Digital Commons Network^™

Scalable Quality Assessment Of Ground Motion Records Via Interpretable Deep Learning Architectures, Ali Montazeri Namin

All Graduate Theses and Dissertations, Fall 2023 to Present

Earthquake engineers rely on accurate recordings of ground shaking to design safe and resilient buildings. However, sorting through thousands of these recordings to find the reliable ones and throwing out those ruined by sensor errors or background noise is traditionally done by hand. Because modern seismic networks collect massive amounts of earthquake data every day, this manual checking process is much too slow. To fix this, researchers are turning to artificial intelligence to automatically check the quality of these recordings.

While artificial intelligence offers a fast solution, there are limitations. These computer models can become massive and expensive to run, …

Comparative Performance Of Physiological Vital-Sign Forecasting Under Random And Patient-Wise Splitting Using Deep Learning, Lavanya Vasavi Chittem Reddy

Theses and Dissertations

Physiological vital-sign forecasting estimates future measurements based on recent temporal patterns and can support analysis of continuously recorded monitoring data. This study comparatively evaluated deep feedforward, recurrent, bidirectional recurrent, long short-term memory, and bidirectional long short-term memory architectures for one-step-ahead forecasting of peripheral oxygen saturation, heart rate, and pulse rate. Each model received consecutive observations of peripheral oxygen saturation, heart rate, pulse rate, respiratory rate, and age, while separate single-output models predicted the next value of the selected target.

Random and patient-wise data splitting were compared using identical input definitions, preprocessing procedures, model architectures, and training hyperparameters. The strongest architecture …

Ncf Sensor Coated With Cofe1.96la0.04o4/Lafeo3 Heterostructures For Room-Temperature Detection Of Liquefied Petroleum Gas Concentration, Ziqiang Liu, Fujian Tang, Yufang He, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Liquefied petroleum gas (LPG) is a highly flammable fuel widely used for cooking, heating and transportation, making real-time leak detection essential for preventing fire, explosion, and suffocation hazards. In this study, a room-temperature no-core fiber (NCF) sensor coated with CoFe_1.96La_0.04O₄/LaFeO₃ heterostructures was developed for LPG detection. The heterostructures were characterized using XRD, Raman spectroscopy, SEM, TEM, XPS, PL spectroscopy, and UV–vis spectroscopy. Experimental results showed that La incorporation and heterostructure formation refined the particles size, improved surface accessibility, and modulated the electronic structure and band gap, thereby enhancing LPG adsorption, carrier redistribution, and …

Lithium Titanate-Functionalized Mixed Matrix Polymer Membrane For High-Performance Osmotic Energy Conversion, Rockson Kwesi Tonnah, Yasaman Boroumand, Milad Razbin, Mostafa Vahdani, Amir Razmjou, Mohsen Asadnia

Research outputs 2022 to 2026

Reverse electrodialysis (RED) offers a promising route to harvest osmotic energy from salinity gradients, yet practical implementation is hindered by the lack of scalable, high-performance ion-selective membranes that combine efficient ion transport with mechanical robustness and environmental sustainability. Here, we report a mixed matrix membrane composed of polyethersulfone (PES), polyvinylpyrrolidone (PVP), and lithium titanium oxide (Li₂TiO₃, LTO) that addresses these challenges through biomimetic ion transport channels. The strategic integration of LTO nanoparticles introduces ion-exchange sites and oxygen-rich PES ether linkages to create preferential cation transport pathways that mimic biological ion channels. This mixed matrix design delivers over three-fold cation conduction, …

Prototyping Pinhole Collimators For Imaging Irradiated Nuclear Fuel, Narrie Loftus, Ashish Avachat, Joseph T. Graham, Ryan Fronk, Jordan Fox, Rachel Shaffer, Seth Kilby

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

Collimator design and fabrication is a costly and time-consuming process that does not lend itself to simplified testing. This manuscript presents an approach for the rapid prototyping of sub-millimeter pinhole collimators for nuclear fuel applications using low-melting-point, high-mass-density materials. Multiple collimators were fabricated to test collimator parameters and properties, including acceptance angle, aperture diameter, and magnification. The fabricated collimators were paired with a position-sensitive CdZnTe detector to determine the detector response convolved with the collimator. Preliminary analysis using these collimators showed that they were effective in collimating multi-energy gamma laboratory sources. Radiography was performed to confirm magnification and determine coarse …

