Chemical Engineering Commons^™

Robust Hybrid Tree-Based Machine Learning-Assisted Optimization Of Well Parameters To Reduce Asphaltene Precipitation Risk In Oil Fields, Malek Jalilian, Madani, Alireza Keshavarz, Stefan Iglauer, Abbas Khaksar Manshad, Amir H. Mohammadi

Research outputs 2022 to 2026

Asphaltene precipitation is a persistent flow-assurance issue in carbonate oil wells, leading to increased intervention frequency and production losses. This study utilizes multi-decade surveillance and operational data from a mature carbonate field (1983–2023) to train and optimize five tree-based models: Decision Tree, Random Forest, Extra Trees, Gradient-Boosting Decision Tree, and CatBoost, employing a Tree-Structured Parzen Estimator. These models, constrained by operational parameters, were integrated to identify optimal settings that can minimize the frequency of asphaltene precipitation and the subsequent cleanups. The novelty of this research lies in the direct integration of interpretable tree ensembles with an optimizer that adheres to …

Probe Cytotoxic And Oxidative Stress Effects Of Nanoplastics On Caco-2 Cells: Insights From Raman Spectroscopy And Machine Learning, Bryan Gustafson, Negar Kosari, Emily Brothersen, Morgan Mosher, Anhong Zhou

Biological Engineering Student Research

Nanoplastics and microplastics have become an increasing ecological and health concern due to their widespread presence in the environment and food chain. In this study, Caco-2 cells were used as an in vitro model of the human intestinal epithelium to investigate the cytotoxic effects of polystyrene nanoparticles and microparticles of varying sizes, concentrations, and surface modification. Oxidative stress and apoptosis were evaluated following particle exposure. Raman spectroscopy, combined with machine learning analysis, was employed to detect and characterize biochemical alterations in the cells. Several peak ratios were selected to cluster data depending on size. A correlation between apoptosis and both …

Video Compression Optimization Techniques Using Artificial Intelligence: Review/Review Article, Amal Abbas Kadhim, Wedad Abdul Khuder Naser, Nada Abdulkareem Hameed

Al-Esraa University College Journal for Engineering Sciences

Compressing video is an important and essential function in today's multimedia systems as it enables the efficient storage and transmission of large volumes of video data. The proliferation of high-resolution videos that are being used in various application areas such as video streaming, video conferencing, surveillance, and autonomous systems has caused the strong demand for more efficient compression algorithms. This paper presents an in-depth review of video compression techniques with particular focus on AI methods. It also discusses traditional video coding standards including H. 264/AVC, H. 265/HEVC, and AV1, including motion estimation, transform coding quantization entropy coding, and rate-distortion optimization. …

Review And Development Of An Explicit Machine Learning Model For Pollutant Gas Solubility In Ionic Liquids As Green Solvents, Amir Dashti, Farid Amirkhani, Mojtaba Raji, John L. Zhou, Ali Altaee, Ali Braytee, Brett Turner, Hossein Ali Khonakdar, Amir Razmjou

Research outputs 2022 to 2026

The increasing release of greenhouse gases (GHGs) like CO₂, CH₄, N₂O, and industrial contaminants (indirect GHGs) such as SO₂ and H₂S has prompted significant global worries due to their role in climate change, air pollution, and harm to the environment. Ionic liquids (ILs) as green solvents have emerged as promising alternatives to traditional solvents because of their minimal volatility, high thermal stability, and adjustable physicochemical characteristics. Yet, limited gas solubility data in ILs is hindering their applications in carbon capture and air pollution control. Machine learning (ML) is a powerful tool for modeling and simulating the solubility of polluting gases …

Increasing The Prediction Accuracy Of Plant Oil Production Processes Through Adjusting The Parameters Of Machine Learning Models, Umidjon Ruziev, M.K. Shodiev, A.T. Rajabov

Chemical Technology, Control and Management

Vegetable oil production is characterized by high variability in output indicators due to nonlinear interactions between raw material parameters, equipment modes, and heat and mass transfer conditions. Existing approaches to applying machine learning in this field, as a rule, do not account for the impact of hyperparameter adjustments on forecasting quality across specific technological stages. The article presents a systematic methodology for adjusting model parameters (Ridge regression, SVR, GBM, LSTM) applied to three key tasks: predicting residual oil content in oil cake, color index during bleaching, and free fatty acid content during deodorization. In a set of 1000 observations, including …

Hybrid Data-Driven Cement-Stabilized Soil Design: An Integration Of Machine Learning, Multi-Objective Optimization, And Life Cycle Assessment, Chikezie Chimere Onyekwena, Yunli Li, Ikenna J. Okeke, Ubani Obinna Uzodimma, Monday Uchenna Okoronkwo, Wenping Wu

