Engineering Science and Materials Commons^™

Algorithms For Making Control Systems Based On Neural Network Technology To Automate Dynamic Plants, Jasur Sevinov, Abdishukurov Maqsudovich Shavkat

Chemical Technology, Control and Management

This paper examines the stages of multilayer neural network technology for the control and identification of dynamic systems. It is seen many ways to use multilayer neural networks in control systems. Neural controllers are based on several models and algorithms. Examples of such algorithms include the predictive model-based control algorithm, the Narma-L2 control algorithm, and the benchmark model-based control algorithm. These controllers illustrate various general ways to use multilayer neural networks in control systems. Common, standard linear control architectures have been proposed for many neural controllers.

Data-Driven Constrained Control, Ali Kashani

Mechanical Engineering ETDs

Ensuring safety is a fundamental challenge. Traditional methods often rely on precise mathematical models, which are difficult, impractical, or costly to obtain for real-world systems with complex, nonlinear dynamics. This dissertation develops direct data-driven control approaches that enable safe and efficient operation of nonlinear systems without requiring explicit models or performing system identification. This effort leverages machine learning, optimization, and control theory to bridge theoretical rigor and practical applicability. Deterministic guarantees are provided based on the Lipschitz continuity of the system, and probabilistic guarantees through scenario optimization. The computation of safe sets is performed using one-shot approaches with broad neural …

Vacuum Consistent Electrochemistry In Ionic Liquid Combined With Oxide Epitaxy, Yuji Matsumoto

Journal of Electrochemistry

We introduce our state-of-the art of “vacuum consistent electrochemistry” to an investigation of the interfaces between oxides and ionic liquid (IL). Pulsed laser deposition (PLD) has been one of the powerful and sophisticated techniques to realize nanoscale preparation of high-quality epitaxial oxide thin films. On the other hand, electrochemistry is a simple, very sensitive, and non-destructive analysis technique for solid-liquid interfaces. To ensure the reproducibility in experiment of the interfaces of such epitaxial oxide films, as well as bulk oxide single-crystals, with IL, we employ a home-built PLD-electrochemical (EC) system with IL as an electrolyte. The system allows one to …

Redox-Active Monolayers On Ito Prepared By Post-Amidation And Direct Esterification And Their Spectroelectrochemical Characterization, Takamasa Sagara, Sae Nakai, Ryusuke Yofu, Shota Kojin

Journal of Electrochemistry

A redox-active monolayer on an optically transparent electrode constitutes a typical platform for spectroelectrochemical sensing. The necessity for its sophistication arises from the availability of multi-dimensional sensing signals. Simultaneous monitoring of the redox current and color change synchronized with the oxidation state change significantly enhances sensitivity and selectivity. This study aimed to elucidate the modification of an indium tin oxide (ITO) electrode with a viologen monolayer with an ordered orientation. Novel methods were developed to immobilize a viologen molecule bearing a carboxyl group to form assembled monolayers through a condensation reaction using 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide with N-hydroxy-succinimide (EDC/NHS). In the two methods …

Ambient Co2 Capture And Valorization Enabled By Tandem Electrolysis Using Solid-State Electrolyte Reactor, Yan-Bo Hua, Bao-Xin Ni, Kun Jiang

Journal of Electrochemistry

Electrocatalytic carbon dioxide reduction is a promising technology for addressing global energy and environmental crises. However, its practical application faces two critical challenges: the complex and energy-intensive process of separating mixed reduction products and the economic viability of the carbon sources (reactants) used. To tackle these challenges simultaneously, solid-state electrolyte (SSE) reactors are emerging as a promising solution. In this review, we focus on the feasibility of applying SSE for tandem electrochemical CO₂ capture and conversion. The configurations and fundamental principles of SSE reactors are first discussed, followed by an introduction to its applications in these two specific areas, …

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 …

Thermal Modeling Techniques For Additively Manufactured Porous Metals, Lucas D. Rambo

Master's Theses

Advances in additive manufacturing have enabled the creation of metallic components with tailored porosity, offering design flexibility for thermal, structural, and fluid transport applications. This thesis investigates the effective thermal conductivity (ETC) of porous Inconel 625 (IN625) and Inconel-ceramic composites (IN625-RAM2) fabricated by Laser Powder Bed Fusion (LPBF) using Elementum 3D’s PermiAM technology. Experimental measurements of thermal conductivity using the laser flash method are compared to numerical models built from micro-computed tomography data. Simulations were performed using both finite element (FE) and finite volume (FV) methods to evaluate thermal transport in real microstructures. The study investigates the effects of micro- …

Mae Enewsbrief Jan-Mar 2025, Department Of Mechanical And Aerospace Engineering, Michigan Technological University

Department of Mechanical and Aerospace Engineering eNewsBrief

No abstract provided.

Mae Enewsbrief Oct-Dec 2024, Department Of Mechanical And Aerospace Engineering, Michigan Technological University

Department of Mechanical and Aerospace Engineering eNewsBrief

No abstract provided.

