Engineering Commons^™

Learning–Assisted Constraint Filtering To Enhance Power System Optimization Performance, Fouad Hasan

LSU Doctoral Dissertations

Machine learning (ML) is a powerful tool that provides meaningful insights for operators to make fast and efficient decisions by analyzing data from power systems. ML techniques have great potential to assist in solving optimization problems within a shorter time frame and with less computational burden. AC optimal power flow (ACOPF), dynamic economic dispatch (D-ED), and security-constrained unit commitment (SCUC) are the three energy management optimization functions studied in this dissertation. ACOPF is solved every 5~15 minutes. Because of the nonconvex and complex nature of ACOPF, solving this problem for large systems is computationally expensive and time-consuming. Classification and regression …

Selecting The Optimal Formwork System For Horizontal Elements, Alaa Allam, Emad Elbeltagi, Mohamed Naguib Abouelsaad, Mohamed E. El Madawy

Mansoura Engineering Journal

Various types of formworks are available in the market for construction of cast-in-place concrete structures. Formwork has a significant impact on both construction time and cost. As such, decision making on the optimal formwork system is difficult and timeconsuming for designers or planners particularly for high-rise buildings, where any reduction in the cost of single-story formwork significantly decrease total construction cost. Formworks must be designed effectively as numerous accidents have happened as a result of poor design decisions. This article presents a Genetic Algorithm optimization model to select the optimal formwork system among Cuplock, Shore brace and Props systems that …

An Analysis Of The Production Of Pharmaceutical-Grade Acetone Via The Dehydrogenation Of Isopropanol (Ipa), Jordan Desplas

Honors Theses

The production of 99.9 wt% acetone from isopropanol in Unit 1100 is designed to start up in 2025 and operate for 12 years after startup. The engineering team was tasked with designing the process, creating an economic model, and optimizing the net present value (NPV). The process was simulated in AVEVA PRO/II Simulation for the design process, and the economic analysis was estimated in Microsoft Excel. Parametric and topological optimization was performed linearly on the unit operations in the process. The NPV was improved by $14M from a base case of $122M to an optimized case of $136M. The project …

Exploiting Symmetry In Linear And Integer Linear Programming, Ethan Jedidiah Deakins

Doctoral Dissertations

This thesis explores two algorithmic approaches for exploiting symmetries in linear and integer linear programs. The first is orbital crossover, a novel method of crossover designed to exploit symmetry in linear programs. Symmetry has long been considered a curse in combinatorial optimization problems, but significant progress has been made. Up until recently, symmetry exploitation in linear programs was not worth the upfront cost of symmetry detection. However, recent results involving a generalization of symmetries, equitable partitions, has made the upfront cost much more manageable.

The motivation for orbital crossover is that many highly symmetric integer linear programs exist, and …

Addressing The Challenged Of Dcop Based Decision-Making Algorithms In Modern Power Systems, Luis Daniel Ramirez Burgueno

Open Access Theses & Dissertations

Natural disasters have been determined as the leading cause of power outages, causing not only huge economic losses, but also the interruption of crucial welfare activities and the arise of security concerns. Because of the later, decision-making considering grid modernization, power system economics, and system resiliency has been a crucial theme in power systemsâ?? research. The need to better withstand catastrophic events and reducing the dependency of bulky generating units has propelled the development and better management of behind-the-meter generation or distributed energy resources (DERs). DERs can assist in the grid in different manners, not only by meeting energy demand …

Editorial: Innovative Shared Transportation, Marco Nie, Hai Wang, Wai Yuen Szeto

Research Collection School Of Computing and Information Systems

Recent technological developments—mobile computing, autonomous driving, alternative fuel vehicles, and blockchain, to name a few—have enabled numerous innovations in mobility, transportation, and logistics services. They offer unprecedented opportunities to transform conventional transportation systems, for both personal travel and freight logistics, with novel solutions. Of these solutions, those built on the emerging concept of shared economy, such as Uber, Didi, and Cargostream, have received much attention recently. The rapidly expanding scope of shared transportation services now includes ride-sourcing, ridesharing, car sharing, hitch service, flexible paratransit, shared freight delivery, shared logistics, bike sharing, shared last-mile service, parking space sharing, and so on.

