2,374 research outputs found

    ๋ณต์žกํ•œ ๋™ํŠน์„ฑ์„ ๊ฐ–๋Š” ๋‹ค์ƒ ๋ฐ˜์‘๊ธฐ์˜ ์„ค๊ณ„๋ฅผ ์œ„ํ•œ ๊ณ„์‚ฐ ํšจ์œจ์ ์ธ ๋ชจ์‚ฌ ๋ฐ ์ตœ์ ํ™” ์ „๋žต

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€,2020. 2. ์ด์ข…๋ฏผ.๋ณธ ๋ฐ•์‚ฌํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ๋ฉ€ํ‹ฐ ์Šค์ผ€์ผ ๋ชจ๋ธ๋ง, ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•œ ๋ชจ๋ธ ๋ณด์ •๋ฒ•, ์ตœ์ ํ™” ์ˆœ์œผ๋กœ ์ง„ํ–‰๋˜๋Š” ์‚ฐ์—…์šฉ ํ™”ํ•™ ๋ฐ˜์‘๊ธฐ์˜ ์„ค๊ณ„ ์ „๋žต์„ ์ œ์‹œํ•œ๋‹ค. ๋ฐ˜์‘๊ธฐ๋Š” ํ™”ํ•™ ๊ณต์ •์—์„œ ์ œ์ผ ์ค‘์š”ํ•œ ๋‹จ์œ„์ด์ง€๋งŒ, ๊ทธ ์„ค๊ณ„์— ์žˆ์–ด์„œ๋Š” ์ตœ์‹  ์ˆ˜์น˜์  ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค๋Š” ์—ฌ์ „ํžˆ ๊ฐ„๋‹จํ•œ ๋ชจ๋ธ์ด๋‚˜ ์‹คํ—˜ ๋ฐ ๊ฒฝํ—˜ ๊ทœ์น™์— ์˜์กดํ•˜๊ณ  ์žˆ๋Š” ํ˜„์‹ค์ด๋‹ค. ์‚ฐ์—… ๊ทœ๋ชจ์˜ ๋ฐ˜์‘๊ธฐ๋Š” ๋ฌผ๋ฆฌ, ํ™”ํ•™์ ์œผ๋กœ ๋ชน์‹œ ๋ณต์žกํ•˜๊ณ , ๊ด€๋ จ ๋ณ€์ˆ˜ ๊ฐ„์˜ ์Šค์ผ€์ผ์ด ํฌ๊ฒŒ ์ฐจ์ด๋‚˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•„ ์ˆ˜ํ•™์  ๋ชจ๋ธ๋ง ๋ฐ ์ˆ˜์น˜์  ํ•ด๋ฒ•์„ ๊ตฌํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๋‹ค. ๋ชจ๋ธ์„ ๋งŒ๋“ค๋”๋ผ๋„ ๋ถ€์ •ํ™•ํ•˜๊ฑฐ๋‚˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹œ๊ฐ„์ด ๋„ˆ๋ฌด ๊ธด ๋ฌธ์ œ๊ฐ€ ์žˆ์–ด ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์ ์šฉํ•˜๊ธฐ๊ฐ€ ํž˜๋“ค๋‹ค. ๋ฐ˜์‘๊ธฐ ๋‚ด ํ˜„์ƒ์˜ ๋ณต์žก์„ฑ๊ณผ ์Šค์ผ€์ผ ์ฐจ์ด ๋ฌธ์ œ๋Š” ๋ฉ€ํ‹ฐ ์Šค์ผ€์ผ ๋ชจ๋ธ๋ง์„ ํ†ตํ•ด ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋‹ค. ์ „์‚ฐ์œ ์ฒด์—ญํ•™ ๊ธฐ๋ฐ˜ ๊ตฌํš ๋ชจ๋ธ(CFD-based compartmental model)์„ ์ด์šฉํ•˜๋ฉด, ๋ถˆ๊ท ์ผํ•œ ํ˜ผํ•ฉ ํŒจํ„ด์„ ๋ณด์ด๋Š” ๋Œ€ํ˜• ๋ฐ˜์‘๊ธฐ์—์„œ๋„ ๊ธด ์‹œ๊ฐ„ ๋™์•ˆ์˜ ๋™์  ๋ชจ์‚ฌ๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด ๋ชจ๋ธ์€ ํฐ ๋ฐ˜์‘๊ธฐ๋ฅผ ์™„๋ฒฝํ•˜๊ฒŒ ๊ท ์ผํ•œ ์ž‘์€ ๊ตฌํš๋“ค์˜ ๋„คํŠธ์›Œํฌ๋กœ ๊ฐ„์ฃผํ•˜๊ณ , ๊ฐ ๊ตฌํš์„ ๋ฐ˜์‘ ์†๋„์‹๋“ค๊ณผ CFD ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ ๊ฐ€์ ธ์˜จ ์œ ๋™ ์ •๋ณด๊ฐ€ ํฌํ•จ๋œ ์งˆ๋Ÿ‰ ๋ฐ ์—๋„ˆ์ง€ ๊ท ํ˜• ๋ฐฉ์ •์‹์œผ๋กœ ํ‘œํ˜„ํ•œ๋‹ค. ๊ธฐ์ฒด, ์•ก์ฒด, ๊ณ ์ฒด 3์ƒ์ด ์ƒํ˜ธ์ž‘์šฉํ•˜๋ฉฐ ๋ณต์žกํ•œ ์œ ๋™์„ ๋ณด์ด๋Š” ์ˆ˜์„ฑ ๊ด‘๋ฌผ ํƒ„์‚ฐํ™” ๋ฐ˜์‘๊ธฐ๋ฅผ ์ด ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•ด ๋ชจ๋ธ๋งํ•˜์˜€๋‹ค. ์ด ๋•Œ ๋ชจ๋ธ์€ ๋ฏธ๋ถ„ ๋Œ€์ˆ˜ ๋ฐฉ์ •์‹(DAE)์˜ ํ˜•ํƒœ๋ฅผ ๋ ๋ฉฐ, ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ƒ ๋ชจ๋“  ๋ฐ˜์‘๋“ค(๊ธฐ-์•ก ๊ฐ„ ๋ฌผ์งˆ ์ „๋‹ฌ ๋ฐ˜์‘, ๊ณ ์ฒด ์šฉํ•ด ๋ฐ˜์‘, ์ด์˜จ ๊ฐ„ ๋ฐ˜์‘, ์•™๊ธˆ ์นจ์ „ ๋ฐ˜์‘)๊ณผ ์œ ์ฒด ์—ญํ•™, ๋ฐ˜์‘์—ด, ์—ด์—ญํ•™์  ๋ณ€ํ™” ๋ฐ ์šด์ „ ์ƒ์˜ ์ด๋ฒคํŠธ ๋ฐœ์ƒ์„ ๋ชจ๋‘ ๊ณ ๋ คํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ชจ๋ธ์„ ์ด์šฉํ•ด ์ด์‚ฐํ™”ํƒ„์†Œ ์ œ๊ฑฐ ํšจ์œจ, pH ๋ฐ ์˜จ๋„ ๋ณ€ํ™”๋ฅผ ์˜ˆ์ธกํ•˜์—ฌ ์‹ค์ œ ์šด์ „ ๋ฐ์ดํ„ฐ์™€ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ํ†ตํ•œ ๋ณด์ •์ด ์ „ํ˜€ ์—†์ด๋„ 7 % ์ด๋‚ด์˜ ์˜ค์ฐจ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋ชจ๋ธ์˜ ๋ถ€์ •ํ™•์„ฑ ๋ฌธ์ œ๋Š” ๋ชจ๋ธ๋ง ํ›„ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•œ ๋ชจ๋ธ ๋ณด์ •์œผ๋กœ ๊ทน๋ณต ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ด‘๋ฌผ ํƒ„์‚ฐํ™” ๋ฐ˜์‘๊ธฐ ๋ชจ๋ธ์„ ๋ฒ ์ด์ง€์•ˆ ๋ณด์ •(Bayesian calibration)์„ ํ†ตํ•ด ๊ฐ•ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋จผ์ € ๋ชจ๋ธ ์ค‘ ๋ถˆํ™•์‹คํ•œ ๋ถ€๋ถ„์— 8๊ฐœ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๋„์ž…ํ•œ ํ›„, ๋ฒ ์ด์ง€์•ˆ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •๋ฒ•(Bayesian parameter estimation) ๋ฐ ์‹คํ—˜์‹ค ๊ทœ๋ชจ์—์„œ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋“ค์„ ์ด์šฉํ•˜์—ฌ ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์˜ ์‚ฌํ›„ ํ™•๋ฅ  ๋ถ„ํฌ๋ฅผ ์ถ”์ •ํ•˜์˜€๋‹ค. ์–ป์–ด์ง„ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ํ™•๋ฅ  ๋ถ„ํฌ๋“ค์€ ๋ชจ๋ธ ๋ฐ ์‹คํ—˜์˜ ๋ถˆ์™„์ „์„ฑ์œผ๋กœ ์ธํ•ด ๋‚˜ํƒ€๋‚˜๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ๋ถˆํ™•์‹ค์„ฑ ๋ฐ ๋‹ค์ค‘ ๋ด‰์šฐ๋ฆฌ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ž˜ ๋”ฐ๋ผ๊ฐ€๋Š” ํ™•๋ฅ ๋ก ์  ๋ชจ๋ธ ์˜ˆ์ธก์น˜(stochastic model response)๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. 16๊ฐœ์˜ ์‹คํ—˜ ๋ฐ์ดํ„ฐ์…‹ ๋ฐ ํ…Œ์ŠคํŠธ์…‹์˜ ํ”ผํŒ… ์—๋Ÿฌ(fitting error)๋Š” ๊ฒฐ์ •๋ก ์ ์ธ ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜(deterministic optimization)์„ ์‚ฌ์šฉํ•  ๋•Œ๋ณด๋‹ค ๋น„์Šทํ•˜๊ฑฐ๋‚˜ ๋‚ฎ์€ ๊ฒƒ์œผ๋กœ ์ธก์ •๋˜์—ˆ๋‹ค. ์ˆ˜ํ•™์  ์ตœ์ ํ™”์— ์“ฐ์ด๊ธฐ์— ๋„ˆ๋ฌด ๊ธด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹œ๊ฐ„ ๋ฌธ์ œ๋Š” ๋ฒ ์ด์ง€์•ˆ ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜์—ฌ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋‹ค. ํ™”ํ•™ ๋ฐ˜์‘๊ธฐ ์„ค๊ณ„ ์ตœ์ ํ™”๋ฅผ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์ค‘ ๋ชฉ์  ๋ฒ ์ด์ง€์•ˆ ์ตœ์ ํ™”(Multi-objective Bayesian Optimization, MBO)๋ฅผ ์‚ฌ์šฉํ•ด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํšŸ์ˆ˜๋ฅผ ์ตœ์†Œํ™” ํ•˜๋Š” CFD ๊ธฐ๋ฐ˜ ์ตœ์  ์„ค๊ณ„ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์—ฌ์„ฏ ๊ฐ€์ง€ ์„ค๊ณ„ ๋ณ€์ˆ˜๋ฅผ ๊ฐ€์ง€๋Š” ๊ธฐ-์•ก ๊ต๋ฐ˜ ํƒฑํฌ ๋ฐ˜์‘๊ธฐ์—์„œ ์ „๋ ฅ ์†Œ๋น„๋ฅผ ์ตœ์†Œํ™”ํ•˜๊ณ  ๊ฐ€์Šค ๋ถ„์œจ(gas holdup)๋ฅผ ๊ทน๋Œ€ํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์ด ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•œ ๊ฒฐ๊ณผ, ๋‹จ 100 ํšŒ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋งŒ์œผ๋กœ ์ตœ์  ํŒŒ๋ ˆํ†  ์ปค๋ธŒ(Pareto curve)๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ œ์•ˆ๋œ ์ตœ์  ์„ค๊ณ„์•ˆ๋“ค์€ ๋ฌธํ—Œ์— ๋ณด๊ณ ๋œ ๊ธฐ์กด ๋ฐ˜์‘๊ธฐ๋“ค๊ณผ ๋น„๊ตํ•ด ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. . ๋ณธ ๋…ผ๋ฌธ์„ ํ†ตํ•ด ์ œ์•ˆ๋œ CFD ๊ธฐ๋ฐ˜ ๊ตฌํš ๋ชจ๋ธ๋ง๋ฒ•, ๋ฒ ์ด์ง€์•ˆ ๋ชจ๋ธ ๋ณด์ •๋ฒ• ๋ฐ ๋ฒ ์ด์ง€์•ˆ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•์€ ๋ณต์žกํ•œ ๋ฌผ๋ฆฌ์  ๋ฐ ํ™”ํ•™์  ํŠน์ง•์„ ๊ฐ–๋Š” ์‚ฐ์—… ๊ทœ๋ชจ์˜ ํ™”ํ•™ ๋ฐ˜์‘๊ธฐ์— ์ ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.This thesis presents a design strategy for industrial-scale chemical reactors which consists of multi-scale modeling, post-modeling calibration, and optimization. Although the reactor design problem is a primary step in the development of most chemical processes, it has been relied on simple models, experiments and rules of thumbs rather than taking advantage of recent numerical techniques. It is because industrial-size reactors show high complexity and scale differences both physically and chemically, which makes it difficult to be mathematically modeled. Even after the model is constructed, it suffers from inaccuracies and heavy simulation time to be applied in optimization algorithms. The complexity and scale difference problem in modeling can be solved by introducing multi-scale modeling approaches. Computational fluid dynamics (CFD)-based compartmental model makes it possible to simulate hours of dynamics in large size reactors which show inhomogeneous mixing patterns. It regards the big reactor as a network of small zones in which perfect mixing can be assumed and solves