5,432 research outputs found
Applications of Machine Learning to Optimizing Polyolefin Manufacturing
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
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
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
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
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Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products
We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of โ10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20โ25 cm to 10โ15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
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.
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
ํํ ๋ฐ์๊ธฐ ์์คํ ์ ๋ชจ๋ธ๋งํ๊ธฐ ์ํ ๊ณ์ฐ ํจ์จ์ ์ธ ์ ๋ต
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ํํ์๋ฌผ๊ณตํ๋ถ(์๋์งํ๊ฒฝ ํํ์ตํฉ๊ธฐ์ ์ ๊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 Neuhausโs 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 ๋ถ์ผ์ ๋ง์ ์ฐ๊ตฌ์๋ค์ ๋ถ์ ๋ฐ ๊ณ์ฐ ๋ชจ๋ธ์ ์ด์ฉํ์ฌ ์์คํ
์ ๊ฑฐ๋์ ํด์ํ๊ณ ์ต์ ํ, ์ค๊ณ ๋ฐ ์ ์ด๋ฅผ ์ํํ๊ณ ์๋ค. ํ์ง๋ง ๋ชจ๋ธ์ ์ ํ๋์ ๊ณ์ฐ์๊ฐ์ ๊ฑฐ๋๋๋ ๊ด๊ณ์ ์์ด ๊ณ์ฐ์๊ฐ์ด ์ค๋๊ฑธ๋ฆฌ๋ ๋ฌธ์ ๋๋ฌธ์ ๋ชจ๋ธ์ ์ ํ๋๋ฅผ ํํํ ์ ๋ฐ์ ์๋ ์ค์์ด๋ค. ์ด ํ์๋
ผ๋ฌธ์์๋ ๋ ์์คํ
์ ๋ํด ๋ชจ๋ธ์ ์ถฉ์ค๋๋ฅผ ์ ์งํ๋ฉด์๋ ๊ณ์ฐ์๊ฐ์ ํจ๊ณผ์ ์ผ๋ก ์ค์ผ ์ ์๋ ๊ณ์ฐ ํจ์จ์ ์ธ ์ ๋ต์ ์๊ฐํ๋ค.
์ฒซ ๋ฒ์งธ ์์คํ
์ ๊ณ ๋ถ์ ๋ฐ์๊ธฐ๋ก ๊ณ ๋ถ์์ ๋ฐ์์ ์ค์ ์ ๋๊ณ ์๋ค. ์ฐ์ ๊ต๋ฐ ํฑํฌ ๋ฐ์๊ธฐ์ ๋ํ ์ ๋ฐ๋ ํด๋ฆฌ์ํธ๋ ์ ๋ถ์๋ ๋ถํฌ๋ฅผ ์์ธกํ๊ธฐ ์ํด ๋ชจ๋ฉํธ ๋ฐฉ๋ฒ๊ณผ ๋ชฌํ
์นด๋ฅผ๋ก ์๋ฎฌ๋ ์ด์
๊ธฐ๋ฒ์ ๊ฒฐํฉํ ํ์ด๋ธ๋ฆฌ๋ ์ ๊ทผ ๋ฐฉ์์ด ์ ์๋์๋ค. ๊ณ์ฐ ํจ์จ์ ์ธ ์๋ฎฌ๋ ์ด์
์ ์ํด ๋ฐ๋ณต๋๋ ๋ฐ์๋ค์ ์งํฉ์ธ โ๋ธ๋ฝโ์ด๋ผ๋ ๊ฐ๋
์ด ์๋ก์ด ๋์
๋์๋ค. โ๋ธ๋ฝ ํค๋คํฑ ๋ชฌํ
์นด๋ฅผ๋กโ๋ผ๊ณ ๋ถ๋ฆฌ๋ ์ด ๋ชจ๋ธ์ Neuhaus๊ฐ ์ ์ํ ๋ชจ๋ธ๋ณด๋ค ์ฝ 10~32๋ฐฐ ๋น ๋ฅด๋ค. ์ด ๋ชจ๋ธ์ ๋ชจ๋ ์ ์ ์ํ์์คํ
์ ์ ์ฉํ ์ ์์ผ๋ฉฐ, ํน์ ๋ฐ์์ด ๋ค๋ฅธ ๋ฐ์๋ค๋ณด๋ค ํจ์ฌ ๋น ๋ฅธ ๊ฒฝ์ฐ์ ๊ณ์ฐ ์๊ฐ ๊ฐ์ํจ๊ณผ๋ฅผ ๋๋ฆด ์ ์๋ค. ๋ํ ์์คํ
์ ์ด์ ์จ๋ ๋ฐ ๊ฐ์์ ์ ๋๋๊ฐ ๋ถ์๋ ๋ถํฌ์ ๋ฏธ์น๋ ์ํฅ์ ๋ํด ์ฌ๋ก ์ฐ๊ตฌ๋ฅผ ์ํํ์๋ค. 180๊ฐ์ ์ฌ๋ก ์ฐ๊ตฌ ์๋ฎฌ๋ ์ด์
์ ๋ฐํ์ผ๋ก ๋ถ์๋ ๋ถํฌ๊ฐ ์๋๋ฅผ ๋ณด์ด๋ ์กฐ๊ฑด์ ๋ํ ์ ๋์ ์ง์นจ์ ์ ์ํ์๋ค.