Comparative Study Of Experimental And Fea Strain Data For Cnt-Coated Fiberglass Sensors, Extensometers, And Metal Foil Strain Gauges, Omar Dwidar

Master's Theses

Material testing is essential across industries such as aerospace, automotive, and construction, playing a critical role in verifying material selection, diagnosing failures, and understanding the development of flaws in structures. These insights are key to designing successful, reliable systems. Conventional metal foil strain gauges are low cost and reliable but provide limited sensitivity with a typical gauge factor around 2. Extensometers provide highly sensitive strain measurements with the disadvantage of a bulky form factor. With advanced materials such as carbon nanotubes, it is possible to manufacture a sensor with the sensitivity closer to that of an extensometer with the small …

Preparation And Modification Of Mxene Composites For Application In Electrochemical Energy Storage, Zhang-Hai You, Ding-Ze Lu, Kiran Kumar Kondamareddy, Wen-Ju Gu, Peng-Fei Cheng, Jing-Xuan Yang, Rui Zheng, Hong-Mei Wang

Journal of Electrochemistry

With the acceleration of advanced industrialization and urbanization, the environment is deteriorating rapidly, and non-renewable energy resources are depleted. The gradual advent of potential clean energy storage technologies is particularly urgent. Electrochemical energy storage technologies have been widely used in multiple fields, especially supercapacitors and rechargeable batteries, as vital elements of storing renewable energy. In recent years, two-dimensional material MXene has shown great potential in energy and multiple application fields thanks to its excellent electrical properties, large specific surface area, and tunability. Based on the layered materials of MXene, researchers have successfully achieved the dual functions of energy storage and …

Three-Dimensional Melamine Carbon Sponge/Nai As Cathode Materials For Sodium-Ion Batteries, Qian-Ying Huang, Yue Liu, Zi-Xin Lin, Shu-Yi Zheng, Ting-Ting Mei, Yu-Ting Tang, Ying-He Zhang, Jun Liu

Journal of Electrochemistry

The sodium-iodine (Na-I) battery exhibits significant potential as an alternative energy storage device to the lithium-ion battery. However, its development is hindered by inadequate electrical and thermal stability, as well as the dissolution and shuttling of polyiodide. In this study, we report a preparation method for melamine carbon sponge (MC) via carbonizing a commercially available kitchen sponge. It was revealed that the as-prepared MC, composed of unique self-growing carbon nanotubes, could provide both physical and chemical adsorption capabilities for intermediate polyiodides to improve the electrochemical performance of NaI. Consequently, the NaI/MC electrode effectively minimized polyiodide dissolution and reduced the electrochemical …

Understanding The Morphology And Mass Transport Resistance Of Mesoporous Carbon-Supported Pemfc Based On Modeling Analysis, Hao Deng, Jia Liu, Zhong-Jun Hou

Journal of Electrochemistry

Mesoporous carbon supports mitigate Pt sulfonic poisoning through nanopore-confined Pt deposition, yet their morphological impacts on oxygen transport remain unclear. This study integrates carbon support morphology simulation with an enhanced agglomerate model to establish a mathematical framework elucidating pore evolution, Pt utilization, and oxygen transport in catalyst layers. Results demonstrate dominant local mass transport resistance governed by three factors: (1) active site density dictating oxygen flux; (2) ionomer film thickness defining shortest transport path; (3) ionomer-to-Pt surface area ratio modulating practical pathway length. At low ionomer-to-carbon (I/C) ratios, limited active sites elevate resistance (Factor 1 dominant). Higher I/C ratios improve …

Optimization Of Renewable Energy Systems: Comparative Analysis Of Advanced Algorithms And Photovoltaic-Electrolyzer Performance For Cost Reduction And Efficiency Enhancement, Mohamed-Amine Babay, Mustapha Adar, Ahmed Chebak, Mustapha Mabroukia

Journal of Electrochemistry

The increasing demand for cost-effective and efficient renewable energy solutions presents significant optimization challenges in hybrid energy systems. This paper addresses these challenges by conducting a comparative analysis of three advanced optimization algorithms—Lévy Flight Optimization (LFO), Archimedean Optimization (AO), and Quantum Gorilla Optimization (QGO)—to minimize the Total Net Present Cost (TNPC) and Levelized Cost of Energy (LCOE) in hybrid renewable energy systems. The study integrates critical cost parameters such as Capital Expenditure (CAPEX), Operational Expenditure (OPEX), replacement costs, and salvage values into an advanced optimization framework. Three system configurations are evaluated: Wind Turbines and Fuel Cells (WT/FC), Photovoltaic Systems and …

Theoretical Insights Into The Atomic And Electronic Structures Of Polyperyleneimide: On The Origin Of Photocatalytic Oxygen Evolution Activity, Yi-Qing Wang, Zhi Lin, Ming-Tao Li, Shao-Hua Shen

