on sensor data and applying intelligent signal processing methods. The main objective is to evaluate the effectiveness of artificial intelligence and deep learning models for intelligent analysis, forecasting, and anomaly detection of data obtained from sensors. Time-series data collected from industrial sensors were analyzed using LSTM (Long Short-Term Memory) and Autoencoder neural networks, as well as the Kalman filter. At the first stage of the study, sensor signals were denoised and their true state was estimated using the Kalman filter. …

Algorithms For Assessing Soil Salinity Levels Based On Remote Sensing Imagery, Bobomurod Mamitjonovich Tojiboev

Chemical Technology, Control and Management

This article investigates methods for assessing soil salinity levels based on satellite (remote sensing) imagery and their calculation algorithms. Determining the degree of salinity plays a crucial role in the rational use of land resources and increasing agricultural efficiency. The study analyzes indices for determining soil salt content using remote sensing technologies, particularly multispectral images obtained from satellite systems such as Landsat and Sentinel (for example, SI - Salinity Index, NDVI - Normalized Difference Vegetation Index, and others). Furthermore, algorithms are developed based on these indices that enable automatic determination of salinity assessments. Artificial intelligence, machine learning, and geographic information …

Synthesis Of A Control System For Thermal Energy Facilities Using The Quantum Photon Spin Method, Isomiddin Siddikov, N.S Yakubova

Chemical Technology, Control and Management

Increasing energy efficiency and reducing fuel consumption in the process of generating electricity and heat at thermal power plants is one of the urgent tasks. Such systems operate under conditions of random changes in external and internal influences, as well as measurement uncertainties, which reduce the quality of control. In order to overcome this problem, it was proposed to develop an intelligent control system using the quantum photon-spin method to control technological units of thermal power plants. In the proposed approach, a multi-dimensional heating boiler device was taken as a control object, and the simulation modeling of the control system …

Magnetic Nanoparticles Tethered With Zn–Dpa For The Removal Of Bacteria From Red Blood Cell Suspension, Tochukwu P. Okonkwo, Rajendra P. Gautam, Jacob B. Limburg, Breckin L. Forstrom, Bowen J. Houser, Aaron Rappleyea, Tyler P. Green, Joseph P. Talley, Alexander D. Daum, Stacey J. Smith, Karine Chesnel, William G. Pitt, Roger G. Harrison

Faculty Publications

Bacterial infections continue to drive the need for more effective and rapid methods for bacterial analysis. To address this, magnetic nanoparticles (MNPs) have emerged as promising tools, especially when their surfaces are modified with bacteria binders. The bis-zinc–dipicolylamine (Zn–DPA) complex is known for its broad affinity to bacteria. We have synthesized MNPs via a thermal decomposition method, encapsulated them in silica, modified their surface with Zn–DPA, and tested their ability to remove bacteria. The MNPs retain their superparamagnetic properties and crystallite structure after being encapsulated. The MNPs coated with silica and Zn–DPA effectively bind and remove both Gram-positive and Gram-negative …

Identification Of Flow Regime In A Co-Current Upflow Packed Bed Reactor (U-Pbr) Using Gamma-Ray Densitometry (Grd), Youssef Yatimi, Ahmed Alalou, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Owing to their significant impact on the reaction rate, the comprehensive understanding of the hydrodynamics in any novel design of the Upflow Packed Bed Reactor (U-PBR) is essential for the optimization of its throughput. In this regard, the identification of the flow regime is of paramount importance. Therefore, in this work, we use the photon counts time-series data obtained using the non-invasive Gamma-ray densitometry (GRD) to identify, for the first time, the flow regimes and their transition velocities at two axial locations (Z/D=0.8,1.56) and five radial locations (r/R=0,±0.5,±0.9) within the packed catalysts' bed of U-PBR with a novel plenum design …

Synergetic Adsorptive Mechanisms, Reusability And Cost Evaluations Of Green Fabricated Hybrid Barium Cobalt Nanocomposites For Methylene Blue And Lead Removal From Wastewater, Titus Chinedu Egbosiuba, Chibuike Christogonus Njoku, Ijeoma Jacinta Ani, Godwin Ifeanyi Ogbuehi, Abdulkareem Omeiza Yusuf, Abiodun Azeez Saka, Ugochukwu Ewuzie, Ebuka Emmanuel Ezennajiego, Blessing Onyinye Okafor, Walter Chibuike Anene, Cecilia Chika Unegbu, Florence Acha, Monday Uchenna Okoronkwo

Chemical and Biochemical Engineering Faculty Research & Creative Works

Barium nanoparticles (BaNPs), cobalt nanoparticles (CoNPs), and hybrid barium-cobalt nanocomposites (BaCoNCs) were synthesized using an eco-friendly green technique with Anacardium occidentale leaf extract for sequestration of methylene blue (MB) dye and lead (Pb (II)) ions from industrial wastewater. The nanoparticles and nanocomposites were characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), high-resolution scanning electron microscopy (HRSEM), and Brunauer-Emmett-Teller (BET) analysis to assess their functional groups, crystalline structure, morphology, and surface properties, confirming successful synthesis. Batch adsorption studies showed that optimal pollutant removal occurred at pH values of 8 for MB and 5 for Pb(II), with a contact time …