mass and energy balance equations with kinetics and flow information adopted from CFD hydrodynamics model at each zone. An aqueous mineral carbonation reactor with complex gasโ€“liquidโ€“solid interacting flow patterns was modeled using this method. The model considers the gas-liquid mass transfer, solid dissolution, ionic reactions, precipitations, hydrodynamics, heat generation and thermodynamic changes by the reaction and discrete operational events in the form of differential algebraic equations (DAEs). The total CO2 removal efficiency, pH, and temperature changes were predicted and compared to real operation data. The errors were within 7 % without any post-adjustment. The inaccuracy problem of model can be overcome by post-modeling approach, such as the calibration with experiments. The model for aqueous mineral carbonation reactor was intensified via Bayesian calibration. Eight parameters were intrduced in the uncertain parts of the rigorous reactor model. Then the calibration was performed by estimating the parameter posterior distribution using Bayesian parameter estimation framework and lab-scale experiments. The developed Bayesian parameter estimation framework involves surrogate models, Markov chain Monte Carlo (MCMC) with tempering, global optimization, and various analysis tools. The obtained parameter distributions reflected the uncertain or multimodal natures of the parameters due to the incompleteness of the model and the experiments. They were used to earn stochastic model responses which show good fits with the experimental results. The fitting errors of all the 16 datasets and the unseen test set were measured to be comparable or lower than when deterministic optimization methods are used. The heavy simulation time problem for mathematical optimization can be resolved by applying Bayesian optimizaion algorithm. CFD based optimal design tool for chemical reactors, in which multi-objective Bayesian optimization (MBO) is utilized to reduce the number of required CFD runs, is proposed. The developed optimizer was applied to minimize the power consumption and maximize the gas holdup in a gas-sparged stirred tank reactor, which has six design variables. The saturated Pareto front was obtained after only 100 iterations. The resulting Pareto front consists of many near-optimal designs with significantly enhanced performances compared to conventional reactors reported in the literature. It is anticipated that the suggested CFD-based compartmental modeling, post-modeling Bayesian calibration, and Bayesian optimization methods can be applied in general industrial-scale chemical reactors with complex physical and chemical features.1. Introduction 1 1.1. Industrial-scale chemical reactor design 1 1.2. Role of mathematical models in reactor design 2 1.3. Intensification of reactor models through calibration 5 1.3.1. Bayesian parameter estimation 6 1.4. Optimization of the reactor models 7 1.4.1. Bayesian optimization 9 1.5. Aqueous mineral carbonation process : case study subject 10 1.6. Outline of the thesis 12 2. Multi-scale modeling of industrial-scale aqueous mineral carbonation reactor for long-time dynamic simulation 14 2.1. Objective 14 2.2. Experimental setup 15 2.3. Mathematical models 19 2.3.1. Reactor model 19 2.3.2. CFD model 28 2.3.3. Numerical setting 30 2.4. Results and discussions 32 2.4.1. CFD-based compartmental model for industrial-scale reactor. 