Chemical and Biochemical Engineering Faculty Research & Creative Works

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial …

Applying Machine Learning Techniques For Early Detection Of Cyber Attacks On Iot Devices, Noor Adnan Allamy

Al-Esraa University College Journal for Engineering Sciences

This research designs, implements, and evaluates a machine learning-based framework for the early detection of cyber attacks targeting Internet of Things (IoT) devices, with a specific focus on the context and challenges present in Iraq. The study conducts a comparative analysis of three supervised learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN)—using a combination of benchmark datasets (NSL-KDD, CIC-IDS-2017, Bot-IoT) and a synthesized dataset adapted to simulate the Iraqi threat landscape. Key performance metrics, including accuracy, precision, recall, and F1-score, were used for evaluation. The proposed Random Forest model demonstrated superior performance, achieving an accuracy …

Generative Ai For Method Development In Analytical Chemistry: A New Paradigm In Experimental Design And Optimization, Yasir Fathi Mahmood

Al-Esraa University College Journal for Engineering Sciences

The fast development of artificial intelligence (AI), especially generative AI models, is changing the environment of analytical chemistry. As classical method generation in analytical methods relies on manual trial-and-error methodology as well as statistical methods, generative AI is a new paradigm with automated generation of experimental methodology and optimization. In this paper, the authors discuss the use of generative AI-based technologies, including large language models (LLMs) and neural network-based generators, to create new, efficient, and customized methods of analysis. The paper examines existing applications, technology frameworks, and issues and offers a roadmap with regards to the future incorporation of generative …

Improving Self-Diagnostic Methods Of Flow Measurement Systems Based On Artificial Intelligence, Elbek Ortikov

Chemical Technology, Control and Management

The article examines methods for improving self-diagnostics of consumption measurement systems based on artificial intelligence in the context of industry digitalization and the development of cyber-physical systems. It has been shown that traditional flow meters used to measure the flow rate of liquids and gases are subject to mechanical, hydraulic, electronic, and hidden failures, which reduce the accuracy and reliability of measurements. A justification for the need to transition from classical maintenance methods to intelligent self-control methods that ensure the detection of anomalies and hidden malfunctions in real time is presented. A multi-level architecture of intelligent self-diagnosis is proposed, including …

Empirical Analysis Of Machine Learning Models For Predicting Equipment Failures Using Iot Sensor Data, Yusuf Shodiyevich Avazov

Chemical Technology, Control and Management

This article examines the problem of detecting and predicting industrial equipment faults using IoT sensor data through machine learning techniques. Sensor readings such as temperature, vibration, pressure, voltage, and current, as well as FFT-based features, were statistically analyzed. Class imbalance and low signal informativeness were identified as key factors limiting model accuracy. Results obtained from Logistic Regression, Random Forest, and XGBoost models were comparatively evaluated, showing that when ROC-AUC values remain around 0.5, distinguishing fault and non-fault states becomes challenging. Correlation and feature-importance analyses confirmed the absence of strong dominant indicators. The findings highlight the need to improve sensor architecture …

Machine Learning Model For Detecting Masked Hypertension In Young Adults, Brendyn Miller, Samuel Coeyman, Annemarie Wentzel, Carina M.C. Mels, William J. Richardson

Chemical Engineering Faculty Publications and Presentations

Introduction Cardiovascular disease (CVD) remains the leading global cause of mortality, with hypertension (HT) being a significant contributor, responsible for 56% of CVD-related deaths. Masked hypertension (MHT), a condition where patients exhibit normotensive blood pressure (BP) in clinical settings but elevated BP in out-of-clinic measurements, poses an elevated risk for cardiovascular complications and often goes undiagnosed. Current diagnostic methods, such as ambulatory BP monitoring (ABPM) and home BP monitoring (HBPM), have limitations in feasibility and accessibility. Methods This study aimed to address these challenges by leveraging machine learning (ML) models to predict MHT based on clinical data from a single …

Possibilities Of Digitizing And Applying Artificial Intelligence To National Occupational Classification (Noc-2025) In Uzbekistan, Shohrux Nurali O‘G‘Li Narzullayev

