5,432 research outputs found

    Applications of Machine Learning to Optimizing Polyolefin Manufacturing

    Full text link
    This chapter is a preprint from our book by , focusing on leveraging machine learning (ML) in chemical and polyolefin manufacturing optimization. It's crafted for both novices and seasoned professionals keen on the latest ML applications in chemical processes. We trace the evolution of AI and ML in chemical industries, delineate core ML components, and provide resources for ML beginners. A detailed discussion on various ML methods is presented, covering regression, classification, and unsupervised learning techniques, with performance metrics and examples. Ensemble methods, deep learning networks, including MLP, DNNs, RNNs, CNNs, and transformers, are explored for their growing role in chemical applications. Practical workshops guide readers through predictive modeling using advanced ML algorithms. The chapter culminates with insights into science-guided ML, advocating for a hybrid approach that enhances model accuracy. The extensive bibliography offers resources for further research and practical implementation. This chapter aims to be a thorough primer on ML's practical application in chemical engineering, particularly for polyolefin production, and sets the stage for continued learning in subsequent chapters. Please cite the original work [169,170] when referencing

    Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models

    Get PDF
    One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as xylene soluble, particle size distribution and melt flow index (MFI). As an alternative to the online instruments and conventional laboratory tests, these properties can be estimated using model based-soft sensor. This paper presents models for soft sensors to measure MFI in industrial polypropylene loop reactors using artificial neural network (ANN) model, serial hybrid neural network (HNN) model and stacked neural network (SNN) model. All models were developed and simulated in MATLAB. The simulation results of the MFI based on the ANN, HNN, and SNN models were compared and analyzed. The MFI was divided into three grades, which are A (10-12g/10 min), B (12-14g/10 min) and C (14-16 g/10 min). The HNN model is the best model in predicting all range of MFI with the lowest root mean square error (RMSE) value, 0.010848, followed by ANN model (RMSE=0.019366) and SNN model (RMSE=0.059132). The SNN model is the best model when tested with each grade of the MFI. It has shown lowest RMSE value for each type of MFI (0.012072 for MFI A, 0.017527 for MFI B and 0.015287 for MFI C), compared to HNN model (0.014916 for MFI A, 0.041402 for MFI B and 0.046437 for MFI C) and ANN model (0.015156 for MFI A, 0.076682 for MFI B, and 0.037862 for MFI C)

