Chemical Engineering Commons^™

Machine-Learning-Assisted Design Of Deep Eutectic Solvents Based On Uncovered Hydrogen Bond Patterns, Usman Lame Abbas, Yuxuan Zhang, Joseph Tapia, Md Selim, Jin Chen, Jian Shi, Qing Shao

Markey Cancer Center Faculty Publications

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportuni- ties. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the …

Assessing The Potential Of Uav-Based Multispectral And Thermal Data To Estimate Soil Water Content Using Geophysical Methods, Yunyi Guan, Katherine R. Grote

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a …

Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Smart water flooding is a promising eco-friendly method for enhancing oil recovery in carbonate reservoirs. The optimal salinity and ionic composition of the injected water play a critical role in the success of this method. This study advances the field by employing machine learning and data analytics to streamline the determination of these critical parameters, which are traditionally reliant on time-intensive laboratory work. The primary objectives are to utilize data analytics to examine how smart water flooding influences wettability modification, identify key parameter ranges that notably alter the contact angle, and formulate guidelines and screening criteria for successful lab design. …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Smart water flooding is a promising eco-friendly method for enhancing oil recovery in carbonate reservoirs. the optimal salinity and ionic composition of the injected water play a critical role in the success of this method. This study advances the field by employing machine learning and data analytics to streamline the determination of these critical parameters, which are traditionally reliant on time-intensive laboratory work. the primary objectives are to utilize data analytics to examine how smart water flooding influences wettability modification, identify key parameter ranges that notably alter the contact angle, and formulate guidelines and screening criteria for successful lab design. …

Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li

Computer Science Faculty Publications

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding …

Enhancing Wettability Prediction In The Presence Of Organics For Hydrogen Geo-Storage Through Data-Driven Machine Learning Modeling Of Rock/H2/Brine Systems, Zeeshan Tariq, Muhammad Ali, Nurudeen Yekeen, Auby Baban, Bicheng Yan, Shuyu Sun, Hussein Hoteit

Research outputs 2022 to 2026

The success of geological H2 storage relies significantly on rock–H2–brine interactions and wettability. Experimentally assessing the H2 wettability of storage/caprocks as a function of thermos-physical conditions is arduous because of high H2 reactivity and embrittlement damages. Data-driven machine learning (ML) modeling predictions of rock–H2–brine wettability are less strenuous and more precise. They can be conducted at geo-storage conditions that are impossible or hazardous to attain in the laboratory. Thus, ML models were utilized in this research to accurately model the wettability behavior of a ternary system consisting of H2, rock minerals (quartz and mica), and brine at different operating geological …

Moisture Effects On Visible Near-Infrared And Mid-Infrared Soil Spectra And Strategies To Mitigate The Impact For Predictive Modeling, Francis Hettige Chamika Anuradha Silva

Theses and Dissertations

Instrumental disparities and soil moisture are two of the key limitations in implementing spectroscopic techniques in the field. This study sought to address these challenges through two objectives. The first objective was to assess Visible-near infrared (VisNIR) and mid-infrared (MIR) spectroscopic approaches and explore the feasibility of transferring calibration models between laboratory and portable spectrometers. The second objective addressed the challenge of soil moisture and its impact on spectra. The portable spectrometers demonstrated comparable performance to their laboratory-based counterparts in both regions. Spiking with extra-weight, was the most effective calibration transfer method eliminating disparities between instruments. The samples were rewetted …

Employment Of Intelligent Predictive Maintenance On Thermal Power Plant Component Parts Taking Condenser Vacuum As A Case Study, Titus Ajewole

Tanzania Journal of Engineering and Technology (TJET)

This work proposes deployment of machine learning in the maintenance of individual constituent parts of steam power plant assemblages. With the condenser vacuum of a steam turbine (in a six-turbine plant assemblage) taken as a case study, information on the past operating parameters of the selected plant component was used to forecast its future working condition. Based on Exponential Gaussian Process of Regression, a model was developed, trained using the diachronic operational data, and employed in determining the future. A quantitative evaluation was employed to provide the distribution of the test values of the data about the lines of regression, …

A Probabilistic View Of Protein Stability, Conformational Specificity, And Design, Jacob A. Stern, Tyler J. Free, Kimberlee L. Stern, Spencer Gardiner, Nicholas A. Dalley, Bradley Charles Bundy, Joshua L. Price, David Wingate, Dnnis Della Corte