Accelerating The Derivation Of Optimal Powertrain Control Strategies Using Reinforcement Learning And Virtual Prototypes, Daniel Egan

All Dissertations

The push for improvements in fuel economy while reducing tailpipe emissions has resulted in significant increases in automotive powertrain complexity, subsequently increasing the resources, both time and money, needed to develop them. Powertrain performance is heavily influenced by the quality of their controller/calibration with modern powertrains reaching levels of complexity where using traditional design of experiment-based methodologies to develop them can take years. Recently, reinforcement learning (RL), a machine learning technique, has emerged as a method to rapidly create optimal controllers for systems of unlimited complexity directly which creates an opportunity to use RL to reduce the overall time and …

A Value-Based Sequential Optimization Framework For Efficient Materials Design Considering Uncertainty And Variability, Maher Alghalayini

All Dissertations

Many problems in engineering and science can be framed as decision problems in which we choose values for decision variables that lead to desired outcomes. Notable examples include maximizing lift in airplane wing design, improving the efficiency of a power plant, or identifying processing protocols resulting in structural materials with desired mechanical properties. These problems typically involve a significant degree of uncertainty about the often-complex underlying relationships between the decision variables and the outcomes. Solving such decision problems involves the use of computational models or physical experimentation to generate data to make predictions and test hypotheses. As a result, both …

Comparative Design Space For Bistable Composites: An Integrated Framework Of Optimization, Finite Element Analysis, And Experimental Testing, Jonathan Bolanos

All Theses

Bistable composites are a class of advanced materials that can actuate between two stable shapes, making them attractive for a wide range of engineering applications. However, designing these composites to achieve optimal performance remains a challenging task. To address the challenge, this research develops an integrated framework that combines a genetic algorithm optimization technique, finite element analysis in Abaqus, and experimental testing to explore the design comparative space for square bistable composites composed of DA 409 carbon fibers. This leads to the study of generating an optimization algorithm to account for the relationship between the chances of a successful maximum …

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …

Chance Constrained Stochastic Optimal Control Of Discrete Time Linear Stochastic Systems With Applications In Multi-Satellite Operations, Shawn Priore

Electrical and Computer Engineering ETDs

Stochastic disturbances arise in a variety of engineering applications. For tractability, Gaussian disturbances are often assumed. However, this may not always be valid, such as when a disturbance exhibits heavy-tailed or skewed phenomena. As autonomous systems become more ubiquitous, non-Gaussian disturbances will become more common due to the compounding effects of sensing, actuation, and external forces. Despite this, little has been done to develop formal methods that are both computationally efficient and allow for analytical assurances with non-Gaussian disturbances. Addressing convex polytopic set acquisition and non-convex collision avoidance chance constraints with quantile and moment-based reformulations, this dissertation proposes novel stochastic …

Network Economics-Based Crowdsourcing In Online Social Networks, Natasha S. Kubiak

Electrical and Computer Engineering ETDs

This thesis addresses the challenge of user recruitment by various competing marketing agencies (MAs) in Online Social Networks. A labor economics approach, following the principles of contract theory, is devised to enable MAs to reveal the potential of each participating user to contribute a personalized level of quality and quantity of information to the crowdsourcing process. The MAs objective is to maximize their personal benefit, i.e., total utility obtained, given its budget. The latter optimization problem is formulated as a Generalized Colonel Blotto (GCB) game among the MAs, where each MA aims at incentivizing each user to report its information. …

Leveraging Automated Fiber Placement Computer Aided Process Planning Framework For Defect Validation And Dynamic Layup Strategies, Joshua Allen Halbritter

Theses and Dissertations

Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. The current state of process planning requires a high degree of interaction from the process planner and could greatly benefit from increased automation. Therefore, a list of key steps and functions are created to identify the more difficult and time-consuming phases of process planning. Additionally, a set of metrics must exist by which to evaluate the effectiveness of the manufactured laminate from the machine code created during the Process Planning …

Comprehensive Process Planning Optimization Framework For Automated Fiber Placement, Alex Ryan Brasington

Theses and Dissertations

Advanced composite materials came about in 1966 and have since been widely used due to the possibility of superior structural performance while also achieving weight reductions. Such opportunities have led to composite materials being used to fabricate complex components, often in the aerospace sector. Most components, especially in aviation, are on a large scale and are outside the capabilities of traditional composite manufacturing techniques. Traditional manufacturing methods are also labor intensive, time consuming, have a high level of material scrap, and are prone to human error. This has led to the need for innovative manufacturing solutions to withstand the ever-increasing …

Optimizing Locations And Sizes Of Asphalt Concrete Plants In Karbala, Iraq, Ghayath Ali, Sawsan R Mohammad, Alaa M. Abdulhussein

Al-Bahir Journal for Engineering and Pure Sciences

This study develops and presents a methodology for determining the optimal geographic distribution and size of asphalt concrete plants in Karbala, Iraq, in order to minimize the cost of asphalt concrete produced. The purpose of this study is to discuss these points. The methodology can identify potential locations for asphalt concrete plants within a study area, considering the plants' operation and capital costs and the costs of transporting raw materials to the plants and asphalt concrete to demand centers. Matrix Laboratory (MATLAB) software have been used to program the methodology. This methodology has been applied to Karbala using actual data. …

Unmanned-Aircraft-System-Assisted Early Wildfire Detection With Air Quality Sensors †, Doaa Rjoub, Ahmad Alsharoa, Ala'eddin Masadeh