    Bidirectional optimization of the melting spinning process

    Get PDF
    This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities

    Multi-tier framework for the inferential measurement and data-driven modeling

    Get PDF
    A framework for the inferential measurement and data-driven modeling has been proposed and assessed in several real-world application domains. The architecture of the framework has been structured in multiple tiers to facilitate extensibility and the integration of new components. Each of the proposed four tiers has been assessed in an uncoupled way to verify their suitability. The first tier, dealing with exploratory data analysis, has been assessed with the characterization of the chemical space related to the biodegradation of organic chemicals. This analysis has established relationships between physicochemical variables and biodegradation rates that have been used for model development. At the preprocessing level, a novel method for feature selection based on dissimilarity measures between Self-Organizing maps (SOM) has been developed and assessed. The proposed method selected more features than others published in literature but leads to models with improved predictive power. Single and multiple data imputation techniques based on the SOM have also been used to recover missing data in a Waste Water Treatment Plant benchmark. A new dynamic method to adjust the centers and widths of in Radial basis Function networks has been proposed to predict water quality. The proposed method outperformed other neural networks. The proposed modeling components have also been assessed in the development of prediction and classification models for biodegradation rates in different media. The results obtained proved the suitability of this approach to develop data-driven models when the complex dynamics of the process prevents the formulation of mechanistic models. The use of rule generation algorithms and Bayesian dependency models has been preliminary screened to provide the framework with interpretation capabilities. Preliminary results obtained from the classification of Modes of Toxic Action (MOA) indicate that this could be a promising approach to use MOAs as proxy indicators of human health effects of chemicals.Finally, the complete framework has been applied to three different modeling scenarios. A virtual sensor system, capable of inferring product quality indices from primary process variables has been developed and assessed. The system was integrated with the control system in a real chemical plant outperforming multi-linear correlation models usually adopted by chemical manufacturers. A model to predict carcinogenicity from molecular structure for a set of aromatic compounds has been developed and tested. Results obtained after the application of the SOM-dissimilarity feature selection method yielded better results than models published in the literature. Finally, the framework has been used to facilitate a new approach for environmental modeling and risk management within geographical information systems (GIS). The SOM has been successfully used to characterize exposure scenarios and to provide estimations of missing data through geographic interpolation. The combination of SOM and Gaussian Mixture models facilitated the formulation of a new probabilistic risk assessment approach.Aquesta tesi proposa i avalua en diverses aplicacions reals, un marc general de treball per al desenvolupament de sistemes de mesurament inferencial i de modelat basats en dades. L'arquitectura d'aquest marc de treball s'organitza en diverses capes que faciliten la seva extensibilitat aixรญ com la integraciรณ de nous components. Cadascun dels quatre nivells en que s'estructura la proposta de marc de treball ha estat avaluat de forma independent per a verificar la seva funcionalitat. El primer que nivell s'ocupa de l'anร lisi exploratรฒria de dades ha esta avaluat a partir de la caracteritzaciรณ de l'espai quรญmic corresponent a la biodegradaciรณ de certs compostos orgร nics. Fruit d'aquest anร lisi s'han establert relacions entre diverses variables fรญsico-quรญmiques que han estat emprades posteriorment per al desenvolupament de models de biodegradaciรณ. A nivell del preprocรฉs de les dades s'ha desenvolupat i avaluat una nova metodologia per a la selecciรณ de variables basada en l'รบs del Mapes Autoorganitzats (SOM). Tot i que el mรจtode proposat selecciona, en general, un major nombre de variables que altres mรจtodes proposats a la literatura, els models resultants mostren una millor capacitat predictiva. S'han avaluat tambรฉ tot un conjunt de tรจcniques d'imputaciรณ de dades basades en el SOM amb un conjunt de dades estร ndard corresponent als parร metres d'operaciรณ d'una planta de tractament d'aigรผes residuals. Es proposa i avalua en un problema de predicciรณ de qualitat en aigua un nou model dinร mic per a ajustar el centre i la dispersiรณ en xarxes de funcions de base radial. El mรจtode proposat millora els resultats obtinguts amb altres arquitectures neuronals. Els components de modelat proposat s'han aplicat tambรฉ al desenvolupament de models predictius i de classificaciรณ de les velocitats de biodegradaciรณ de compostos orgร nics en diferents medis. Els resultats obtinguts demostren la viabilitat d'aquesta aproximaciรณ per a desenvolupar models basats en dades en aquells casos en els que la complexitat de dinร mica del procรฉs impedeix formular models mecanicistes. S'ha dut a terme un estudi preliminar de l'รบs de algorismes de generaciรณ de regles i de grafs de dependรจncia bayesiana per a introduir una nova capa que faciliti la interpretaciรณ dels models. Els resultats preliminars obtinguts a partir de la classificaciรณ dels Modes d'acciรณ Tรฒxica (MOA) apunten a que l'รบs dels MOA com a indicadors intermediaris dels efectes dels compostos quรญmics en la salut รฉs una aproximaciรณ factible.Finalment, el marc de treball proposat s'ha aplicat en tres escenaris de modelat diferents. En primer lloc, s'ha desenvolupat i avaluat un sensor virtual capaรง d'inferir รญndexs de qualitat a partir de variables primร ries de procรฉs. El sensor resultant ha estat implementat en una planta quรญmica real millorant els resultats de les correlacions multilineals emprades habitualment. S'ha desenvolupat i avaluat un model per a predir els efectes carcinรฒgens d'un grup de compostos aromร tics a partir de la seva estructura molecular. Els resultats obtinguts desprรจs d'aplicar el mรจtode de selecciรณ de variables basat en el SOM milloren els resultats prรจviament publicats. Aquest marc de treball s'ha usat tambรฉ per a proporcionar una nova aproximaciรณ al modelat ambiental i l'anร lisi de risc amb sistemes d'informaciรณ geogrร fica (GIS). S'ha usat el SOM per a caracteritzar escenaris d'exposiciรณ i per a desenvolupar un nou mรจtode d'interpolaciรณ geogrร fica. La combinaciรณ del SOM amb els models de mescla de gaussianes dona una nova formulaciรณ al problema de l'anร lisi de risc des d'un punt de vista probabilรญstic

    Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization

    Full text link
    Laser-directed-energy deposition (DED) offers advantages in additive manufacturing (AM) for creating intricate geometries and material grading. Yet, challenges like material inconsistency and part variability remain, mainly due to its layer-wise fabrication. A key issue is heat accumulation during DED, which affects the material microstructure and properties. While closed-loop control methods for heat management are common in DED research, few integrate real-time monitoring, physics-based modeling, and control in a unified framework. Our work presents a digital twin (DT) framework for real-time predictive control of DED process parameters to meet specific design objectives. We develop a surrogate model using Long Short-Term Memory (LSTM)-based machine learning with Bayesian Inference to predict temperatures in DED parts. This model predicts future temperature states in real time. We also introduce Bayesian Optimization (BO) for Time Series Process Optimization (BOTSPO), based on traditional BO but featuring a unique time series process profile generator with reduced dimensions. BOTSPO dynamically optimizes processes, identifying optimal laser power profiles to attain desired mechanical properties. The established process trajectory guides online optimizations, aiming to enhance performance. This paper outlines the digital twin framework's components, promoting its integration into a comprehensive system for AM.Comment: 12 Pages, 10 Figures, 1 Table, NAMRC Conferenc

    Automatic Features Extraction From Time Series Of Passive Microwave Images For Snowmelt Detection Using Deep-Learning โ€“ A Bidirectional Long-Short Term Memory Autoencoder (Bi-Lstm-Ae) Approach.

    Get PDF
    The Antarctic surface snowmelt is prone to the polar climate and is common in its coastal regions. With about 90 percent of the planet\u27s glaciers, if all of the Antarctica glaciers melted, sea levels will rise about 58 meters around the planet. The development of an effective automated ice-sheet snowmelt monitoring system is therefore crucial. Microwave remote sensing instruments, on the one hand, are very sensitive to snowmelt and can see day and night through clouds, allowing us to distinguish melting from dry snow and to better understand when, where, and for how long melting has taken place. On the other hand, deep-learning (DL) algorithms, which can learn from linear and non-linear data in a hierarchical way robust representations and discriminative features, have recently become a hotspot in the field of machine learning and have been implemented with success in the geospatial and remote sensing field. This study demonstrates that deep learning, particularly long-short memory autoencoder architecture (LSTM-AE) is capable of fully exploiting archives of passive microwave time series data. In this thesis, An LSTM-AE algorithm was used to reduce and capture essential relationships between attributes stored as brightness temperature within pixel time series and k-means clustering is applied to cluster the leaned representations. The final output map highlights the melt extent in Antarctica