Crushing Behavior Of Crash Boxes With Hybrid Honeycomb–Auxetic Fillers: Effects Of Architecture, Geometry, And Material Behaviors, A. Yudhanto, A. Jusuf, L. D. Lumanauw, M. Falyanzhuri, A. Afdhal

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Additively manufactured (AM) fillers provide new opportunities to tailor the crushing response and energy absorption of thin-walled metallic crash boxes. This study presents a combined experimental–numerical investigation of hexagonal AA6063-T4 crash boxes filled with architected structures, i.e., honeycomb, auxetic-reentrant, and hybrid honeycomb–auxetic topology. The hybrid configuration, which integrates cells with positive and negative Poisson's ratios, triggers coordinated mechanisms (that enhance folding behavior and collapse control) unattainable via single topology. Quasi-static axial compression tests were conducted to characterize force–displacement curves, deformation mechanisms, energy absorption (EA), and specific energy absorption (SEA). Finite element models developed in the explicit solver LS-DYNA were employed …

A Review Of Gallium And Germanium Recovery From Industrial Solid Residues, Ernest V. Oteng, Marthias Silwamba, Lana Alagha, Alex Luyima

Mining Engineering Faculty Research & Creative Works

The global demand for critical elements such as gallium (Ga) and germanium (Ge), and the shift toward circular mining has accelerated the development of new extraction and recovery processing these metals from industrial solid residues. This transition is driven by a substantial surge in demand for advanced technologies and the depletion of primary ore. Consequently, industrial solid residues, particularly zinc processing residues, smelter slags, flue dusts, and coal fly ash, are emerging as viable alternative sources. These residues incorporate Ga and Ge through isomorphic substitution in phases such as ferrites, silicates, and glassy aluminosilicates, and are enriched by industrial processes, …

Lrq-Solver: A Transformer-Based Neural Operator For Fast And Accurate Solving Of Large-Scale 3d Pdes, Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, Tiezhu Gao, Zhuowei Wang, Aimin Yang, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

Solving large-scale PDEs on complex three-dimensional geometries remains a central challenge in scientific and engineering computing, often due to expensive pre-processing stages and high computational overhead. We present Low-Rank Query-based PDE Solver (LRQ-Solver), a physics-integrated deep learning framework for efficient CAE simulations of complex three-dimensional geometries in CAD-driven design analysis. Built upon the Parameter-Conditioned Lagrangian Modeling (PCLM) that embeds physical consistency into the learning process and the Low-Rank Query Attention (LR-QA) module that reduces attention complexity from O(N2) to O(NC2+C3) via covariance decomposition, LRQ-Solver supports multi-configuration analysis within iterative design workflows. On two benchmark datasets, it achieves a 28.6% error …

Mind The Hazard: Modeling And Interpreting Comfort With Personalized Sensing, Yufei Zhang, Matteo Favero, Patrick Chwalek, Sailin Zhong, Denis Lalanne, A. Joseph Paradiso, Clayton Miller, Andrew Sonta

Research Collection College of Integrative Studies

Recent advances in personalized sensing and comfort feedback have spurred the development of data-driven comfort models tailored to individual needs. However, because current models treat sequential comfort feedback independently, they are subject to unstable predictions and limited interpretability, hindering their deployment in building management. This study introduces a dynamic modeling framework that utilizes a Neural Ordinary Differential Equations-based Continuous-time Markov Chain to model the transitions in comfort states over time. Our modeling approach, developed through a field study utilizing smart glasses and mobile app feedback, tracks occupants' comfort transitions across daily activities and contexts. The results demonstrate that this model …

Adaptive Improved Particle Swarm Optimization-Based Maximum Power Point Tracking And Energy Smoothing For Photovoltaic Hybrid Battery–Supercapacitor Storage Systems, Mohammad Aminul Islam, Guo Shaokai, Jakaria Mahdi Imam, Mohammad Khairul Basher, Nowshad Amin, Tarek Abedin, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