32 2.4.2. Design and simulation of higher-scale reactors 42 2.5. Conclusions 47 3. Model intensification of aqueous mineral carbonation kinetics via Bayesian calibration 50 3.1. Objective 50 3.2. Experimental methods 51 3.2.1. Solution and gas preparation 51 3.2.2. Laboratory-scale mineral carbonation process 53 3.3. Mathematical models 56 3.3.1. Kinetics of aqueous mineral carbonation process 56 3.3.2. Differential algebraic equation (DAE) model for the reactor 65 3.3.3. Discrete events for simulation procedure 71 3.3.4. Numerical setting 72 3.4. Bayesian parameter estimation 72 3.4.1. Problem formulation 73 3.4.2. Bayesian posterior inference 76 3.4.3. Sampling 81 3.5. Results and discussions 82 3.5.1. Stochastic output response 82 3.5.2. Quality of parameter estimtates 86 3.5.3. Assessment of parameter uncertainties 91 3.5.4. Kinetics study with the proposed model parameters 99 3.6. Conclusions 103 4. Multi-objective optimization of chemical reactor design using computational fluid dynamics 106 4.1. Objective 106 4.2. Problem Formulation 107 4.3. Optimization scheme 113 4.3.1. Multi-objective optimization algorithm 113 4.3.2. CFD-MBO optimizer 120 4.4. CFD modeling 125 4.4.1. Tank specifications 125 4.4.2. Governing equations 125 4.4.3. Simulation methods 127 4.5. Results and discussion 128 4.5.1. CFD model validation 128 4.5.2. Optimization results 130 4.5.3. Analysis of optimal designs 139 4.6. Conclusions 144 5. Concluding Remarks 146 Bibliography 149 Abstract in Korean (๊ตญ๋ฌธ์ดˆ๋ก) 163Docto

    Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations

    Full text link
    The development of new manufacturing techniques such as 3D printing have enabled the creation of previously infeasible chemical reactor designs. Systematically optimizing the highly parameterized geometries involved in these new classes of reactor is vital to ensure enhanced mixing characteristics and feasible manufacturability. Here we present a framework to rapidly solve this nonlinear, computationally expensive, and derivative-free problem, enabling the fast prototype of novel reactor parameterizations. We take advantage of Gaussian processes to adaptively learn a multi-fidelity model of reactor simulations across a number of different continuous mesh fidelities. The search space of reactor geometries is explored through an amalgam of different, potentially lower, fidelity simulations which are chosen for evaluation based on weighted acquisition function, trading off information gain with cost of simulation. Within our framework we derive a novel criteria for monitoring the progress and dictating the termination of multi-fidelity Bayesian optimization, ensuring a high fidelity solution is returned before experimental budget is exhausted. The class of reactor we investigate are helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics, have the potential to be highly parameterized, and are easily manufactured using 3D printing. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming its mixing performance. In doing so we demonstrate our design framework to be extensible to a broad variety of expensive simulation-based optimization problems, supporting the design of the next generation of highly parameterized chemical reactors.Comment: 22 Pages with Appendi