    ํ™”ํ•™ ๋ฐ˜์‘๊ธฐ ์‹œ์Šคํ…œ์„ ๋ชจ๋ธ๋งํ•˜๊ธฐ ์œ„ํ•œ ๊ณ„์‚ฐ ํšจ์œจ์ ์ธ ์ „๋žต

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€(์—๋„ˆ์ง€ํ™˜๊ฒฝ ํ™”ํ•™์œตํ•ฉ๊ธฐ์ˆ ์ „๊ณต), 2022.2. ์ด์ข…๋ฏผ.Many researchers in chemical engineering have been using analytical and computational models to predict the behaviors of systems and use these models to process optimization, design and control. However, until now, researchers are still forced to compromise on model fidelity and accuracy. Although high fidelity models can improve the model accuracy, simulating these models is usually time-consuming, making it difficult to perform optimization. In this thesis, computationally efficient strategies about two system are introduced which can maintain model fidelity but effectively reduce the calculation time. Polymer reactor is selected for the first system and we focused on polymer kinetics. A hybrid approach that combines the method of moments and Monte Carlo simulation to predict the molecular weight distribution of low-density polyethylene for a continuous stirred tank reactor system is proposed. A 'Block,' which is repeating reaction group, is introduced for the calculation cost-effective simulation. This model called the 'block Kinetic Monte Carlo' is ~10 to 32 times faster than Neuhausโ€™s model. The model can be applied to any steady state system and provide a calculation cost reduction effect, where one reaction is much faster than others; for example, the propagation reaction. Furthermore, we perform a case study on the effects of the system temperature and initiator concentration on the MWD and reaction rate ratio. Based on the simulation results of 180 case studies, we determine a quantitative guideline for the appearance of shoulder, which is a function of the rate ratio of reactions to the propagation reaction. Computational fluid dynamics (CFD) based reactor system is selected for the second system. CFD is an essential tool for solving engineering problem that involves fluid dynamics. Especially in chemical engineering, fluid motion usually has extensive effects on system states such as temperature and component concentration. However, due to the critical issue of long computational times for simulating CFD, application of CFD is limited for many real-time problems such as real-time optimization and process control. In this study, we develop the surrogate model of Continuous stirred tank reactor (CSTR) with Van de Vusse reaction using Physics-informed neural network (PINN) which can train the governing equations of system. We propose PINN architecture that can train every governing equation which chemical reactor system follows and can train multi-reference frame system. Also, we investigate that PINN can resolve the problem of neural network that needs lots of training data, are easily overfitted and cannot contain