Electrical and Computer Engineering Faculty Research & Creative Works

Numerous Hectares of Land Are Destroyed by Wildfires Every Year, Causing Harm to the Environment, the Economy, and the Ecology. More Than Fifty Million Acres Have Burned in Several States as a Result of Recent Forest Fires in the Western United States and Australia. According to Scientific Predictions, as the Climate Warms and Dries, Wildfires Will Become More Intense and Frequent, as Well as More Dangerous. These Unavoidable Catastrophes Emphasize How Important Early Wildfire Detection and Prevention Are. the Energy Management System Described in This Paper Uses an Unmanned Aircraft System (UAS) with Air Quality Sensors (AQSs) to Monitor Spot …

A Novel Covid-19 Herd Immunity-Based Optimizer For Optimal Accommodation Of Solar Pv With Battery Energy Storage Systems Including Variation In Load And Generation, Sumanth Pemmada, Nita Patne, Divyesh Kumar, Ashwini Manchalwar

Turkish Journal of Electrical Engineering and Computer Sciences

The world has now looked towards installing more renewable energy sources type distributed generation (DG), such as solar photovoltaic DG (SPVDG), because of its advantages to the environment and the quality of power supply it produces. However, these sources' optimal placement and size are determined before their accommodation in the power distribution system (PDS). This is to avoid an increase in power loss and deviations in the voltage profile. Furthermore, in this article, solar PV is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG as well as the reduction in peak demand. This paper …

Team And Skill Matching For Disaster Recovery Operations, Emily B. Frahm

Theses and Dissertations

United States Air Force (USAF) bases are key power projection platforms that ensure mission readiness and help bring humanitarian aid to locations in need. Recovering airfields after attack or natural disaster is a key mission of USAF civil engineers, and accomplishing this repair as swiftly as possible is key to maintaining our position in the global order. Accomplishing a disaster recovery project is a set of teams, each assigned to a specific task, and made up of a series of personnel. The question answered within this paper is: how do we match the right person with the appropriate skills to …

The Electromagnetic Bayonet: Development Of A Scientific Computing Method For Aperture Antenna Optimization, Michael P. Ingold

Theses and Dissertations

The quiet zone of a radar range is the region over which a transmitted EM field approximates a uniform plane wave to within some finite error tolerance. Any target to be measured must physically fit within this quiet zone to prevent excess measurement error. Compact radar ranges offer significant operational advantages for performing RCS measurements but their quiet zone sizes are constrained by space limitations. In this work, a scientific computing approach is used to investigate whether equivalent-current transmitters can be designed that generate larger quiet zones than a conventional version at short range. A time-domain near-field solver, JefimenkoModels, was …

Development Of The Tlvmie Force Field And A Standardized Methodology For Improved Pure-Component And Mixture Liquid Viscosity Predictions, Daniel J. Carlson

Theses and Dissertations

Existing viscosity prediction methods and relevant literature are reviewed. An exhaustive review of group contribution, corresponding states, and interpolative prediction methods finds that even the best of these models produces large prediction errors and often require significant experimental data. Molecular dynamics simulation techniques for viscosity prediction are evaluated and compared to one another to determine the best choice for this work. A thorough investigation finds that Equilibrium Molecular Dynamics (EMD) simulations are the best option for reproducible and reliable liquid viscosity predictions. The many tuning parameters available in molecular dynamics simulations are investigated for their effects on prediction uncertainty and …

Scheduling Electric Vehicle Charging For Grid Load Balancing, Zhixin Han, Katarina Grolinger, Miriam Capretz, Syed Mir

Electrical and Computer Engineering Publications

In recent years, electric vehicles (EVs) have been widely adopted because of their environmental benefits. However, the increasing volume of EVs poses capacity issues for grid operators as simultaneously charging many EVs may result in grid instabilities. Scheduling EV charging for grid load balancing has a potential to prevent load peaks caused by simultaneous EV charging and contribute to balance of supply and demand. This paper proposes a user-preference-based scheduling approach to minimize costs for the user while balancing grid loads. The EV owners benefit by charging when the electricity cost is lower, but still within the user-defined preferred charging …

Environmental Efficiency Assessment Of Dublin Port Using Two-Stage Non-Radial Dea Model, Boban Djordjević, Raja Maitra, Bidisha Ghosh

Articles

Global maritime trade has reached 11 billion tons and accounts for more than 80% of global merchandise trade (United Nations Conference on Trade & Development (UNCTAD), 2019). As a result, there is a wide range of vessels, from very large bulk carriers (coal, ores, grains, etc., and crude oil/refinery carriers) to container ships to various cruise ships and naval vessels. To efficiently accommodate these various vessels, ports have had to evolve from wharves to efficient logistical hubs within the larger supply chain that move vessels deeper into the hinterland. Port development is critical to managing the growing volume of cargo …

Quantum Computing And Its Applications In Healthcare, Vu Giang

OUR Journal: ODU Undergraduate Research Journal

This paper serves as a review of the state of quantum computing and its application in healthcare. The various avenues for how quantum computing can be applied to healthcare is discussed here along with the conversation about the limitations of the technology. With more and more efforts put into the development of these computers, its future is promising with the endeavors of furthering healthcare and various other industries.

