03. Bachelor's Thesis
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Item Open Access Design of an Industrial Plant for Production of Monoethanolamine Solution for CO2 Capture in Kazakhstan(Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Ziyabek, Alikhan; Zhaidarbek, Balnur; Kongilkosh, Bek; Jexenov, Daniyar; Yessengaziyeva, KarinaKazakhstan's significant energy reserves, particularly in crude oil and coal, position it as a major global energy producer. However, the expansion of these natural resources has led to significant environmental degradation. To achieve carbon neutrality by 2060, this Capstone project focuses on designing an efficient and cost-effective production process for a monoethanolamine (MEA) solution, crucial for carbon capture. The project covers selecting the optimal manufacturing pathway, product specifications, reaction kinetics, catalyst choices, equipment design, plant location, and economic analysis. MEA will be produced by reacting ammonia with ethylene oxide using water as a catalyst, targeting an annual production of 68.5 kilotonnes of 30 wt% MEA solution. Validated through Aspen and Python-based simulations, the design includes detailed process flow diagrams and equipment specifications. The plant, to be located in the Pavlodar region, shows a net present value of USD 149.5 million with a 4.15-year payback period. Future enhancements could involve producing diethanolamine (DEA) and triethanolamine (TEA), ammonia recycling, and water use optimization.Item Open Access Parking Assistance Application using IoT Devices and Machine Learning on the Edge(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-20) Amangeldina, Aruzhan; Tuleshov, Zhanabek; Baimukhanov, Batyrkhan; Saduakhas, RuanaTraditional parking management systems are inefficient in monitoring large scaled parking slot. This study addresses the growing issue of urban parking inefficiency, where drivers waste significant time searching for vacant spots. This problem is tackled by deploying edge devices in the parking lot, equipped with compact convolutional neural networks for real-time analysis. To evaluate performance, three models (mAlexNet224, mAlexNet32, and Mohan's model) were chosen from other research or adapted for this task. These models were then tested in a simulated parking lot environment on OpenMV or Raspberry Pi depending on the model's input size. While mAlexNet224 on Raspberry Pi achieved the best accuracy among the tested models, other lightweight models running on OpenMV also delivered satisfactory results, exceeding 90\% in accuracy. Based on the results of the study, mAlexNet32 and Mohan's model on OpenMV's camera appear well-suited for real-world deployment and the affordability of OpenMV devices further strengthens this case.Item Open Access Deep Learning-Based Wind Speed Prediction for Optimized Wind Turbine Operation(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-03) Alimukhambetova, Sofiya; Kochkarova, AyanaWind energy has been a promising source of clean energy that does not negatively affect our environment. Because of the fluctuations in wind speed, it is crucial to predict its values for wind turbines to have the maximum effective power output. This project aims to develop a way for short-term wind speed prediction based on deep learning technologies, such as CNN, LSTM, RNN, and GRU models, alone and in combination. Through iterative experimentation and evaluation, we develop ten final models and assess their performance based on Mean Squared Error (MSE), score, and computational efficiency. Our findings reveal that the GRU model achieves the highest performance with a MSE of 0.00238 m/s and R2 score of 0.8796. Additionaly, the similarly structured LSTM model demonstrates superior computational efficiency along with high R2 value, outperforming GRU model. By examining the performance of multiple deep learning architectures, the project seeks to identify the most suitable approach for wind speed prediction, thereby facilitating more efficient and sustainable utilization of wind resources for power generation.Item Restricted ADAPTING TO LEARNER’S COGNITIVE DIFFERENCES USING REINFORCEMENT LEARNING(Nazarbayev University School of Engineering and Digital Sciences, 2023) Nurgazy, Symbat; Issa, Ilyas; Kassymbekov, Saparkhan; Kuangaliyev, ZholamanItem Open Access FINAL PROJECT REPORT DOCUMENT– SPRING 2024(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-20) Duzelbay, Alisher; Bakkali, Nurseiit; Issa, Yeldar; Serikbayev, Yermakhan; Bulatov, YernurThe NU Life Hub project aims to address the fragmentation of information and resources for Nazarbayev University (NU) students by offering a centralized platform designed specifically for their needs. The project addresses students' challenges in finding and participating in campus activities, accessing