The rapid development of photovoltaic (PV) systems has made them an important component of the global clean energy strategy. However, the intermittency and non-linear characteristics of photovoltaic (PV) output remain major challenges for stable renewable energy utilization. This study proposes an adaptive improved particle swarm optimization (IPSO)-based maximum power point tracking (MPPT) strategy integrated with hybrid energy storage coordination for photovoltaic systems. The IPSO introduces adaptive inertia adjustment, velocity clamping, and stagnation reinitialization, which improve the convergence robustness under dynamic irradiance and temperature conditions. The algorithm was benchmarked against Perturb & Observe (P&O), Incremental Conductance (INC), and standard PSO using …

Green Lithium Extraction From Alkaline Brines Using A Dibenzoylmethane Octanol System, Maryam Gonbadi, Shayan Abrishami, Ozra Gholipour, Ana Vafadar, Amir Razmjou

Research outputs 2022 to 2026

The escalating global demand for lithium necessitates the development of efficient, selective, and environmentally sustainable extraction technologies from brine resources. This study presents a novel solvent extraction system employing dibenzoylmethane (DBM) as a lithium-selective β-diketone chelating extractant and 1-octanol as a biodegradable diluent for lithium recovery from alkaline salt lake brines. The effects of critical process parameters, including aqueous phase pH, extractant concentration, and total dissolved solids (TDS), on lithium extraction efficiency and selectivity were systematically investigated. Under optimized conditions (pH 12, 0.3 M DBM in 1-octanol), the system achieved a single-stage lithium extraction efficiency of 80.8% with exceptional separation …

A Review Of Natural Hydrogen Generation From Iron-Rich Rocks: Mechanisms, Influencing Factors, Techniques, And Knowledge Gaps, Kaveh Moghanirahimi, Lionel Esteban, Marina Pervukhina, Muhammad Arif, Stefan Iglauer, Alireza Keshavarz

Research outputs 2022 to 2026

Natural hydrogen has emerged as a promising clean energy resource with significant potential for large-scale subsurface production. Among the various geological sources, water–rock interactions involving iron-bearing rocks are among the most extensively studied pathways for natural hydrogen generation. This study provides a comprehensive review of hydrogen production from iron-rich lithologies, including mafic and ultramafic rocks, iron oxides, iron carbonates, and peralkaline granites. The fundamental mechanism involves the reduction of water coupled with the oxidation of ferrous iron to ferric iron under anoxic conditions. Key processes, including serpentinization, magnetite alteration, and siderite decomposition, are critically evaluated using experimental and modelling data. …

Nanoparticle-Enabled Superplasticity In Extruded Az91 Magnesium Alloy With Elongation Exceeding 600%, Bai Xin Dong, Hong Yu Yang, Xin Zhang, Feng Qiu, Qi Chuan Jiang, Lai Chang Zhang

Research outputs 2022 to 2026

No abstract provided.

Ai-Driven Biomarker Discovery & Progression Modeling For Precision Diagnosis Of Glaucoma, Cheng Huang

Computer Science and Engineering Theses and Dissertations

This dissertation presents a comprehensive study on the integration of artificial intelligence (AI) for glaucoma diagnosis and retinal image analysis. Leveraging multimodal imaging data including fundus photography, Optical Coherence Tomography Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA), the research develops a suite of deep learning frameworks designed to detect early glaucomatous changes with high precision, robustness, and interpretability. A series of novel architectures are introduced, spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models, all designed to enhance diagnostic accuracy and generalizability across diverse populations. To facilitate reproducible and scalable ophthalmic AI research, this work …

Treatment Of Municipal Solid Waste Incineration Bottom Ash Using Cement For Integrated Recovery Of Hydrogen And Production Of Supplementary Cementitious Material, Muhammad Haris Javed, Wenyu Liao, Fathma Zuhra, David Vollero, David Schmidenberg, Hongyan Ma

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Municipal solid waste incineration (MSWI) bottom ash (MBA) represents both a disposal challenge and a potential resource for sustainable construction materials, including use as a supplementary cementitious material (SCM). However, the conventional use of MBA in cement-based materials is hindered by its high metallic aluminum content, which reacts in alkaline environments to release hydrogen gas and causes volumetric instability (e.g., expansion and cracking). This study investigates a cement-based alkali pretreatment method using diluted cement suspension to stabilize metallic aluminum and improve the compatibility of MBA as an SCM. Compared to conventional alkali solutions, the proposed approach avoids the direct use …

