Engineering Commons^™

Machine Learning Modeling Of Polymer Coating Formulations: Benchmark Of Feature Representation Schemes, Nelson I. Evbarunegbe

Masters Theses

Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities.

Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this …

Application Of Crystal Engineering In Multicomponent Pharmaceutical Crystals: A Study Of Theory And Practice, Soroush Ahmadi Nasrabadi

Electronic Thesis and Dissertation Repository

Multicomponent crystallization, a prominent strategy in crystal engineering, offers the ability to modify the physicochemical properties of crystals by introducing a secondary component to their lattice structure. Such multicomponent crystals have found widespread application in the pharmaceutical industry. This thesis explores the experimental screening, characterization, application, and theoretical prediction of multicomponent crystals of Active Pharmaceutical Ingredients (APIs).

The first case study investigates a new solvate of Dasatinib which exhibits high instability at room temperature and transforms into a different polymorph upon desolvation. The crystal structure of this compound is obtained, revealing insights into its transient nature and the potential application …

Lignin Copolymer Property Prediction Using Machine Learning, Collin Larsen

Chemical Engineering Undergraduate Honors Theses

Lignin, an abundant biopolymer, is a waste byproduct of the paper and pulp industry. Despite its renewable nature and potential applicability in various products, such as plastics and composites, the development of lignin-based materials has been impeded by the cumbersome, Edisonian process of trial and error. This research proposes a novel approach to forecasting the properties of lignin-based copolymers by utilizing a recurrent neural network (RNN) based on the Keras models previously created by Tao et al. Example units of modified lignin were synthesized via esterification and amination functional group modifications. To increase the efficiency and accuracy of the prediction …

Comparative Analysis Of Artificial Intelligence And Numerical Reservoir Simulation In Marcellus Shale Wells, Arya Maher Sattari

Graduate Theses, Dissertations, and Problem Reports

This dissertation addresses the limitations of conventional numerical reservoir simulation techniques in the context of unconventional shale plays and proposes the use of data-driven artificial intelligence (AI) models as a promising alternative. Traditional methods, while providing valuable insights, often rely on simplifying assumptions and are constrained by time, resources, and data quality. The research leverages AI models to handle the complexities of shale behavior more effectively, facilitating accurate predictions and optimizations with less resource expenditure.

Two specific methodologies are investigated for this purpose: traditional numerical reservoir simulations using Computer Modelling Group's GEM reservoir simulation software, and an AI-based Shale Analytics …

Application Of Distributed Fiber-Optic Sensing For Pressure Predictions And Multiphase Flow Characterization, Gerald Kelechi Ekechukwu

LSU Doctoral Dissertations

In the oil and gas industry, distributed fiber optics sensing (DFOS) has the potential to revolutionize well and reservoir surveillance applications. Using fiber optic sensors is becoming increasingly common because of its chemically passive and non-magnetic interference properties, the possibility of flexible installations that could be behind the casing, on the tubing, or run on wireline, as well as the potential for densely distributed measurements along the entire length of the fiber. The main objectives of my research are to develop and demonstrate novel signal processing and machine learning computational techniques and workflows on DFOS data for a variety of …

Machine Learning Based Real-Time Quantification Of Production From Individual Clusters In Shale Wells, Ayodeji Luke Aboaba

Graduate Theses, Dissertations, and Problem Reports

Over the last two decades, there has been advances in downhole monitoring in oil and gas wells with the use of Fiber-Optic sensing technology such as the Distributed Temperature Sensing (DTS). Unlike a conventional production log that provides only snapshots of the well performance, DTS provides continuous temperature measurements along the entire wellbore.

Whether by fluid extraction or injection, oil and gas production changes reservoir conditions, and continuous monitoring of downhole conditions is highly desirable. This research study presents a tool for real-time quantification of production from individual perforation clusters in a multi-stage shale well using Artificial Intelligence and Machine …

Hydrocarbon Pay Zone Prediction Using Ai Neural Network Modeling., Darren D. Guedon

Graduate Theses, Dissertations, and Problem Reports

This paper captures the ability of AI neural network technology to analyze petrophysical datasets for pattern recognition and accurate prediction of the pay zone of a vertical well from the Santa Fe field in Kansas.

