Chemical Engineering Commons^™

Factors Influencing The Effectiveness Of Machine Learning Algorithms In Predicting Raw Wastewater Characteristics: An Analysis Based On Weekly Field Data, Hoda Khoshvaght, Ratish Ramyad Permala, Amir Razmjou, Mehdi Khiadani

Research outputs 2022 to 2026

Accurate prediction of raw wastewater characteristics can reduce laboratory costs, enable optimized chemical dosing, and improve the overall efficiency of wastewater treatment plant (WWTP) operations. This study evaluates machine learning (ML) models as a fast, sustainable, and cost-effective alternative for predicting Biochemical Oxygen Demand (BOD) and Ammonia Nitrogen (NH4⁺-N) in raw wastewater. Seven ML algorithms, including tree-based, neural, and regression approaches, were trained using standard wastewater indicators, including organic load, nutrient levels, and pH, with hyperparameters tuned via grid search cross-validation. Model performance was evaluated using correlation-based (R²) and error-based metrics (RMSE, MAE, MAPE) to ensure …

Soft Sensing Of Biological Oxygen Demand In Industrial Wastewater Using Machine Learning Models, Muhammad Hassnain, Sarada M.W. Lee, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

Traditional methods for determining biological oxygen demand (BOD) from industrial water resource recovery facilities (WRRFs) are time-consuming and often impractical for real-time process control. This study explores the application of machine learning (ML) and artificial intelligence (AI) models for the prediction of final effluent BOD (F-BOD) based on physicochemical and operational parameters by leveraging nineteen years of historical laboratory and instrumentation data from the WRRF of an essential oil manufacturing plant. The predictions from these models are then used to simulate the process dynamics, assessing the optimal operational boundary conditions for all input parameters at which the target (F-BOD) falls …

Towards Accurate First-Principles Modeling In Heterogenous Catalysis: Ethane Dehydrogenation And Hydrogenolysis Over Pt Catalysts, Mubarak Ayo Bello

Theses and Dissertations

First-principles modeling has become central to heterogeneous catalysis research, offering mechanistic insight and guiding catalyst design, yet conventional approaches often struggle to accurately describe complex catalytic systems due to methodological uncertainties, simplified assumptions, and the structural diversity of nanoparticle catalysts. This dissertation addresses these challenges using ethane dehydrogenation (EDH) and hydrogenolysis (EH) over platinum catalysts as model systems. The first study benchmarks density functional theory (DFT) functionals against the random phase approximation (RPA) for the EDH network on Pt(111), identifying cost-effective functionals and demonstrating the efficiency of BEEF-vdW ensembles for capturing functional uncertainty. The second study (addressed in prior work) …

Performance Limiting Factors For Silicon Anodes In Lithium-Ion Batteries, Najmaddin Bashirzada

Theses and Dissertations

The rising global demand for high-performance and sustainable energy storage solutions has placed lithium-ion batteries (LIBs) at the forefront of technological progress. However, the energy density limits of traditional graphite anodes require the investigation of alternative materials. Silicon (Si), with its high theoretical capacity, is a promising anode material. Still, its practical use faces major challenges such as volumetric expansion, unstable solid electrolyte interphase (SEI) formation, and low conductivity. This thesis examines the electrochemical behavior and limitations of Si-based electrodes using coin cells and ex-situ three-electrode cells. A comparison of electrochemical testing methods – i.e., Galvanostatic Intermittent Titration Technique (GITT) …

Towards Accurate First-Principles Modeling In Heterogenous Catalysis: Ethane Dehydrogenation And Hydrogenolysis Over Pt Catalysts, Mubarak Ayo Bello

Theses and Dissertations

First-principles modeling has become central to heterogeneous catalysis research, offering mechanistic insight and guiding catalyst design, yet conventional approaches often struggle to accurately describe complex catalytic systems due to methodological uncertainties, simplified assumptions, and the structural diversity of nanoparticle catalysts. This dissertation addresses these challenges using ethane dehydrogenation (EDH) and hydrogenolysis (EH) over platinum catalysts as model systems. The first study benchmarks density functional theory (DFT) functionals against the random phase approximation (RPA) for the EDH network on Pt(111), identifying cost-effective functionals and demonstrating the efficiency of BEEF-vdW ensembles for capturing functional uncertainty. The second study (addressed in prior work) …