Rheology Modifiers For Water-Based Slurry Pipeline Transportation: Overview, Efficiency, And Intensification, Ahmed Alalou, Mohammed El Asri, Ahmed Boulahna, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Owing to their cost-efficiency and eco-friendliness, water-based slurry pipelines are the state-of-the-art technology for the transportation of several types of solids such as coal, fly ash, petcoke, iron ore, phosphate rock and other minerals. This review comprehensively examines the effect of numerous commercial dispersants and novel chemical additives that are used as rheology modifiers to enhance the slurryability and fluidity of solid-water slurries at high solids loading. Several key performance parameters such as the rheological characteristics (shear stress, apparent viscosity, yields stress, and flow behavior), the potential stability, surface tension and contact angle that highlight the efficiency of the rheology …

Ph-Compensated Hydrogen Peroxide Quantification Using A Dual-Modal Fiber-Optic Probe, Homayoon Soleimani Dinani, Bohong Zhang, Maryam Karimi, Rex E. Gerald, Shelley D. Minteer, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We report a dual-modal fiber-optic probe that integrates electrochemical quantification of hydrogen peroxide (H₂O₂) with co-localized fluorescent pH sensing for pH-indexed interpretation of the H₂O₂ response. H₂O₂ is a reactive oxygen species involved in oxidative stress, inflammation, and cellular signaling, and local pH modulates both its production and electrochemical response. Many electrochemical H₂O₂ sensors exhibit pH-dependent sensitivity, creating ambiguity unless pH is measured and used for compensation, which is difficult in small, heterogeneous, or rapidly changing microenvironments. A three-electrode configuration—working (WE), counter (CE), and Ag/AgCl pseudo-reference (pRE) electrodes—is fabricated directly on the cylindrical surface of a 710-µm-diameter optical fiber using …

Graphene Oxide–Driven In Situ Bismuth Reduction Enables Highly Efficient Electroreduction Of Co2 To Formic Acid, Hao Feng, Bohong Zhang, Jie Huang, Xinhua Liang

Electrical and Computer Engineering Faculty Research & Creative Works

To enable large scale efficient electrochemical CO₂ reduction reaction (CO₂RR) to formic acid (HCOOH), it is important to develop catalysts that can be operated in a wide potential window with good stability. Herein, we successfully synthesized Bi₂O₃ catalyst supported on graphene oxide (GO) and graphene (G) and found that Bi₂O₃/GO catalyst had a better overall performance than Bi₂O₃/G. The Bi₂O₃/GO catalyst demonstrated an outstanding CO₂RR performance with a greater than 90% faradaic efficiency (FE) across a wide applied potential window …

Thermo-Mechanical Behaviour Of Metal-Coated Optical Fibers For Distributed High-Temperature Sensing: From Laboratory Characterization To Industrial Case Validation, Koustav Dey, Rony Kumer Saha, Bohong Zhang, Laura Bartlett, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Reliable distributed temperature sensing in high-temperature environments remains a significant challenge due to the thermal and mechanical limitations of conventional optical fibers. In particular, polymer-coated fibers degrade above ∼300 °C due to coating failure, mechanical fragility and hydrogen ingress. Metal-coated optical fibers offer a robust alternative for harsh environments such as Electric Arc Furnaces (EAFs), aerospace engines, nuclear systems, and oil and gas wells, owing to their superior mechanical strength and hermetic sealing. In this work, a first comprehensive experimental investigation of the thermo-mechanical behavior of metal-coated optical fibers for distributed high temperature sensing is presented over a wide temperature …

An Eco-Inspired Emulsion Membrane Harnessed With Waste Vegetable Oil For Effective Separation Of Ibuprofen: Insights Into Stability, Performance, And Kinetics, Huma Warsi Khan, A. Vijaya Bhaskar Reddy, Parvez Alam Khan, Samsur Akm Rahman, Muhammad Moniruzzaman