physical meaning. Furthermore, we modify the original PINN suggested by Raissi in order to solve the memory error and divergence problem with two methods: (1) Mini-batch training; (2) Weighted loss function. We also suggest a similarity based sampling strategy where the accuracy can be improved up to 5 times over the random sampling. This work can provide the guideline for developing the high performance surrogate model of chemical process.ํ™”ํ•™ ๊ณตํ•™ ๋ถ„์•ผ์˜ ๋งŽ์€ ์—ฐ๊ตฌ์ž๋“ค์€ ๋ถ„์„ ๋ฐ ๊ณ„์‚ฐ ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ์‹œ์Šคํ…œ์˜ ๊ฑฐ๋™์„ ํ•ด์„ํ•˜๊ณ  ์ตœ์ ํ™”, ์„ค๊ณ„ ๋ฐ ์ œ์–ด๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๋ชจ๋ธ์˜ ์ •ํ™•๋„์™€ ๊ณ„์‚ฐ์‹œ๊ฐ„์€ ๊ฑฐ๋ž˜๋˜๋Š” ๊ด€๊ณ„์— ์žˆ์–ด ๊ณ„์‚ฐ์‹œ๊ฐ„์ด ์˜ค๋ž˜๊ฑธ๋ฆฌ๋Š” ๋ฌธ์ œ ๋•Œ๋ฌธ์— ๋ชจ๋ธ์˜ ์ •ํ™•๋„๋ฅผ ํƒ€ํ˜‘ํ•  ์ˆ˜ ๋ฐ–์— ์—†๋Š” ์‹ค์ƒ์ด๋‹ค. ์ด ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ๋‘ ์‹œ์Šคํ…œ์— ๋Œ€ํ•ด ๋ชจ๋ธ์˜ ์ถฉ์‹ค๋„๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ๋„ ๊ณ„์‚ฐ์‹œ๊ฐ„์„ ํšจ๊ณผ์ ์œผ๋กœ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ๊ณ„์‚ฐ ํšจ์œจ์ ์ธ ์ „๋žต์„ ์†Œ๊ฐœํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์‹œ์Šคํ…œ์€ ๊ณ ๋ถ„์ž ๋ฐ˜์‘๊ธฐ๋กœ ๊ณ ๋ถ„์ž์˜ ๋ฐ˜์‘์— ์ค‘์ ์„ ๋‘๊ณ  ์žˆ๋‹ค. ์—ฐ์† ๊ต๋ฐ˜ ํƒฑํฌ ๋ฐ˜์‘๊ธฐ์— ๋Œ€ํ•œ ์ €๋ฐ€๋„ ํด๋ฆฌ์—ํ‹ธ๋ Œ์˜ ๋ถ„์ž๋Ÿ‰ ๋ถ„ํฌ๋ฅผ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด ๋ชจ๋ฉ˜ํŠธ ๋ฐฉ๋ฒ•๊ณผ ๋ชฌํ…Œ ์นด๋ฅผ๋กœ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฒ•์„ ๊ฒฐํ•ฉํ•œ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ ‘๊ทผ ๋ฐฉ์‹์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ๊ณ„์‚ฐ ํšจ์œจ์ ์ธ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์œ„ํ•ด ๋ฐ˜๋ณต๋˜๋Š” ๋ฐ˜์‘๋“ค์„ ์ง‘ํ•ฉ์ธ โ€˜๋ธ”๋ฝโ€™์ด๋ผ๋Š” ๊ฐœ๋…์ด ์ƒˆ๋กœ์ด ๋„์ž…๋˜์—ˆ๋‹ค. โ€˜๋ธ”๋ฝ ํ‚ค๋„คํ‹ฑ ๋ชฌํ…Œ ์นด๋ฅผ๋กœโ€™๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ์ด ๋ชจ๋ธ์€ Neuhaus๊ฐ€ ์ œ์•ˆํ•œ ๋ชจ๋ธ๋ณด๋‹ค ์•ฝ 10~32๋ฐฐ ๋น ๋ฅด๋‹ค. ์ด ๋ชจ๋ธ์€ ๋ชจ๋“  ์ •์ƒ ์ƒํƒœ์‹œ์Šคํ…œ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ํŠน์ • ๋ฐ˜์‘์ด ๋‹ค๋ฅธ ๋ฐ˜์‘๋“ค๋ณด๋‹ค ํ›จ์”ฌ ๋น ๋ฅธ ๊ฒฝ์šฐ์— ๊ณ„์‚ฐ ์‹œ๊ฐ„ ๊ฐ์†Œํšจ๊ณผ๋ฅผ ๋ˆ„๋ฆด ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์‹œ์Šคํ…œ์˜ ์šด์ „ ์˜จ๋„ ๋ฐ ๊ฐœ์‹œ์ œ์˜ ๋†๋„๊ฐ€ ๋ถ„์ž๋Ÿ‰ ๋ถ„ํฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. 180๊ฐœ์˜ ์‚ฌ๋ก€ ์—ฐ๊ตฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ๋ฐ”ํƒ•์œผ๋กœ ๋ถ„์ž๋Ÿ‰ ๋ถ„ํฌ๊ฐ€ ์ˆ„๋”๋ฅผ ๋ณด์ด๋Š” ์กฐ๊ฑด์— ๋Œ€ํ•œ ์ •๋Ÿ‰์  ์ง€์นจ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋‘๋ฒˆ์งธ ์‹œ์Šคํ…œ์€ ์ „์‚ฐ์œ ์ฒด์—ญํ•™ ๊ธฐ๋ฐ˜์˜ ๋ฐ˜์‘๊ธฐ ์‹œ์Šคํ…œ์ด๋‹ค. ์ „์‚ฐ์œ ์ฒด์—ญํ•™์€ ์œ ์ฒด์˜ ํ๋ฆ„์„ ํ•ด์„ํ•จ์— ์žˆ์–ด ํ•„์ˆ˜์ ์ธ ๊ธฐ๋ฒ•์ด๋‹ค. ํŠนํžˆ ํ™”ํ•™๊ณตํ•™๋ฐ˜์‘๊ธฐ์—์„œ ์œ ์ฒด์˜ ํ๋ฆ„์€ ๋‚ด๋ถ€์˜ ์˜จ๋„๋‚˜ ๋†๋„์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ „์‚ฐ์œ ์ฒด์—ญํ•™์€ ๊ณ„์‚ฐ์‹œ๊ฐ„์ด ์˜ค๋ž˜๊ฑธ๋ฆฐ๋‹ค๋Š” ๋‹จ์ ์œผ๋กœ ์ธํ•ด ์‹ค์‹œ๊ฐ„ ์ตœ์ ํ™” ๋ฐ ๊ณต์ • ์ œ์–ด์™€ ๊ฐ™์€ ์‘์šฉ์— ์‚ฌ์šฉ์ด ์ œํ•œ๋œ๋‹ค. ์ด ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ์‹œ์Šคํ…œ์˜ ์ง€๋ฐฐ ๋ฐฉ์ •์‹์„ ํ›ˆ๋ จํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌผ๋ฆฌ์ •๋ณด์‹ ๊ฒฝ๋ง(PINN)์„ ์‚ฌ์šฉํ•˜์—ฌ Van de Vusse ๋ฐ˜์‘์ด ํฌํ•จ๋œ ์—ฐ์† ๊ต๋ฐ˜ ํƒฑํฌ ๋ฐ˜์‘๊ธฐ์˜ ๋Œ€๋ฆฌ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•œ๋‹ค. ํ™”ํ•™ ๋ฐ˜์‘๊ธฐ ์‹œ์Šคํ…œ์ด ๋”ฐ๋ฅด๋Š” ๋ชจ๋“  ์ข…๋ฅ˜์˜ ์ง€๋ฐฐ ๋ฐฉ์ •์‹์„ ํ›ˆ๋ จํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ๋‹ค์ค‘ ์ฐธ์กฐ ํ”„๋ ˆ์ž„ ์‹œ์Šคํ…œ์„ ํ›ˆ๋ จ ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌผ๋ฆฌ์ •๋ณด์‹ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋ฌผ๋ฆฌ์ •๋ณด์‹ ๊ฒฝ๋ง(PINN)์€ ๊ธฐ์กด์— ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์ด ๊ฐ€์ง€๋Š” ๊ณผ์ ํ•ฉ ๋ฌธ์ œ๋‚˜ ๋ฐ์ดํ„ฐ๊ฐ€ ๋งŽ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ์  ๊ทธ๋ฆฌ๊ณ  ๋ฌผ๋ฆฌ์  ์˜๋ฏธ๋ฅผ ๋ฐ˜์˜ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๋ฌธ์ œ๋“ค์„ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๋ฉ”๋ชจ๋ฆฌ ์˜ค๋ฅ˜ ๋ฐ ๋ชจ๋ธ์˜ ๋ฐœ์‚ฐ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ Raissi๊ฐ€ ์ œ์•ˆํ•œ ๊ธฐ์กด์˜ ๋ฌผ๋ฆฌ์ •๋ณด์‹ ๊ฒฝ๋ง(PINN)์„ ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์œผ๋กœ ์ˆ˜์ •ํ•˜์˜€๋‹ค. 1) ๋ฏธ๋‹ˆ ๋ฐฐ์น˜ ํ›ˆ๋ จ; 2) ๊ฐ€์ค‘ ์†์‹ค ํ•จ์ˆ˜. ๊ทธ๋ฆฌ๊ณ  ํ•™์Šต ๋ฐ์ดํ„ฐ๋ฅผ ์ถ”์ถœํ•จ์— ์žˆ์–ด ๋ฌด์ž‘์œ„ ์ถ”์ถœ์— ๋น„ํ•ด ์ •ํ™•๋„๋ฅผ ์ตœ๋Œ€ 5๋ฐฐ๊นŒ์ง€ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์œ ์‚ฌ์„ฑ ๊ธฐ๋ฐ˜ ์ถ”์ถœ ์ „๋žต์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๊ฐ€ ํ™”ํ•™ ๊ณต์ •์˜ ๊ณ ์„ฑ๋Šฅ ๋Œ€๋ฆฌ ๋ชจ๋ธ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ์ง€์นจ์ด ๋˜๊ธฐ๋ฅผ ํฌ๋งํ•œ๋‹ค.Abstract i Contents iv List of Figures vii List of Tables xi Chapter 1 Introduction 1 1.1 Research motivation 1 1.2 Research objective 3 1.3 Outline of the thesis 5 Chapter 2 Molecular weight distribution modeling of LDPE in a continuous stirred-tank reactor using coupled deterministic and stochastic approach 6 2.1 Introduction 6 2.2 Methodology 10 2.2.1 Polymer reaction mechanism 10 2.2.2 Reactor model 16 2.2.3 Deterministic part 16 2.2.4 Stochastic part 20 2.3 Result 34 2.3.1 Verification 34 2.3.2 Reduction in calculation time 39 2.3.3 Case study 41 2.3.4 Shouldering condition 49 2.4 Conclusions 52 2.5 Notations 54 2.6 Abbreviations 57 Chapter 3 Physics-informed deep learning for data-driven solutions of computational fluid dynamics 58 3.1 Introduction 58 3.2 PINN 61 3.3 Model description 64 3.3.1 CFD modeling 64 3.3.2 Governing equations 67 3.3.3 PINN architecture 71 3.4 Result and Discussion 79 3.4.1 Model verification 79 3.4.2 Improvement of model performance 86 3.4.3 Comparison of PINN model with 1-D ODE model 98 3.5 Conclusion 102 3.6 Appendix 105 3.7 Notations 106 Chapter 4 Concluding Remarks 111 4.1 Summary of contributions 111 4.2 Future work 112 Reference 114 Abstract in Korean (๊ตญ๋ฌธ์ดˆ๋ก) 121๋ฐ•
    • โ€ฆ
    corecore