Faculty Publications

Various approaches have used neural networks as probabilistic models for the design of protein sequences. These "inverse folding" models employ different objective functions, which come with trade-offs that have not been assessed in detail before. This study introduces probabilistic definitions of protein stability and conformational specificity and demonstrates the relationship between these chemical properties and the p(stucture|seq) Boltzmann probability objective. This links the Boltzmann probability objective function to experimentally verifiable outcomes. We propose a novel sequence decoding algorithm, referred to as “BayesDesign”, that leverages Bayes’ Rule to maximize the p(stucture|seq) objective instead of the p(seq|structure) objective common in inverse folding …

Novel Breath Collection Techniques For Detection Of Covid-19., James Morris

Electronic Theses and Dissertations

Volatile Organic Compounds (VOC) generated endogenously in the human body can be used to detect diseases that induce oxidative stress and inflammation. Breath analysis has been used for the detection of diseases such as COPD, Depression, Lung Cancer and most recently COVID-19. Methods such as Exhaled Breath Condensate (EBC), Sorbent Tubes, Solid Phase Microextraction (SPME), and silicon microreactors have shown considerable capability in extracting and concentrating trace VOC’s present in human breath. Silicon microreactors functionalized with capture agents to derivatize carbonyl compounds are effective but the overall maximum flow rate of breath sample possible during analysis is low, making direct …

Prediction Of Terephthalic Acid Yield In Aqueous Hydrolysis Of Polyethylene Terephthalate, Hossein Abedsoltan, Zeinab Zoghi, Amir H. Mohammadi

Chemical and Biochemical Engineering Faculty Research & Creative Works

Aqueous hydrolysis is used to chemically recycle polyethylene terephthalate (PET) due to production of high-quality terephthalic acid (TPA), the PET monomer. PET hydrolysis depends on various factors including PET size, catalyst concentration, and reaction temperature. So, modeling PET hydrolysis by considering the effective factors can provide useful information for material researchers to specify how to design and run these reactions. It will save time, energy, and materials by optimizing the hydrolysis conditions. Machine learning algorithms enable to design models to predict the output results. For the first time, 381 experimental data were gathered to model aqueous hydrolysis of PET. Effective …

A Data-Driven Multivariate Process Monitoring Platform For Knowledge Discovery And Model Building In Industrial Applications, Estelle E. Seghers

LSU Master's Theses

In industrial chemical manufacturing processes, the amount of raw data generated can add complexity in the analysis and understanding of the process dynamics. Being able to properly interpret this data can help improve plant operation, especially regarding safety and profitability. This research has culminated in FastMan-JMP, a platform proposed for monitoring of industrial processes and optimization of the offline data-driven model-building process as part of the process monitoring workflow. FastMan-JMP is a tool developed in Python to apply various data mining and machine learning techniques quickly and easily to better understand valuable patterns and hidden trends in process data. One …

Synchronization Of The Training Of Specialists In The Automation Of Technological Processes In Accordance With The Dynamics Of The Taxonomy Of Learning Goals, Yusuf Shodievich Avazov, Kamola Abdullaeva

Chemical Technology, Control and Management

Changes in the characteristics of modern ones, reflecting them as the "digital generation", inevitably necessitate electronic interactive, mobile and mixed, machine learning, especially in the field of automation and control of technological processes and production. As a consequence of reflecting these realities, there have been parallel changes in the taxonomy of learning objectives - from the classical to the revised rethought and digital.

In order to study the problem of synchronizing the training of future specialists in the field of automation and control of technological processes and production and funny knowledge in accordance with the dynamics of the taxonomy of …

Machine Learning Assisted Framework For Advanced Subsurface Fracture Mapping And Well Interference Quantification, Mohammad Faiq Adenan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., …

Insights To Surfactant Huff-Puff Design In Carbonate Reservoirs Based On Machine Learning Modeling, Ya Yao, Yue Qiu, Yue Cui, Mingzhen Wei, Baojun Bai

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Surfactants could react with adsorbates on carbonate rock surface to alter wettability from oil-wetness to water-wetness, which is effective to enhance oil recovery. Surfactant huff-puff treatment is mostly applied for this purpose and the resulting surfactant performance is the outcome of complex interfacial processes. Currently, the effect of important parameters on surfactant performance is not completely reported and the contribution of each parameter to surfactant performance is hard to be quantified. Traditional methods to optimize surfactant performance are time-consuming and show strong dependency on extensive experiments. in this paper, we address these problems from machine learning (ML) perspectives. Several ML …