Publications and Research

Emulsion liquid membranes (ELMs) have emerged as excellent alternatives for the separation and recovery of biologically active drugs (BADs) from industrial waste at trace concentrations. Conventional ELMs generally utilize toxic petroleum-based solvents (PBS) that pose environmental and health risks. Although virgin vegetable oils have been appeared as sustainable alternatives to PBS, their widespread application is limited by economic constraints, resource sustainability, and competition with the food supply. In contrast, waste vegetable oil (WVO) represents an abundant, low-cost, and eco-friendly alternative, yet its potential as a green diluent for ELMs remains largely unexplored. To address this challenge, present study investigated the …

Sensors And Smart Food Packaging For Supply Chains: Bridging Expiration Dates And Actual Perishability, Muhammad Umar Azam, Jolina Rodrigues, Mohamed Hamid Salim, Sarath Haridas Kaniyamparambil, Khalid Askar, Imane Belyamani, Blaise L. Tardy, Nouha Alcheikh

All Works

The food supply chain is undergoing critical changes to minimize environmental hazards, such as those associated with packaging, and reduce food waste and loss. One of the critical components of the latter is associated with the high variance in the perishability of individual foodstuffs. Herein, we review the state of the art in smart sensor systems and emphasize the critical role they could play in addressing the mismatch between “batch” based expiry dates and individual food products’ time-dependent responses to spoilage. Following a brief overview of food shelf life and associated legislation, a subsequent section summarizes the development of sensors …

Decoding Surface Chemistry Effects On Polystyrene Nanoplastic Fouling In Plasma-Grafted Pes Membranes With Distinct Functional Groups, Mohadeseh Najafi, Javad Farahbakhsh, Ebrahim Mahmoudi, Michael Johns, Masoumeh Zargar

Research outputs 2022 to 2026

Nanoplastic (NP) fouling remains a key challenge for ultrafiltration (UF) membranes, yet systematic, controlled comparisons of how membrane functional groups govern NP-membrane interactions are still limited. Here, the role of membrane surface chemistry was systematically investigated using a controlled comparative framework based on plasma-grafted polyethersulfone (PES) UF membranes tailored with structurally comparable methacrylate monomers bearing distinct terminal functionalities, including mono-2-(methacryloyloxy)ethyl succinate (MMES, -COOH), N-[3-(dimethylamino)propyl] methacrylamide (DMAPMA, -N(CH3)2), and [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (SBMA, zwitterionic). Comprehensive characterisations supported successful surface grafting while preserving the membrane substructure. Fouling and separation were assessed using polystyrene (PS) NPs having different surface chemistries, i.e., PS, PS-COOH and …

Numerically Evaluating The Effect Of Pin Geometries On Interlayer Material Mixing And Thermo-Mechanical Characteristics During Additive Friction Stir Deposition (Afsd), Numan Habib, Ana Vafadar, Ferdinando Guzzomi

Research outputs 2022 to 2026

Additive Friction Stir Deposition (AFSD) is an additive manufacturing technique used to fabricate large-sized components layer-by-layer in a solid state, below the melting temperature. Poor interlayer mixing between subsequent layers results in a lack of mechanical interlocking, leading to low-strength components. The role of pin design plays a crucial role in fabricating high-strength components, requiring a comprehensive understanding of thermo-mechanical behaviour and flow pattern in the deposition zone. In this study, five different pin geometries are presented and investigated using a three-dimensional (3D) computational fluid dynamics (CFD) model. A user-defined function (UDF) was used to calculate strain- and temperature-dependent viscosity. …

Experimental Investigation Of Internal Flow Structures And Permeate Flux Behaviour In Direct Contact Membrane Distillation, Ali Kandi, Mehdi Khiadani, Yujie Yuan, Abdellah Shafieian

Research outputs 2022 to 2026

Understanding internal hydrodynamics of Direct Contact Membrane Distillation (DCMD) is crucial for desalination performance and mitigating temperature polarization. Despite its importance, quantitative experimental characterization of hydrodynamics governing boundary-layer development and transport enhancement within DCMD channels remains limited. This study presents the first spatially resolved Particle Image Velocimetry PIV measurement of velocity fields inside a smooth, actively operating flat-sheet DCMD channel, linking observed hydrodynamic structures to permeate flux. Two-dimensional velocity fields were captured at different streamwise positions for feed flow rates ranging from 0.7 to 4 L·min−1, enabling analysis of flow evolution and turbulence characteristics relevant to mass transfer. The results …