    Machine Learning-Assisted Discovery of Novel Reactor Designs via CFD-Coupled Multi-fidelity Bayesian Optimisation

    Full text link
    Additive manufacturing has enabled the production of more advanced reactor geometries, resulting in the potential for significantly larger and more complex design spaces. Identifying and optimising promising configurations within broader design spaces presents a significant challenge for existing human-centric design approaches. As such, existing parameterisations of coiled-tube reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. Given algorithmic improvements and the onset of additive manufacturing, we propose two novel coiled-tube parameterisations enabling the variation of cross-section and coil path, resulting in a series of high dimensional, complex optimisation problems. To ensure tractable, non-local optimisation where gradients are not available, we apply multi-fidelity Bayesian optimisation. Our approach characterises multiple continuous fidelities and is coupled with parameterised meshing and simulation, enabling lower quality, but faster simulations to be exploited throughout optimisation. Through maximising the plug-flow performance, we identify key characteristics of optimal reactor designs, and extrapolate these to produce two novel geometries that we 3D print and experimentally validate. By demonstrating the design, optimisation, and manufacture of highly parameterised reactors, we seek to establish a framework for the next-generation of reactors, demonstrating that intelligent design coupled with new manufacturing processes can significantly improve the performance and sustainability of future chemical processes.Comment: 11 pages, 8 figure

    Model predictive control techniques for hybrid systems

    Get PDF
    This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduaciรณn y Ciencia DPI2007-66718-C04-01Ministerio de Eduaciรณn y Ciencia DPI2008-0581

    ๊ฐ•ํ™”๋œ ์นœํ™˜๊ฒฝ ํ™”ํ•™ ๊ณต์ •์˜ ํƒ€๋‹น์„ฑ ๊ฐœ์„ ์„ ์œ„ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฐ˜ ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€(์—๋„ˆ์ง€ํ™˜๊ฒฝ ํ™”ํ•™์œตํ•ฉ๊ธฐ์ˆ ์ „๊ณต), 2021.8. ์ด์ข…๋ฏผ.Technologies to mitigate risks from climate change have made significant advances in both research academia and industry. However, most advanced techniques were still considered only in the lab-scale experience. Moreover, the process intensification development during the process synthesis is in its incipient stages. To promote the application of a novel process technique to an eco-friendly process, the process feasibility of economy or operation should be considered. In this thesis, simulation-based framework to improve feasibility of intensified chemical processes, which are suggested under limited experimental conditions, is proposed. To solve the feasibility problem derived from the characteristics of process intensification, which is mainly developed by the numerous experiments rather than derived from the theoretical verification, the digital twin technology, which is