Chemical Technology, Control and Management

This article examines the process of digitizing National Occupational Classification (NOC-2025) in Uzbekistan, developed on the basis of the International Standard Classification of Occupations (ISCO-08), and the possibilities of applying artificial intelligence technologies to it. Although this classification exists today in a national form, and its digitization and the introduction of artificial intelligence elements to it based on modern technologies remain a pressing issue. In order to digitize the classification, international systems such as the International Standard Classification of Occupations (ISCO-08, ILO), European Skills, Competences, Qualifications and Occupations (ESCO), Occupational Information Network (O*NET, USA) and National Occupational Classification (NOC, Canada) …

Foundations Of Artificial Intelligence In Healthcare Diagnostics: A Systematic Survey, Raghad Tariq Al-Hassani

Al-Esraa University College Journal for Engineering Sciences

Artificial Intelligence (AI) is becoming the cornerstone of the future of healthcare diagnostics, that has to ability to change the healthcare diagnostic landscape in terms of diagnostic accuracy, speed, and availability. This systematic review investigates the basic methods, tools, applications, and challenges involved in the integration of AI in diagnostic medicine. It emphasizes the using of machine learning models, deep learning networks (e.g., CNNs), NLP for clinical documentation, and smart computing infrastructures, such as edge device and IoMT. They are making possible real-time, data-driven decision making that is already at human-expert-level performance or, in some cases, even better (in the …

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 …

Predicting Groundwater Withdrawals Using Machine Learning With Limited Metering Data: Assessment Of Training Data Requirements, Dawit Asfaw, Ryan G. Smith, Sayantan Majumdar, Katherine Grote, Bin Fang, B. B. Wilson, V. Lakshmi, J. J. Butler

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The future of major aquifer systems supporting irrigated agriculture is threatened due to unsustainable groundwater pumping. Metering of pumping is key for implementing robust groundwater management, but metering is limited in most aquifers. Although machine learning methods have been used to estimate pumping over certain regions, these studies have not fully demonstrated the data quantity and input parameter requirements to accurately estimate regional groundwater pumping. This study determined the data quantity required and identified relevant features to develop Random Forests-based annual groundwater pumping estimates (2008–2020) over the Kansas High Plains aquifer. We predicted pumping at two spatial scales, i.e., point …

Adaptive Control Of Air Purification Processes In Industrial Painting Workshops: A Review Of Scientific Literature, N.A Kabulov, Sh. Mamatbekov

Chemical Technology, Control and Management

This article examines modern approaches to adaptive control of air purification processes in industrial facilities, particularly in paint shops for parts coating. The relevance of the study is driven by the need to enhance air purification efficiency, ensure worker safety, and comply with environmental regulations. The paper analyzes adaptive control methods, including the use of neural networks, machine learning, and adaptive control algorithms, which enable real-time optimization of purification systems. Special attention is given to the utilization of sensor data for predicting pollution levels and automatically adjusting purification parameters. The article also discusses the advantages and limitations of implementing adaptive …

Modeling And Estimation Of Co2 Capture By Porous Liquids Through Machine Learning, Farid Amirkhani, Amir Dashti, Hossein Abedsoltan, Amir H. Mohammadi, John L. Zhou, Ali Altaee

Chemical and Biochemical Engineering Faculty Research & Creative Works

Porous liquids (PLs) are newly developed porous materials that combine unique fluidity with permanent porosity, which exhibit promising functionalities. They have shown ability to efficiently absorb greenhouse gases such as carbon dioxide (CO₂). Experimental measurement is one approach to determining the solubility of various greenhouse gases in PLs, which has drawbacks such as being expensive and time-consuming. Hence, simulation models are valuable to predict the solubility of CO₂ in various PLs. This work aims to develop machine learning (ML) modeling methods for accurately estimating CO₂ solubility under varying conditions (e.g. PLs, temperature, pressure). Adaptive Neuro-Fuzzy Inference …

Algorithms For Fast Fire Risk Prediction And Real-Time Data Processing, Mirzoyan Mirzaaxmedovich Kamilov, Tolaniddin Ramziddinovich Nurmukhamedov, Oybek Zokirovich Koraboshev, Bakhodir Saydullayevich Achilov

Chemical Technology, Control and Management

Ensuring fire safety in facilities with high fire risk is one of the pressing problems of modern society. Nowadays, there is a great need for accurate and effective prediction systems for fire prevention and rapid response. Since traditional methods do not provide the ability to quickly analyze and predict in real time, the development of algorithms and modern approaches using modern technologies is of great importance. This article analyzes fire risk prediction algorithms, their principles of operation and effectiveness, and considers methods for assessing and predicting fire risk using Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies. The …

Uncertainty Propagation And Sensitivity Analysis For Constrained Optimization Of Nuclear Waste Vitrification, Lagrande Gunnell, Xiaonan Lu, John D. Vienna, Dong-Sang Kim, Brian J. Riley, John Hedengren