a convenient marketplace to fulfill their needs, and staying informed about various campus events. The NU Life Hub solution will be a comprehensive platform that combines event management, marketplace, and community engagement functions to improve the overall university experience. During the project, extensive research was conducted to understand existing solutions and approaches to address similar challenges university communities face. This analysis informed the design and development of the NU Life Hub, ensuring that best practices were embedded into the platform and critical challenges were effectively addressed.Item Open Access DEVELOPMENT OF BRAIN-BASED SMART-HOME/TYPING SYSTEM(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Yergaliyeva, Aiana; Berikbol, Arnur; Seiilkhan, ArsenOur project combines EEG-EOG signals to develop an efficient Brain-Computer Interface (BCI) spelling system for Virtual Reality (VR) and Mixed Reality (MR) environments. This hybrid speller enables users to spell using brain activity by leveraging multi-modal signals and various classification strategies. Aimed at improving the quality of life for individuals with motor disabilities, such as spinal cord injuries, ALS, locked-in syndrome, and the elderly, our BCI system provides an alternative communication channel. Focusing on the well-established P300 Row-Column (RC) speller paradigm, we incorporate convolutional neural network (CNN) classification techniques for enhanced performance. Additionally, we use mixed reality glasses to improve user comfort and EEG signal quality. Our methodology includes comprehensive experimental procedures, from environment setup to data analysis and iterative refinement. By advancing BCI technology and integrating VR and MR interfaces, our project seeks to promote accessibility and inclusivity, enabling individuals of all abilities to communicate and participate more fully in social, educational, and professional activities.Item Restricted DESIGN OF HIGH-RISE HOTEL BUILDING IN RIVERSIDE, CALIFORNIA, USA(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-12) Aldabergenuly, Magzum; Galymzhankyzy, Anel; Kussainova, Zhanna; Kydyrali, Tolezhan; Temirbekov, Doszhan; Ualiyev, DulatThis capstone project report summarizes our team’s efforts in developing a high-rise hotel in Riverside, California, within a strong wind and seismic zone. Our multidisciplinary approach divided the project into Architectural, Structural & Materials (40%), Geotechnical (30%), Construction Management (15%), and Environmental Engineering (15%) areas. The project's primary challenge was identifying necessary parameters and adhering to professional design procedures. Through a combination of self-study and mentoring, we conducted independent research and literature reviews to set and achieve specific goals in each project area. Our process involved planning the design using knowledge from courses, secondary sources, and faculty guidance, leading to a synthesized and customized design approach. Key accomplishments include load identification, layout design, preliminary member and force-resisting system design, computer modeling (S), suitable foundation type identification and preliminary foundation design (G), project management method establishment, cost analysis, scheduling, risk management (M), and waste generation rate and composition identification (E). Despite challenges in knowledge gaps and sourcing information, we successfully met our objectives, providing a solid foundation for further project development.Item Restricted "MOMENTUM MAYHEM" 3D PUZZLE GAME PROTOTYPE(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-21) Yerzhanuly, Azamat; Kabykesh, Dias; Ussoltsev, Vladimir; Serik, Yernar; Rysbek, Yelnaz"Momentum Mayhem" is a 3D puzzle game prototype that aims to develop physics concepts in the game environment. The project comprises four stages: ideation, pre-production, production, and post-production. The ideation phase involved analyzing popular physics-based games such as "Fall Guys" and "We Were Here" to inspire unique gameplay concepts. In the pre-production phase, the team outlined the game's requirements and designed its architecture. The production phase focused on creating and integrating essential game components, including character controllers, environmental objects, UI/UX, level maps, and multiplayer functionality. "Momentum Mayhem" leverages Unity for its game engine and Photon PUN 2 for multiplayer networking. The game features physics-based puzzles requiring cooperative gameplay, aiming to enhance problem-solving skills and teamwork. This project highlights the growing game development scene in Kazakhstan by showcasing creative gameplay and technical skills. The document further elaborates on the background, related work, project approach, execution, and