Generation Of Phase Transitions Boundaries Via Convolutional Neural Networks, Christopher Alexis Ibarra

Open Access Theses & Dissertations

Accurate mapping of phase transitions boundaries is crucial in accurately modeling the equation of state of materials. The phase transitions can be structural (solid-solid) driven by temperature or pressure or a phase change like melting which defines the solid-liquid melt line. There exist many computational methods for evaluating the phase diagram at a particular point in temperature (T) and pressure (P). Most of these methods involve evaluation of a single (P,T) point at a time. The present work partially automates the search for phase boundaries lines utilizing a machine learning method based on convolutional neural networks and an efficient search …

Machine Learning With Gradient-Based Optimization Of Nuclear Waste Vitrification With Uncertainties And Constraints, Lagrande Gunnell, Kyle Manwaring, Xiaonan Lu, Jacob Reynolds, John Vienna, John Hedengren

Faculty Publications

Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared …

Classification Of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach, Yanwei Zhang, Stephen S. Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Shear wave splitting (SWS) analysis is widely used to provide critical constraints on crustal and mantle structure and dynamic models. In order to obtain reliable splitting measurements, an essential step is to visually verify all the measurements to reject problematic measurements, a task that is increasingly time consuming due to the exponential increase in the amount of data. In this study, we utilized a convolutional neural network (CNN) based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data …

Machine Learning Prediction Of Glass Transition Temperature Of Conjugated Polymers From Chemical Structure, Amirhadi Alesadi, Zhiqiang Cao, Zhaofan Li, Song Zhang, Haoyu Zhao, Xiaodan Gu, Wenjie Xia

Faculty Publications

Predicting the glass transition temperature (T_g) is of critical importance as it governs the thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive modeling framework to predict T_g of CPs through the integration of machine learning (ML), molecular dynamics (MD) simulations, and experiments. With 154 T_g data collected, an ML model is developed by taking simplified “geometry” of six chemical building blocks as molecular features, where side-chain fraction, isolated rings, fused rings, and bridged rings features are identified as the dominant ones for T_g. MD simulations further unravel the fundamental roles …

Recent Advances In Electrochemical Kinetics Simulations And Their Applications In Pt-Based Fuel Cells, Ji-Li Li, Ye-Fei Li, Zhi-Pan Liu

Journal of Electrochemistry

Theoretical simulations of electrocatalysis are vital for understanding the mechanism of the electrochemical process at the atomic level. It can help to reveal the in-situ structures of electrode surfaces and establish the microscopic mechanism of electrocatalysis, thereby solving the problems such as electrode oxidation and corrosion. However, there are still many problems in the theoretical electrochemical simulations, including the solvation effects, the electric double layer, and the structural transformation of electrodes. Here we review recent advances of theoretical methods in electrochemical modeling, in particular, the double reference approach, the periodic continuum solvation model based on the modified Poisson-Boltzmann …

Artificial Intelligence-Based Material Discovery For Clean Energy Future, Reza Maleki, Mohsen Asadnia, Amir Razmjou

Research outputs 2022 to 2026

Artificial intelligence (AI)-assisted materials design and discovery methods can come to the aid of global concerns for introducing new efficient materials in different applications. Also, a sustainable clean future requires a transition to a low-carbon economy that is material-intensive. AI-assisted methods advent as inexpensive and accelerated methods in the design of new materials for clean energies. Herein, the emerging research area of AI-assisted material discovery with a focus on developing clean energies is discussed. The applications, advantages, and challenges of using AI in material discovery are discussed and the future perspective of using AI in clean energy is studied. This …

Understanding Structure/Process-Property Relationships To Optimize Development Lifecycle In Yttria-Stabilized Zirconia Aerogels For Thermal Management, Rebecca C. Walker

Theses and Dissertations

Aerogels are mesoporous materials with unique properties, including high specific surface area, high porosity, low thermal conductivity, and low density, increasing these materials’ effectiveness in applications such as catalyst supports, sorption media, and electrodes in solid oxide fuel cells. Zirconia (ZrO₂) aerogels have special interest for high-temperature applications due to the high melting point of ZrO₂ (2715°C) and stability between 600°C and 1000°C, where other aerogel systems often begin to sinter and densify. These properties and unique pore structure make zirconia aerogels advantageous as thermal management systems, especially in aeronautics and aerospace applications. However, to be effective …