Faculty Publications

The vitrification of high-level waste (HLW) by heating a mixture of glass-forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization …

Artificial Intelligence And Internet Of Things Integration In Pharmaceutical Manufacturing: A Smart Synergy, Reshma Kodumuru, Soumavo Sarkar, Varun Parepally, Jignesh Chandarana

Michigan Tech Publications

Background: The integration of artificial intelligence (AI) with the internet of things (IoTs) represents a significant advancement in pharmaceutical manufacturing and effectively bridges the gap between digital and physical worlds. With AI algorithms integrated into IoTs sensors, there is an improvement in the production process and quality control for better overall efficiency. This integration facilitates enabling machine learning and deep learning for real-time analysis, predictive maintenance, and automation—continuously monitoring key manufacturing parameters. Objective: This paper reviews the current applications and potential impacts of integrating AI and the IoTs in concert with key enabling technologies like cloud computing and data analytics, …

Fundamental And Application Of Co-Assembly Of Peptides And Proteins: Experiment And Computation, Newton A. Ihoeghian, Qing Shao

Chemical and Materials Engineering Faculty Publications

Peptides and proteins can co-assemble into various nanostructures based on complementary non-covalent interactions such as electrostatic forces, hydrogen bonding, and hydrophobic associations. These co-assemblies create a design space of functional materials for a wide spectrum of energy, environmental, and biomedical applications. This review focuses on the fundamentals and applications of three co-assembling systems: ultra-short peptides, peptides, and proteins. We will present and discuss experimental studies demonstrating applications in drug delivery, tissue engineering, and biomaterials development. We will also discuss the contribution of computational research, including molecular dynamics simulations and machine learning, to enhance our understanding of assembly mechanisms. Integrating experimental …

Nanotube Spectral Fingerprinting And Machine Learning For Optimized Bioimaging/Sensing And Disease Detection Applications In Als, Rodrigo Monroy Lopez

Open Access Master's Theses

Single-walled carbon nanotubes (SWCNTs) possess unique physicochemical and optical properties that make them ideal candidates for biomedical imaging, biosensing, and disease diagnostics. This thesis explores the potential of SWCNT-based spectral fingerprinting combined with ML (Machine Learning) algorithms to optimize bioimaging, disease detection and prediction, with a specific focus on differentiating between healthy and amyotrophic lateral sclerosis (ALS) lymphoblastic patient samples. By functionalizing SWCNTs with single-stranded DNA, we enhance their stability and target specificity, enabling their application in serum patient samples.

A comprehensive spectral analysis of DNA-SWCNTs was conducted using near-infrared fluorescence spectroscopy and other characterization techniques, including UV-Vis-absorption spectroscopy. The …

Augmentation And Hybridization Of Conventional Forms Of Process Control With Advanced Control Methods, Daniel Paul Beahr

Graduate Theses, Dissertations, and Problem Reports (ETD)

Within the last decade in the field of process control, there has developed a distinct gap between the technological advancements of today and the well-established theory that has preceded it. This is most evident where the novel developments and structures often outpace the necessary guarantees and strong foundation required to ensure the possibility of widespread adoption. Therefore, the goals of this work are to broadly integrate those novel advanced control methods into the field of process control in such a way to increase the applicability and encourage implementation.

The first portion of this work seeks to further explore the nature …

Ion Hydration In Bulk And Nanoconfined Water: Insights From Machine Learning Force Fields, Zachary D. Baker

Theses and Dissertations--Chemical and Materials Engineering

Understanding ionic hydration remains a central challenge in physical chemistry and materials science, as the interactions between ions and water molecules govern diverse phenomena ranging from electrolyte transport to selective ion separation. While experimental techniques have provided invaluable insights into solvation energetics and coordination numbers, they often lack atomistic resolution, particularly under nanoscale confinement where direct measurement becomes infeasible. Molecular dynamics (MD) simulations can bridge this gap; however, conventional classical force fields are limited by their simplified, fixed functional forms and empirical parameterization, whereas ab initio molecular dynamics (AIMD) achieves higher accuracy at the expense of severe computational cost and …

3d Printing Parameter Optimisation Combined With Heat Treatment For Achieving High Density And Enhanced Performance In Refractory High-Entropy Alloys, Deyu Jiang, Miao Luo, Changxi Liu, Yashuo Zhang, Lai Chang Zhang, Kuaishe Wang, Wen Wang, Lechun Xie, Liqiang Wang, Weijie Lu, Di Zhang