Bridging Materials And Energy Storage Mechanisms In Zn-I2 Batteries, Rong-Qi Liu, Wen-Shuo Shang, Jin-Tao Zhang

Journal of Electrochemistry

Zinc-iodine (Zn-I₂) batteries have emerged as a compelling candidate for large-scale energy storage, driven by the growing demand for safe, cost-effective, and sustainable alternatives to conventional systems. Benefiting from the inherent advantages of aqueous electrolytes and zinc metal anodes, including high ionic conductivity, low flammability, natural abundance, and high volumetric capacity, Zn-I₂ batteries offer significant potential for grid-level deployment. This review provides a comprehensive overview of recent progress in three critical domains: positive-electrode engineering, zinc anode stabilization, and in situ characterization methods. On the cathode side, anchoring iodine to conductive matrices effectively mitigates polyiodide shuttling and enhances …

Series Reports From Professor Wei’S Group Of Chongqing University: Advancements In Electrochemical Energy Conversions (2/4): Report 2: High-Performance Water Splitting Electrocatalysts, Ling Zhang, Wang-Yang Wu, Qiu-Yue Hu, Shi-Dan Yang, Li Li, Rui-Jin Liao, Zi-Dong Wei

Journal of Electrochemistry

The unavailability of high-performance and cost-effective electrocatalysts has impeded the large-scale deployment of alkaline water electrolyzers. Professor Zidong Wei’s group has focused on resolving critical challenges in industrial alkaline electrolysis, particularly elucidating hydrogen and oxygen evolution reaction (HER/OER) mechanisms while addressing the persistent activity-stability trade-off. This review summarizes their decade-long progress in developing advanced electrodes, analyzing the origins of sluggish alkaline HER kinetics and OER stability limitations. Professor Wei proposes a unifying “12345 Principle” as an optimization framework. For HER electrocatalysts, they have identified that metal/metal oxide interfaces create synergistic “chimney effect” and “local electric field enhancement effect”, enhancing selective …

Local Electric Fields Coupled With Cl− Fixation Strategy For Improving Seawater Oxygen Reduction Reaction Performance, Yu-Rong Liu, Miao Zhang, Yan-Hui Yu, Ya-Lin Liu, Jing Li, Xiao-Dong Shi, Zhen-Ye Kang, Dao-Xiong Wu, Peng Rao, Ying Liang, Xin-Long Tian

Journal of Electrochemistry

Development of robust electrocatalyst for oxygen reduction reaction (ORR) in a seawater electrolyte is the key to realize seawater electrolyte-based zinc-air batteries (SZABs). Herein, constructing a local electric field coupled with chloride ions (Cl⁻) fixation strategy in dual single-atom catalysts (DSACs) was proposed, and the resultant catalyst delivered considerable ORR performance in a seawater electrolyte, with a high half-wave potential (E_1/2) of 0.868 V and a good maximum power density (Pmax) of 182 mW·cm⁻² in the assembled SZABs, much higher than those of the Pt/C catalyst (E_1/2: 0.846 V; Pmax: 150 mW·cm^{−2 …}

Significantly Enhanced Oxygen Reduction Reaction Activity In Co-N-C Catalysts Through Synergistic Boron Doping, Chang Lan, Jing-Sen Bai, Xin Guan, Shuo Wang, Nan-Shu Zhang, Yu-Qing Cheng, Jin-Jing Tao, Yu-Yi Chu, Mei-Ling Xiao, Chang-Peng Liu, Wei Xing

Journal of Electrochemistry

The weak adsorption energy of oxygen-containing intermediates on Co center leads to a considerable performance disparity between Co-N-C and costly Pt benchmark in catalyzing oxygen reduction reaction (ORR). In this work, we strategically engineer the active site structure of Co-N-C via B substitution, which is accomplished by the pyrolysis of ammonium borate. During this process, the in-situ generated NH₃ gas plays a critical role in creating surface defects and boron atoms substituting nitrogen atoms in the carbon structure. The well-designed CoB₁N₃ active site endows Co with higher charge density and stronger adsorption energy toward oxygen species, …