Modeling Preannouncement And Launch Timing Decisions For Product Line Extension Under Innovation Uncertainty And Resource-Sharing Dual-Sourcing Supply Chain, Adewole Adegbola, Venkat Allada

Engineering Management and Systems Engineering Faculty Research & Creative Works

This research seeks to develop a system dynamics (SD) model to address challenges associated with the product line extension (PLE) problem. In this work, we specifically consider the "Two-Generation Product Line Extension (TGPLE) Problem", where a firm introduces a product variant in the market and plans to extend the launch to include a newer generation product in a resource-sharing dual-sourcing (RSDS) environment. We begin by identifying the key factors that influence product line extension, and we developed a TGPLE-RSDS construct based on three (3) inter-related systems: Market System, Production System and Supply Chain System. The proposed TGPLE-RSDS construct also illustrates …

Exploring The Role Of Single Pilot Operations In Night Cargo Aviation: A Phenomenological Study, Kollin Ellis

Doctoral Dissertations and Projects

The purpose of this qualitative descriptive phenomenology was to examine the lived experiences of pilots who have operated alone at night in an aviation cargo environment. The design was guided by an epistemological philosophical assumption. Extreme advancements in the technology of artificial intelligence and their applications in the aviation industry have raised the question of the value of a single operator serving in commercial cockpits. Some experts argue that a two-person crew is unnecessarily redundant. A significant gap in the current literature of experiential data existed to prompt this study. Through the contextual lens of the Dual Process Theory and …

Comparison Of Gravity Separation And Flotation For Pyrite Recovery From Tailings, Gülay Bulut, Gönül Göksu Gökçe, Dilruba Karamanlı, Oğuzhan Mert Gürkan, Ergin Sarp Zenzirci, Binnur Kırım, Alim Gül

Journal of Sustainable Mining

Tailings generated during the production of lead, zinc, and copper concentrates contain significant amounts of sulfide minerals, particularly pyrite. Pyrite, which remains in tailings after the recovery of other metals, is one of the main contributors to acid mine drainage (AMD). Therefore, recovering pyrite from tailings is environmentally and economically an important issue. In this study, pyrite recovery from tailings was investigated using flotation and gravity separation methods. Mineralogical characterization and liberation analyses were conducted by Mineral Liberation Analysis (MLA), indicating that pyrite is the major sulfide mineral with over 76% liberation degree even in the coarsest fractions. Flotation tests …

Common Ground Newsletter Fall 2026, Missouri University Of Science And Technology

Common Ground

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- Design Team Results

Cuip-X25: A Real-World Network Intrusion Dataset For Next-Generation Ai-Driven Security, Arshad Iqbal, Sohail Asghar

Turkish Journal of Electrical Engineering and Computer Sciences

The efficacy of artificial intelligence (AI) in intrusion detection systems (IDS) is critically dependent on high-fidelity training data. However, as detailed in the manuscript's literature review, existing benchmark datasets are predominantly synthetic, outdated, or imbalanced and fail to capture the complexity of the contemporary threat landscape. To bridge this gap, this study introduces CUIP-X25, a novel real-world cyber-attack dataset captured over a four-month period using a dionaea honeypot deployed on a public network. Unlike synthetic alternatives, this dataset provides an authentic representation of modern adversarial tactics, techniques, and procedures, encompassing 3.16 million real events across ten distinct attack categories, including …

Boundary Layer Sliding Mode Control Strategy For Variable-Speed Compressor In Deep Freezers: Experimental Validation Of Energy Efficiency And Freezing Capacity, Sertan Aksoy, Kami̇l Çeti̇n, Sezai̇ Taşkin

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents the design and experimental validation of a nonlinear sliding mode controller developed for a deep freezer equipped with a variable-speed compressor. The proposed control strategy aims to minimize energy consumption while maintaining rapid and stable cooling performance under varying ambient conditions. A detailed thermal model of the deep freezer was established using an equivalent resistance–capacitance network representation, enabling precise analysis of temperature dynamics. The sliding mode-based control algorithm dynamically adjusts the compressor’s operating frequency according to temperature deviation, ambient conditions, and time-dependent factors, providing robust performance without requiring parameter retuning for different models. Beyond theoretical-based analysis, a …