Research outputs 2022 to 2026

In this study, a Ti1.5Nb1Ta0.5Zr1Mo0.5 (TNTZM) high-entropy alloy was fabricated using laser powder bed fusion (LPBF). By integrating 63 sets of parameter trials with machine learning (ML) models, an optimised process window was identified, achieving a density of up to 99.9%. The combination of relatively high laser power and low scanning speed resulted in the formation of a stable cellular structure. Subsequent heat treatments at 700, 850, and 1000°C showed that while small-angle misorientations developed at cell-wall interfaces and medium-entropy (Ti–Zr–Mo) second-phase particles precipitated preferentially in the cell walls, the overall cellular architecture remained intact. Mechanical testing showed that these …

All-Solid-State Sodium-Ion Batteries: A Leading Contender In The Next-Generation Battery Race, Rui-Jie Zhu, Ze-Chen Li, Wei Zhang, Akira Nasu, Hiroaki Kobayashi, Masaki Matsui

Journal of Electrochemistry

All-solid-state lithium-ion batteries (LIBs) using ceramic electrolytes are considered the ideal form of rechargeable batteries due to their high energy density and safety. However, in the pursuit of all-solid-state LIBs, the issue of lithium resource availability is selectively overlooked. Considering that the amount of lithium required for all-solid-state LIBs is not sustainable with current lithium resources, another system that also offers the dual advantages of high energy density and safety— all-solid-state sodium-ion batteries (SIBs) —holds significant sustainable advantages and is likely to be the strong contender in the competition for developing next-generation high-energy-density batteries. This article briefly introduces the research …

Unveiling The Hidden Threat: How Wireless Networks Fuel Serious Cyber Attacks, Ibtesam Jomaa Hawi

Al-Esraa University College Journal for Engineering Sciences

The spread of wireless networks has led to an increase in serious cyber attacks due to their weak architecture. This article focuses on reevaluating cybersecurity in wireless network technology by integrating statistical information detection methods and artificial intelligence (AI) algorithms. To construct a wireless networking scenario that accurately reflects real-life conditions, we created a data fabrication that included four pre-existing anomalies as well as four newly introduced anomalies. The synthetic dataset created from these generation processes contains 20 thousand distinguishable values, which are later divided into training and validation sets. Using the strategy described before, we began to analyze the …

Optimization Of Tps Films Using An Adaptive Design Of Experiments Approach In A Bayesian Optimization Framework, Theresa Marks, Gracie White, Scott Lohman, Mayank Malhotra

The Journal of Purdue Undergraduate Research

Plastic pollution, amounting to 12 million tons annually, necessitates sustainable alternatives to single-use plastics. Compostable thermoplastic starch (TPS) films show promise but lack strength and durability compared to traditional plastics. This study employs an adaptive design of experiments (DoE) approach to enhance TPS films by optimizing testing points. The research focuses on varying concentrations of plasticizers (acetic acid and glycerol) in a water and potato starch mixture, aiming to identify the optimal ratio maximizing tensile strength and % elongation at break. Gaussian process regression (GPR) with uncertainty estimation and Bayesian optimization (BO) utilizing an acquisition function (AF) are employed. The …

Collision Dynamics Of Compound Droplets In Microchannels: A Combined Numerical And Data-Driven Study, S M Abdullah Al Mamun

Dissertations

Understanding and predicting the hydrodynamic interactions of micron-scale droplets is crucial in a wide range of industrial and real-life applications, including microfluidics, pharmaceutics, drug delivery, food science, and enhanced oil recovery. These multi-phase and multi-scale phenomena are further complicated by the presence of core droplets of an immiscible fluid within shell droplets, known as compound droplets. The collisions and interactions of droplets in emulsions are influenced by various physical and geometric parameters, leading to distinct rheological and dynamic responses. This research employs numerical methods for a systematic parametric study of both simple and compound droplet pair collisions under confined shear …

Mixed Potential Sensor For Natural Gas Leak Detection Design, Manufacturing, And Simulation, Sleight Halley

Chemical and Biological Engineering ETDs

There is a need for a low-cost robust sensor accurately detecting leaks from oil and gas infrastructure. This work presents the development of an electrochemical sensor based on an yttria stabilized zirconia (YSZ) electrolyte with La_0.87Sr_0.13CrO₃, Indium Tin Oxide (In₂O₃ 90 wt%, SnO₂ 10 wt%), Au, Pt electrodes. Selectivity to target gasses of the three sensing electrodes in conjunction with machine learning allows for the accurate discrimination between possible methane sources as well as the quantification of methane concentration. Sensor sensitivity is improved through a low-ionic conductivity substrate of magnesia …