๋๋ฒ์งธ ์์คํ
์ ์ ์ฐ์ ์ฒด์ญํ ๊ธฐ๋ฐ์ ๋ฐ์๊ธฐ ์์คํ
์ด๋ค. ์ ์ฐ์ ์ฒด์ญํ์ ์ ์ฒด์ ํ๋ฆ์ ํด์ํจ์ ์์ด ํ์์ ์ธ ๊ธฐ๋ฒ์ด๋ค. ํนํ ํํ๊ณตํ๋ฐ์๊ธฐ์์ ์ ์ฒด์ ํ๋ฆ์ ๋ด๋ถ์ ์จ๋๋ ๋๋์ ํฐ ์ํฅ์ ๋ฏธ์น๋ค. ๊ทธ๋ฌ๋ ์ ์ฐ์ ์ฒด์ญํ์ ๊ณ์ฐ์๊ฐ์ด ์ค๋๊ฑธ๋ฆฐ๋ค๋ ๋จ์ ์ผ๋ก ์ธํด ์ค์๊ฐ ์ต์ ํ ๋ฐ ๊ณต์ ์ ์ด์ ๊ฐ์ ์์ฉ์ ์ฌ์ฉ์ด ์ ํ๋๋ค. ์ด ํ์๋
ผ๋ฌธ์์๋ ์์คํ
์ ์ง๋ฐฐ ๋ฐฉ์ ์์ ํ๋ จํ ์ ์๋ ๋ฌผ๋ฆฌ์ ๋ณด์ ๊ฒฝ๋ง(PINN)์ ์ฌ์ฉํ์ฌ Van de Vusse ๋ฐ์์ด ํฌํจ๋ ์ฐ์ ๊ต๋ฐ ํฑํฌ ๋ฐ์๊ธฐ์ ๋๋ฆฌ ๋ชจ๋ธ์ ๊ฐ๋ฐํ๋ค. ํํ ๋ฐ์๊ธฐ ์์คํ
์ด ๋ฐ๋ฅด๋ ๋ชจ๋ ์ข
๋ฅ์ ์ง๋ฐฐ ๋ฐฉ์ ์์ ํ๋ จํ ์ ์์ผ๋ฉฐ ๋ค์ค ์ฐธ์กฐ ํ๋ ์ ์์คํ
์ ํ๋ จ ํ ์ ์๋ ๋ฌผ๋ฆฌ์ ๋ณด์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์กฐ๋ฅผ ์ ์ํ๋ค. ๋ฌผ๋ฆฌ์ ๋ณด์ ๊ฒฝ๋ง(PINN)์ ๊ธฐ์กด์ ์ ๊ฒฝ๋ง ๋ชจ๋ธ์ด ๊ฐ์ง๋ ๊ณผ์ ํฉ ๋ฌธ์ ๋ ๋ฐ์ดํฐ๊ฐ ๋ง์ด ํ์ํ๋ค๋ ์ ๊ทธ๋ฆฌ๊ณ ๋ฌผ๋ฆฌ์ ์๋ฏธ๋ฅผ ๋ฐ์ํ ์ ์๋ค๋ ๋ฌธ์ ๋ค์ ํด๊ฒฐํ ์ ์๋ค. ๋ํ ๋ฉ๋ชจ๋ฆฌ ์ค๋ฅ ๋ฐ ๋ชจ๋ธ์ ๋ฐ์ฐ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํ์ฌ Raissi๊ฐ ์ ์ํ ๊ธฐ์กด์ ๋ฌผ๋ฆฌ์ ๋ณด์ ๊ฒฝ๋ง(PINN)์ ๋ ๊ฐ์ง ๋ฐฉ๋ฒ์ผ๋ก ์์ ํ์๋ค. 1) ๋ฏธ๋ ๋ฐฐ์น ํ๋ จ; 2) ๊ฐ์ค ์์ค ํจ์. ๊ทธ๋ฆฌ๊ณ ํ์ต ๋ฐ์ดํฐ๋ฅผ ์ถ์ถํจ์ ์์ด ๋ฌด์์ ์ถ์ถ์ ๋นํด ์ ํ๋๋ฅผ ์ต๋ 5๋ฐฐ๊น์ง ํฅ์์ํฌ ์ ์๋ ์ ์ฌ์ฑ ๊ธฐ๋ฐ ์ถ์ถ ์ ๋ต์ ์ ์ํ๋ค. ์ด ์ฐ๊ตฌ๊ฐ ํํ ๊ณต์ ์ ๊ณ ์ฑ๋ฅ ๋๋ฆฌ ๋ชจ๋ธ ๊ฐ๋ฐ์ ์ํ ์ง์นจ์ด ๋๊ธฐ๋ฅผ ํฌ๋งํ๋ค.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 (๊ตญ๋ฌธ์ด๋ก) 121๋ฐ
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