The Effect Of Ozone Treatment On The Quality Of Biodiesel From Waste Cooking Oil, Sri Redjeki Setyawati, Singgih Wibowo

Makara Journal of Science

This research demonstrates the successful production of biodiesel from waste cooking oil (WCO) using the transesterification method. A methoxide solution, formed by employing a base catalyst and alcohol, facilitated the conversion of triglycerides into methyl esters and glycerol as the byproduct. The conversion process yielded 74% biodiesel and 13.5% glycerol content, with the remaining proportion consisting of water and contaminants. The application of ozone (O₃) aeration proved effective in reducing contaminants—such as alcohol, unreacted catalysts, food residues and water—by approximately 60%. Furthermore, Fourier transform infrared (FTIR) testing affirmed that the biodiesel product—treated with O₃ aeration—maintained its original …

Effect Of Varying Chitosan Molecular Weights On The Morphology And Physical Characteristics Of Pva/Chitosan Composite Hydrogels, Yuan Alfinsyah Sihombing, Halida Rahmi Luthfianti, William Xaveriano Waresindo, Marathur Rodhiyah, Nur’Aini Nafisah, Dian Ahmad Hapidin, Dhewa Edikresnha, Khairurrijal Khairurrijal

Makara Journal of Science

We employed the innovative freeze–thaw method to investigate the effect of chitosan (CS) molecular weights incorporated into polyvinyl alcohol on the morphology and physical characteristics of the hydrogel. This study aimed to unravel the intricate relationships between CS molecular weight variations, CS concentration, and the number of freeze–thaw cycles (4 or 6 cycles) on hydrogel swelling degree and weight loss. Incorporating CS improved the hydrogel structure and enhanced its porosity, resulting in remarkable increases in swelling degree ranging from 2 to 6 times the initial weight. Higher molecular weight CS (310,000–375,000 Da) exhibited a lower swelling degree than medium molecular …

Nonlinear Analytical Models Of The Seismically Isolated ``Frame-Boiler'' System With Cantilever-Type Hysteresis Dampers, Aleksandr Mikhailovich Anushchenko, Dmitrii Evgenievich Bondarev, Aleksandr Yurievich Schukin

Journal of Sustainable Construction Materials and Technologies

This study shows results of development of the ``frame-boiler'' system with hysteresis dampers analytical model. Hysteresis dampers are used as boiler antiseismic bracing. It is noted that simplified analytical models are necessary at the stage of preliminary iterative calculations, when a CAE-modeling is impractical due to significant time costs. The authors have proposed 2 options of analytical models: 2-mass model and 3-mass model. The differential equations of motion for each model are written below. The results of the calculation for seismic impacts are compared with the finite element model. It is shown that 3-mass model gives good match of results …

Advancing Sustainable Structural Solutions: Hemp Fiber-Reinforced Polymer (Hfrp) Reinforcement Bars For Concrete Applications Through Material Selection, Simulation, And Pathways For Future Development, Thomas Cadenazzi, Busra Keles

Journal of Sustainable Construction Materials and Technologies

This paper investigates innovative Hemp Fiber-Reinforced Polymer (HFRP) rebars as a sustainable alternative to conventional Carbon Steel (CS) and Glass Fiber-Reinforced Polymer (GFRP) rebars used as reinforcement in concrete structures. The feasibility of HFRP rebars is assessed, focusing on mechanical properties and sustainability advantages. A virtual tensile test on a modeled M19 HFRP rebar, conducted using Autodesk's Fusion 360, demonstrates tensile strength comparable to CS 235 and greater environmental sustainability than GFRP, despite a slight reduction in stiffness. The study also considers the potential of graphene fiber integration to enhance mechanical properties, underscoring HFRP's adaptability. Findings suggest that HFRP rebars, …