Large-Scale Reality Modeling Of A University Campus Using Combined Uav And Terrestrial Photogrammetry For Historical Preservation And Practical Use, Bryce Berrett, Cory Vernon, Haley Beckstrand, Madi Pollei, Kaleb Markert, Kevin Franke, John Hedengren

Faculty Publications

Unmanned aerial vehicles (UAV) enable detailed historical preservation of large-scale infrastructure and contribute to cultural heritage preservation, improved maintenance, public relations, and development planning. Aerial and terrestrial photo data coupled with high accuracy GPS create hyper-realistic mesh and texture models, high resolution point clouds, orthophotos, and digital elevation models (DEMs) that preserve a snapshot of history. A case study is presented of the development of a hyper-realistic 3D model that spans the complex 1.7 km2 area of the Brigham Young University campus in Provo, Utah, USA and includes over 75 significant structures. The model leverages photos obtained during the historic …

Determination Of Hydrogel Degradation By Passive Mechanical Testing, Avery Rosh-Gorsky

Honors Theses

This paper details a new technique to measure the mechanical properties of ETTMP PEGDA hydrogels using Hertz Contact Theory and simultaneously analyze both the model drug release and gel erosion in situ. This method involves curing a drug loaded hydrogel in a standard cuvette and placing a glass bead and phosphate buffer solution (PBS). Over time, the cross-linked network of the hydrogel breaks down, and, as a result, the ball sinks into the hydrogel. This method provides a macroscopic and inexpensive way to continuously and passively measure properties of the hydrogel as the hydrogel degrades. By plotting both the …

Information Architecture For A Chemical Modeling Knowledge Graph, Adam R. Luxon

Theses and Dissertations

Machine learning models for chemical property predictions are high dimension design challenges spanning multiple disciplines. Free and open-source software libraries have streamlined the model implementation process, but the design complexity remains. In order better navigate and understand the machine learning design space, model information needs to be organized and contextualized. In this work, instances of chemical property models and their associated parameters were stored in a Neo4j property graph database. Machine learning model instances were created with permutations of dataset, learning algorithm, molecular featurization, data scaling, data splitting, hyperparameters, and hyperparameter optimization techniques. The resulting graph contains over 83,000 nodes …

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Investigating The Effect Of E30 Fuel On Long Term Vehicle Performance, Adaptability And Economic Feasibility, Adil Alsiyabi, Seth Stroh, Rajib Saha

Department of Chemical and Biomolecular Engineering: Faculty Publications

Due to the drawbacks associated with the use of petroleum derived fuels, the use of more sustainable fuel sources has garnered increasing attention in several sectors including road transportation. However, the transition away from gasoline is often hindered by the inability of currently operating vehicles to efficiently run under alternative fuels. Therefore, the logical short-term alternative is to transition to clean fuel sources including higher-ethanol fuel blends that are compatible with current fuel systems and spark-ignition engines. In this work, the long-term adaptability and economic feasibility of non-flex vehicles to consume a 30% ethanol (E30) fuel blend was investigated. Sixteen …

Machine Learning Based Applications For Data Visualization, Modeling, Control, And Optimization For Chemical And Biological Systems, Yan Ma

LSU Doctoral Dissertations

This dissertation report covers Yan Ma’s Ph.D. research with applicational studies of machine learning in manufacturing and biological systems. The research work mainly focuses on reaction modeling, optimization, and control using a deep learning-based approaches, and the work mainly concentrates on deep reinforcement learning (DRL). Yan Ma’s research also involves with data mining with bioinformatics. Large-scale data obtained in RNA-seq is analyzed using non-linear dimensionality reduction with Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), followed by clustering analysis using k-Means and Hierarchical Density-Based Spatial Clustering with Noise (HDBSCAN). This report focuses …

Data–Driven Decision–Making For Lost Circulation Treatments: A Machine Learning Approach, Husam H. Alkinani, Abo Taleb T. Al-Hameedi, Shari Dunn-Norman

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Lost circulation is an expensive and critical problem in the drilling operations. Millions of dollars are spent every year to mitigate or stop this problem. In this work, data from over 3000 wells were collected from multiple sources. The data went through a processing step where all outliers were removed, and decision rules were set up. Multiple machine learning methods (support vector machine, decision trees, logistic regression, artificial neural networks, and ensemble trees) were used to create a model that can predict the best lost circulation treatment based on the type of loss and the reason of loss. 5-fold cross-validation …