evaluation of the system.Item Restricted MOTION PLANNING WITH OBSTACLE AVOIDANCE FOR ROBOT MANIPULATORS VIA DEEP REINFORCEMENT LEARNING(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-03) Sadykov, Zhengisbek; Khussainov, TamerlanThe integration of Deep Reinforcement Learning (DRL) in robotic motion planning represents a cutting-edge approach to enhancing the adaptability and efficiency of robotic manipulators in complex environments. In this project we trained a UR5 manipulator for autonomous navigation within a 2D environment. Our methodology hinges on the Stable Baselines 3 library and Proximal Policy Optimization (PPO) algorithms, grounded within the PyBullet and Gym simulation platforms. The culmination of our research affirms the thesis that it is indeed feasible to train a manipulator to proficiently navigate a 2D environment using DRL. The implications of this work not only bolster the potential for practical applications in various domains but also pave the way for further advancements in the field of robotics.Item Open Access COMPUTATIONAL ANALYSIS OF FLUID STRUCTURE INTERACTION (FSI) IN HORIZONTAL AXIS WIND TURBINES (HAWTS)(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-02) Makshatov, Madi; Nurzhanova, Diana; Sataibekova, Aruzhan; Kussinov, ZhanibekWind power plays a crucial role in the worldwide shift towards sustainable and renewable sources of energy. Wind turbine power generation performances made them a widely adopted method for electricity production, playing a crucial role in the world’s energy resources. Accordingly, optimizing the wind turbine blade’s design is essential for increasing wind turbine performance and reducing expenses. The main aim of this capstone project is to analyze the Fluid Structure Interaction of the HAWTs and to achieve the most effective design of the turbine, in terms of power generation performance and resource requirement by optimizing the blades using low fidelity methods. In engineering and scientific studies, low-fidelity and high-fidelity simulation and optimization have become common concepts, especially in the field of wind turbine design and analysis. These concepts are essential in order to study computational structure and controlling the resource demand, as well as improving the operation of the turbines. This paper focuses on applying low-fidelity optimization techniques with QBlade, which is a commonly used open-source software for creating aerodynamic simulations of horizontal axis wind turbines. A low-fidelity simulation can involve simplified fluid dynamics calculations and simplified structural models, in the context of wind turbine design, in order to forecast the turbine’s effectiveness. Compared to the high-fidelity simulations, low fidelity simulations are economically and computationally reasonable. It means that, in the process of optimization of design parameters of the wind turbine more design alternatives are available in order to reach the most effective parameters. Computational Analysis of Fluid-Structure Interaction (FSI) within Horizontal Axis Wind Turbines (HAWTs) will be studied on the NREL 5MW and NREL Phase VI turbine. In order to get the optimization results of these wind turbine blades, low fidelity optimization methods, such as Betz and Schmitz theories will be used.Item Open Access EXPERIMENTAL INVESTIGATION OF DATA TRANSMISSION USING POWERLINE COMMUNICATION(Nazarbayev University School Of Engineering and Digital Sciences, 2024-07-04) Kareibayeva, Gulzat; Kizilirmak, RefikPowerline Communication (PLC) has emerged as a promising technology for data transmission, offering a cost-effective and versatile solution for various applications. The primary objective of the project is to assess the performance of PLC network through the development of experimental setups using PLC adapters. The study tests PLC under different scenarios in real-world conditions. As a result of this project, conclusions were made whether the performance of PLC network is comparable to that of Ethernet network.Item Open Access SHYN: MULTILINGUAL MULTIASPECT MEDIA PROFILING(Nazarbayev University School of Engineering and Digital Sciences, 2024) Orel, Daniil; Khamitov, Rakhat; Kazymbetov, Abylaikhan; Mars, Shynar; Tyler, Ben; Yazici, AdnanWith the rapid increase in the volume of information generated on the web, the issue of misinformation has become more pronounced. Consequently, the ability to assess the factuality of reporting by various web sources, on par with understanding their political bias and attempts to manipulate the reader's decisions has emerged as a critical need. Presently, the majority of existing systems predominantly cater to English language sources, leaving a significant gap in multi-language coverage. To address this, we are introducing Shyn (derived from the Kazakh word