Effect Of Clays On The Spontaneous Imbibition Behavior Of Surfactant Solutions In Shale Oil Reservoirs, Ning Xu, Yanling Wang, Baojun Bai, Chuanbao Zhang, Shizhang Cui, Yu Zhang, Di Li, Wenjing Shi, Wenhui Ding

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Surfactant-enhanced spontaneous imbibition is a proven method of enhancing oil recovery from shale reservoirs. However, a significant knowledge gap concerning the impact of clay minerals on surfactant-enhanced imbibition in shale reservoirs remains. Therefore, this study first analyzed the mineral composition and pore structure of the shale reservoirs. Subsequently, three surfactants were selected for spontaneous imbibition experiments. The influence of various clay minerals on the surfactant imbibition was assessed by considering various properties of the imbibition system. The results showed that the shale pores primarily consisted of macropores (44.03%) and mesopores (43.94%). Surfactants enhance the imbibition recovery by altering the wettability …

Comparative Analysis Of Life Cycle Assessment Of Traditional And Contemporary Timber Buildings, Sadık Akşar, Gökçe Tuna

Journal of Sustainable Construction Materials and Technologies

Wood has long been used as a primary building material, but with the Industrial Revolution and the spread of multi-story structures, its structural limitations became evident. In the 21st century, engineered timber products have reintroduced wood as a sustainable alternative, offering advantages such as lightness, rapid assembly, and recyclability. At the same time, buildings are responsible for significant environmental impacts, making assessment methods essential. Life Cycle Assessment (LCA) is one of the most widely used tools for evaluating these impacts across all stages of a building’s life cycle. This study applies LCA to timber construction systems, focusing on both traditional …

Sustainable Paving Block Development: Utilizing Rejected Coal As Filler And Assessing Its Impact On Paving Block’S Properties, Siti Nikmatin, Allen Kurniawan, Heriansyah Putra, Rima Fitria Adiati, Riawan Ma’Ruf, Dwi Arso Yedi Irwanto

Journal of Sustainable Construction Materials and Technologies

This study investigated the potential use of rejected coal as a fine aggregate filler in paving block production. Comprehensive characterization of the rejected coal was conducted, including toxicity, physical, chemical, and mineralogical analyses of the coal. The results showed that the rejected coal met environmental safety standards (TCLP) and had suitable properties as an aggregate. Paving blocks were produced by incorporating different proportions of rejected coal fillers and were tested for compressive strength, water absorption, and density. The optimal mix with 35% rejected coal filler as a replacement of stone ash achieved a compressive strength of 14.61 MPa, water absorption …

Numerical Comparative Study Of The Thermal Performance And Load-Bearing Capacity Of Hollow Clay Bricks Used In Construction In Libya, Osama Lawej, Mahmoud Ahmed, Ismael Ehtiwesh

Journal of Sustainable Construction Materials and Technologies

The optimization of structural and thermal performance in building components is a key challenge in sustainable construction, particularly in hot climate regions like Libya. Hollow clay bricks are widely used due to their local availability and affordability; however, their internal geometry is often not optimized for thermal resistance or mechanical integrity. This study addresses this gap by numerically investigating the effect of cavity geometry on the load-bearing capacity and thermal insulation performance of hollow clay bricks. Three configurations were analyzed, including two commercial designs from the Libyan market and a newly proposed arched cavity design. Finite Element Method (FEM) simulations …

Developing A Modular Circular Wall Over-Clad System For Facade Thermal Upgrade In Deep Energy Retrofit, Patrick Daly

Journal of Sustainable Construction Materials and Technologies

The EU has implemented several policy and legislative initiatives aimed at decarbonisation and circularity transition of the Built Environment including a strategy for large scale renovation of the building / housing stock. Modularised construction solutions are argued to be a potential acceleration pathway toward this objective. Yet there remains issues and challenges in implementing such strategies in a complex and heterogeneous stock and sector. This research investigates this decarbonisation pathway, examining the feasibility and potential of applying a modularised construction over-clad system, based on advanced circularity, in the deep energy retrofit of social housing. The research is based on a …

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 …

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.

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. …

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 …

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 …

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 …

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 …

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 …