Journal of Electrochemistry

Polymeric perylene diimide (PDI) has been evidenced as a good candidate for photocatalytic water oxidation, yet the origin of the photocatalytic oxygen evolution activity remains unclear and needs further exploration. Herein, with crystal and atomic structures of the self-assembled PDI revealed from the X-ray diffraction pattern, the electronic structure is theoretically illustrated by the first-principles density functional theory calculations, suggesting the suitable band structure and the direct electronic transition for efficient photocatalytic oxygen evolution over PDI. It is confirmed that the carbonyl O atoms on the conjugation structure serve as the active sites for oxygen evolution reaction by the crystal …

Exploring Bim Capabilities For Life Cycle Assessment In Structural Projects: A Valencian Barraca Case Study For Sustainable Material Choices, Oscar Selfa, Barry Mcauley, Kieran Lakhera O'Shea

Conference Papers

The construction sector significantly contributes to greenhouse gas (GHG) emissions, resource consumption, and waste generation, with structural materials accounting for up to 40% of embedded carbon. This study investigates the integration of Building Information Modelling (BIM), Finite Element Analysis (FEA), and Life Cycle Assessment (LCA) tools to promote sustainable structural design. Using a BIM model of a Valencian barraca, three scenarios were evaluated: traditional materials (adobe and wood), concrete with steel, and concrete with Cross- Laminated Timber (CLT). Results show that natural materials generate lower impacts, while steel and concrete exhibit higher emissions. This work proposes a workflow integrating BIM, …

Ultimate Limit State Reliability-Based Approach Life Prediction Of An Existing Frame Structure Under Seismic Hazard In Indonesia, Wira Herucakra, Luh Putri Adnyani

Journal of Materials Exploration and Findings

Life assessment integrated with Structural Integrity Management (SIM) has been implemented in the oil and gas industry for the past decade to maintain the integrity and safety of an in-service structure used to support hydrocarbon exploration, production, and processing activity. Current structural life assessment, as ruled by the American Petroleum Institute (API), uses a fatigue-based approach for offshore structures subjected to wave loading. As the general principle of SIM is not limited to offshore structures, this study offers an alternative life assessment method for onshore structures, especially steel frame structures subjected to earthquake loads. Reliability analysis, combined with the effect …

Data-Driven Dynamic Decision-Making Using Discrete Optimization And Supervised Machine Learning, Navid Rashedi

Dartmouth College Ph.D Dissertations

In recent years, the operations research community has developed data-driven optimization techniques to solve complex combinatorial problems with the aid of machine learning. This thesis contributes to these efforts by combining machine learning with optimization to expedite online decision-making, with applications in transportation and healthcare.

In the domain of airline operations recovery, the focus is on the aircraft recovery process—repairing disrupted schedules by minimizing overall disruption costs. Traditional exact methods are too time-consuming, while heuristic approaches often yield poor solution quality and lack generalizability across varying formulations. To address these challenges, this research employs supervised machine learning to identify near-optimal …

Characteristics Of Carbonaceous Materials Synthesized From Palm Oil Empty Fruit Bunch Waste Using Ferrocene Catalyst, Ahmad Nabil Shahab, Adinda Izzatul Islam, Afif Wardana, Ilham Nur Dimas Yahya, Ary Yanuar Tri Amalia, Nofrijon Sofyan, Donanta Dhaneswara

Journal of Materials Exploration and Findings

The synthesis of carbonaceous materials for reduced graphene oxide (rGO) precursors using oil palm empty fruit bunches waste as a carbon source offers a sustainable solution for waste management in the palm oil industry while delivering high-performance materials. The oil palm empty fruit bunches were carbonized, followed by acid washing, pyrolysis with ferrocene (8%, 12%, and 16% variations), and ultrasonication to produce rGO. The structural, electronic, and morphological properties of the rGO were analyzed using various characterization techniques. The band gap values decreased with increasing ferrocene concentration, from 1.14 eV (8%) to 1.06 (16%), indicating enhanced electronic conductivity. XRD analysis …

Multi-Domain Machine Learning For Biological Classification: Mallard Classification And Protein Function Prediction, Tolulope Samuel Adeyina

Open Access Theses & Dissertations

Multi-domain machine learning applications have revolutionized how we understand and predict complex biological phenomena. This dissertation presents novel computational methodologies addressing two critical problems: mallard classification using single-nucleotide polymorphisms (SNPs), and protein function prediction via interpretable topic-aware peptide embeddings. The research focuses on distinguishing mallard populations through SNP data, which are inherently characterized by ultra-high dimensionality. The research uses advanced feature-selection and dimensionality-reduction strategies alongside machine learning classification algorithms to identify minimal, yet highly predictive SNP sets crucial for accurate breed differentiation. This framework demonstrates robust performance with optimal computational efficiency, significantly aiding conservation and breed management efforts. Furthermore, the …