Shyn meaning "the truth") - an innovative online tool designed for media profiling across ten major languages: English, Chinese, Hindi, Kazakh, Italian, French, Korean, Spanish, German and Russian. This tool represents a significant step forward in supporting diverse linguistic contexts in the critical task of discerning accurate information on the internetItem Embargo ENERGY EFFICIENT CLOCK SYNCHRONIZATION IN IOT USING REINFORCEMENT LEARNING(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Nadirkhanova, Aizhuldyz; Assylbek, DamirThis project addresses the vital challenge of achieving precise clock synchronization within the Internet of Things (IoT), a foundational element for the seamless and efficient operation of interconnected devices. Such synchronization is indispensable for critical IoT functions like coordinated actions, streamlined communication, and power management. The project introduces a novel approach leveraging Reinforcement Learning (RL), specifically the State-Action-Reward-State-Action (SARSA) algorithm. This method equips devices with the capability to autonomously learn and anticipate the timing of data transmissions, fostering self-synchronization without manual intervention or pre-programmed schedules. It's a significant shift from traditional manual adjustments of clock drift, accommodating the unique timing characteristics of each device's crystal oscillator. Small testbeds with ESP32 devices using the ESP-NOW protocol have validated the approach's adaptability to transmission timing variances, maintaining a high success rate in data receipt. Furthermore, the project continues to work on incorporating knowledge transfer techniques and Huffman coding to compress trained data, facilitating rapid convergence to optimal behavior and fostering an environment where devices benefit from shared learning experiences.Item Open Access MOVABLE ALIPBI(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Zhalgasbayeva, Dilyara; Zhuzimkhan, Magzhan; Amirkhanov, Dulat; Turar, AslanbekMovable Alipbi is an iPad application designed to address the handwriting difficulties faced by children with dysgraphia syndrome, enhancing their cognitive development and handwriting skills. This project, targeting children aged 5-12, provides engaging, gamified exercises tailored to individual learning needs in handwriting. The application is a user-friendly, effective solution that includes interactive gaming elements to promote handwriting improvement in kids.Item Open Access ONLINE TUTORING MARKETPLACE(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Konkayeva, Bibigul; Amangeldi, Aruay; Yerkemblan, Dilnas; Abdrakhmanov, ZhassulanThe goal of this project was to develop an online marketplace to connect tutors and students. This addressed issues of inconvenience and high time consumption of the traditional methods. Our delivered solution is a web and mobile application, implemented on React, Java, and Flutter for an effective student-tutor match. The platform functionality also enables online class scheduling and user-wise communication. The developed solution fulfills the objectives of creating an affordable and convenient tutoring platform.Item Restricted DEVELOPMENT OF ULTRA-HIGH PERFORMANCE GEOPOLYMER MORTAR FOR CONSTRUCTION 3D PRINTING(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-02) Nurgaliuly, Dias; Kanay, Symbat; Manarbekuly, TangirbergenThe construction industry faces a pressing need for sustainable materials that offer superior mechanical properties, durability, and printability. This research addresses this need by developing an ultra-high-performance geopolymer mortar specifically tailored for construction 3D printing. The study systematically identifies and evaluates suitable materials for geopolymer mortar through an extensive literature review and material selection process. A design of experiments (DOE) approach is employed to vary key parameters in geopolymer mortar formulations, followed by rigorous experimental validation. The research methodology involves the preparation of three different geopolymer compositions (G1, G2, G3) and testing for setting time, compressive strength, flexural strength, and buildability. Buildability testing includes viscosity analysis, shape retention testing, and extrudability testing to evaluate suitability for 3D printing applications. The study also tests 3D printed geopolymers for compressive strength and conducts a comparative analysis of composition development. Main research results indicate that G2 consistently exhibits the highest compressive and flexural strength across all tested durations, with shorter setting times compared to G1 and G3. However, printed geopolymers demonstrate lower compressive strength compared to molded counterparts, with G2 showing relatively higher retention. Additionally, G2 