Decoupling Optimization For Complex Pdn Structures Using Deep Reinforcement Learning, Ling Zhang, Li Jiang, Jack Juang, Zhiping Yang, Er Ping Li, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

This Article Presents a New Optimization Method for Complex Power Distribution Networks (PDNs) with Irregular Shapes and Multilayer Structures using Deep Reinforcement Learning (DRL), Which Has Not Been Considered Before. a Fast Boundary Integration Method is Applied to Compute the Impedance Matrix of a PDN Structure. Subsequently, a New DRL Algorithm based on Proximal Policy Optimization (PPO) is Proposed to Optimize the Decoupling Capacitor (Decap) Placement by Minimizing the Number of Decaps While Satisfying the Desired Target Impedance. in the Proposed Approach, the PDN Structure Information is Encoded into Matrices and Serves as the Input of the DRL Algorithm, Which …

Expression Optimization Of The Gst-Gfp Fusion Protein Through The Alteration Of Induction Conditions, Matthew J. Vaccaro

Honors Undergraduate Theses

This research sought to determine which induction condition resulted in the greatest GST-GFP fusion protein expression. It will hopefully serve as a guide for future researchers trying to produce their own recombinant protein containing GST and GFP-tags. The CDNB Enzyme Assay was used to determine the quantity of GST-GFP fusion protein present and tested three variables: IPTG concentration, duration, and temperature of induction. The findings showed that IPTG concentration, temperature, and induction duration all had a significant impact on protein expression. Induction temperatures of 20 °C and 25 °C showed better protein expression at IPTG concentrations of 1.0 mM IPTG …

Improving Safety Service Patrol Performance, Mecit Cetin, Hong Yang, Kun Xie, Sherif Ishak, Guocong Zhai, Junqing Wang, Giridhar Kattepogu

Civil & Environmental Engineering Faculty Publications

Safety Service Patrols (SSPs) provide motorists with assistance free of charge on most freeways and some key primary roads in Virginia. This research project is focused on developing a tool to help the Virginia Department of Transportation (VDOT) optimize SSP routes and schedules (hereafter called SSP-OPT). The computational tool, SSP-OPT, takes readily available data (e.g., corridor and segment lengths, turnaround points, average annual daily traffic) and outputs potential SSP configurations that meet the desired criteria and produce the best possible performance metrics for a given corridor. At a high level, the main components of the developed tool include capabilities to: …

Initial Procedure For Small-Scale Extraction Of Subsurface Martian Ice, Evan Steers

Electronic Theses and Dissertations

No abstract provided.

Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Dobbins, Ali R. Hurson, Sahra Sedigh

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes an intelligent recommendation approach to facilitate personalized education and help students in planning their path to graduation. The goal is to identify a path that aligns with a student's interests and career goals and approaches optimality with respect to one or more criteria, such as time-to-graduation or credit hours taken. The approach is illustrated and verified through application to undergraduate curricula at the Missouri University of Science and Technology.

Ai-Driven Security Constrained Unit Commitment Using Predictive Modeling And Eigen Decomposition, Talha Iqbal

Graduate Theses, Dissertations, and Problem Reports

Security Constrained Unit Commitment (SC-UC) is a complex large scale mix integer constrained optimization problem solved by Independent System Operators (ISOs) in the daily planning of the electricity markets. After receiving offers and bids, ISOs have only few hours to clear the day-ahead electricity market. It requires a lot of computational effort and a reasonable time to solve a large-scale SC-UC problem. However, exploiting the fact that a UC problem is solved several times a day with only minor changes in the system data, the computational effort can be reduced by learning from the historical data and identifying the patterns …

Modular Supply Network Optimization Of Renewable Ammonia And Methanol Co-Production, Benjamin Akoh

Graduate Theses, Dissertations, and Problem Reports

To reduce the use of fossil fuels and other carbonaceous fuels, renewable energy sources such as solar, wind, geothermal energy have been suggested to be promising alternative energy that guarantee sustainable and clean environment. However, the availability of renewable energy has been limited due to its dependence on weather and geographical location. This challenge is intended to be solved by the utilization of the renewable energy in the production of chemical energy carriers. Hydrogen has been proposed as a potential renewable energy carrier, however, its chemical instability and high liquefaction energy makes researchers seek for other alternative energy carriers. Ammonia …