During this project, data from 10 completed wells in the Santa Fe field were gathered, resulting in a dataset with 25,580 records, ten predictors (logs data), and a single binary output (Yes or No) to identify the availability of Hydrocarbon over a half feet depth segment in the well. Several models composed of different predictors combinations were also tested to determine how impactful some logs …

Molecular Modeling Of High-Performance Thermoset Polymer Matrix Composites For Aerospace Applications, Prathamesh P. Deshpande

Dissertations, Master's Theses and Master's Reports

The global efforts from major space agencies to transport humans to Mars will require a novel lightweight and ultra-high strength material for the spacecraft structure. Three decades of research with the carbon nanotubes (CNTs) have proved that the material can be an ideal candidate for the composite reinforcement if certain shortcomings are overcome. Also, the rapid development of the polymer resin industry has introduced a wide range of high-performance resins that show high compatibility with the graphitic surface of the CNTs. This research explores the computational design of these materials and evaluates their efficacy as the next generation of aerospace …

Thermodynamic Vapor-Liquid Equilibrium In Naphtha-Water Mixtures, Sandra Milena Lopez-Zamora

Electronic Thesis and Dissertation Repository

Naphtha is used to dilute the froth from bitumen treatment. Naphtha is recovered using a Naphtha Recovery Unit (NRU) and sent back to the froth dilution step. To minimize the environmental and economic impact of the NRU, it is imperative to maximize the naphtha recovery. It is, in this respect, that enhanced NRU Vapour-Liquid-Liquid equilibrium data is a significant value. The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a …

Prediction Of Self-Diffusion Constants In Model And Real Systems Using Machine Learning, Joshua P. Allers

Chemical and Biological Engineering ETDs

Understanding diffusion of chemical compounds is important for the design and optimization of many chemical engineering and energy processes. Recent modifications to the Darken equation allow for accurate prediction of Maxwell-Stefan (MS) diffusion in mixtures. Still, there are few practical applications due to the requirement of individual self-diffusion constants. A reliable predictive model for self-diffusion constants would be highly valuable when used in conjunction with the modified Darken equation. Here, we show that Machine Learning (ML) can be used to develop generalized models for self-diffusion in Lennard Jones (LJ) systems and real systems of pure solutions. The use of Artificial …

Process Data Analytics Using Deep Learning Techniques, Majid Moradi Aliabadi

Wayne State University Theses

In chemical manufacturing plants, numerous types of data are accessible, which could be process operational data (historical or real-time), process design and product quality data, economic and environmental (including process safety, waste emission and health impact) data. Effective knowledge extraction from raw data has always been a very challenging task, especially the data needed for a type of study is huge. Other characteristics of process data such as noise, dynamics, and highly correlated process parameters make this more challenging.

In this study, we introduce an attention-based RNN for multi-step-ahead prediction that can have applications in model predictive control, fault diagnosis, …

Computational Modeling Of The Structure And Catalytic Behavior Of Graphene-Supported Pt And Ptru Nanoparticles, Raymond Gasper

Doctoral Dissertations

Computer modeling has the potential to revolutionize the search for new catalysts for specific applications primarily via high-throughput methodologies that allow researchers to scan through thousands or millions of potential catalysts in search of an optimal candidate. To date, the bulk of the literature on computational studies of heterogeneous catalysis has focused on idealized systems with near-perfect crystalline surfaces that are representative of macroscopic catalysts. Advancing the frontier to nanoscale catalysis, in particular, heterogeneous catalysis on nanoclusters, requires consideration of low-symmetry nanoparticles with realistic structures including the attendant complexity arising from under-coordination of catalyst atoms and dynamic fluxionality of clusters. …