displays more favorable viscosity characteristics and good extrudability, suggesting suitability for smoother extrusion and better layer formation during printing. The findings of this research have significant industrial implications, offering a sustainable alternative to conventional construction materials. The development of ultra-high-performance geopolymer mortar for 3D printing applications enables faster and more efficient construction processes while reducing material wastage and enhancing structural performance. This research contributes to advancements in sustainable construction practices, paving the way for a more environmentally friendly and economically viable future in the construction industry.Item Open Access MOBILE APP FOR DIABETES TYPE 1 MONITORING DEVELOPED FOR IMPROVED UX(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-01) Tuishieva, Aigerim; Syurmen, YussufkadirOur project aimed at improving functionalities of mobile apps dedicated to people with diabetes to help users with their diabetes management and decrease attrition rate. Ultimately, we developed a cross-platform mobile app with three completely novel features: drag & drop data logging, incorporated an ML model for visualization of diabetes data and a rule-based chatbot for monitoring the diabetes progress. Finally, we evaluated the app by collecting responses from a person with T1D (one of the team members’ younger brother, who is 19 years old) on a questionnaire.Item Restricted DESIGN OF 12-STOREY “PARKVIEW” HOTEL IN SAN FRANCISCO, USA(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-12) Argimbayeva, Zhanat; Baimbayeva, Madina; Narbekova, Aiisha; Nygmetova, Damira; Omarova, Zhaniya; Zhangeldi, RufiyaThe project proposes the design of a 12-storey “Park-View” hotel located at 2550 Irving Street in San Francisco, USA. The main design considerations are seismicity hazard and wind load. Geotechnical design focused on conducting a comparison of pile types and finalizing a foundation layout featuring three different pile groups to accommodate varying column loads. Construction management is mainly focused on the analysis of cost-benefit, quality assessment, construction site layout and achieving a shorter payback period.Item Restricted IOT TELEGRAM BOT NETWORK DEPLOYMENT AND MEASUREMENTS(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Bolatov, Adlet; Salym, AdilThis project was done to resolve connectivity inconsistencies encountered while transmitting data from devices with temperature and luminosity sensors to a Telegram bot due to instability in WiFi connection. Consequently, Long Range (LoRa) technology was implemented in this project due to its relevance for low-power and wide-area radio communication. The project lifetime lasted from September 2023 to April 2024, in a total of 7 months together with a 1 month break during December and January. Initially esp32 and Pycom LoPy4 were used as devices, however later we switched to both of them to be esp32. Sender device reads data from temperature and luminosity sensors and sends them to the receiver. Aside from that, the receiver device uses OpenWeatherMap API to get the outside temperature in Astana with a given longitude and latitude. Additional functionality has been added to this project like connection to OpenAI API and machine learning implementation. For the OpenAI API part, the receiver device has been connected to the API to access ChatGPT queries and get answers for questions from it. The ML model implementation uses data gathered from the telegram bot for the past 10 months, which is stored in .csv format. At the end of the day the receiver device connects to OpenWeatherMap API and gets predictions for morning and afternoon temperatures outside of university, using these temperatures and ML model, it posts predicted temperatures for atrium and outside to telegram bot. In the end, multiple tests were conducted with LoRa and other functionality, so that one user uses a sender device and another a receiver device. The connection was tested from different spots, and the main spot for receiver was C4 block in Nazarbayev University, and main spot for sender was green spot in the atrium of university. The connection resulted in a range of -94 to -97 RSSI value which is acceptable for LoRa connection, solving the main problem of the project.Item Open Access PERFORMANCE IMPROVEMENT OF RADAR IMAGING USING MACHINE LEARNING TECHNIQUES(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Zhumabekov, Yertase project aims to facilitate the progress of radarbased or complemented computer vision. The objective of the project is to perform a multi-purpose radar system that can complete object recognition and classification tasks. The machine learning techniques are used to carry out analysys of vast amount of data generated by the radar.