developed to simulate various situations by modeling the reactor and process, is implemented. In addition, the framework, including the procedures from verification or validation of developed digital twin model to feasibility study and improvement of economic and operational feasibility, is proposed. First, intensified eco-friendly processes such as the biodiesel production process and carbon capture and utilization process were simulated, and comparative study and optimization were conducted to improve the economic or operational feasibility of that processes. As an example of applying the procedure to verify and to improve the economic feasibility, the economic feasibility study on the intensified biodiesel production process is implemented to increase the profitability of the biodiesel production process by reducing the process units, enhancing biodiesel quality, and reducing the raw material cost. As an example of applying the procedure to verify and to improve the operational feasibility, the modeling and validation of the semi-continuous carbonation process are implemented to estimate the overall CO2 removal efficiency during the operation and when the reaction ends. Using the developed process model, the operational feasibility of the semi-continuous carbonation process is verified and the optimization algorithms is adopted to obtain the optimal operation recipes. For the effective operational feasibility improvement, two new operation recipes were suggested and optimized via Bayesian optimization. Consequently, in order to verify and improve the applicability of the newly proposed intensified process, a methodology is proposed including the process modeling, which is conducted using laboratory-scale experimental data for the reaction kinetic studies, economic analysis, sensitivity analysis, and comparative study. In addition, the process modeling and optimization, using pilot-scale operation data, are carried out. Especially, the operational feasibility of semi-continuous carbonation process is effectively improved by proposing new operation recipe as well as adopting the digital twin model to the Black-box optimization method. In this thesis, a framework, improving economic/operational feasibility of newly proposed intensified chemical processes with two different experimental data depending on the purpose, is developed.๊ธฐํ›„ ๋ณ€ํ™”๋กœ ์ธํ•œ ์œ„ํ—˜๋“ค์„ ์™„ํ™”ํ•˜๋Š” ๊ธฐ์ˆ ๋“ค์€ ํ•™์ˆ  ๋ถ„์•ผ์™€ ์‚ฐ์—… ๋ชจ๋‘์—์„œ ์ƒ๋‹นํ•œ ๋ฐœ์ „์„ ์ด๋ฃจ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋Œ€๋ถ€๋ถ„์˜ ์ง„๋ณด๋œ ๊ธฐ์ˆ ๋“ค์€ ์—ฌ์ „ํžˆ ์‹คํ—˜์‹ค ๊ทœ๋ชจ์˜ ์‹คํ—˜์—์„œ๋งŒ ๊ณ ๋ ค๋˜๊ณ  ์žˆ๋‹ค. ๋”์šฑ์ด, ๊ณต์ • ํ•ฉ์„ฑ ๊ณผ์ •์—์„œ์˜ ๊ณต์ • ๊ฐ•ํ™” ๊ฐœ๋ฐœ์€ ์ดˆ๊ธฐ ๋‹จ๊ณ„์— ๋จธ๋ฌผ๋Ÿฌ์žˆ๋‹ค. ์ƒˆ๋กœ์šด ๊ณต์ • ๊ธฐ์ˆ ๋“ค์„ ์นœํ™˜๊ฒฝ ๊ณต์ •์— ์ ์šฉํ•˜๋Š” ๊ฒƒ์„ ์ด‰์ง„์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ๋Š”, ๊ณต์ •์˜ ๊ฒฝ์ œ์„ฑ ๋˜๋Š” ์šด์ „ ํƒ€๋‹น์„ฑ์„ ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ์ œํ•œ๋œ ์‹คํ—˜ ์กฐ๊ฑด ํ•˜์—์„œ ์ œ์•ˆ๋œ, ๊ฐ•ํ™”๋œ ์นœํ™˜๊ฒฝ ๊ณต์ •์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฐ˜ ํƒ€๋‹น์„ฑ ๊ฐœ์„  ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ด๋ก ์ ์ธ ๊ฒ€์ฆ์„ ํ†ตํ•œ ๊ณต์ • ๊ฐ•ํ™”๊ฐ€ ์•„๋‹Œ ์ˆ˜๋งŽ์€ ์‹คํ—˜์„ ํ†ตํ•ด ์ œ์•ˆ๋˜๋Š” ๊ณต์ • ๊ฐ•ํ™”์˜ ๋ฐฉ์‹์—์„œ ํŒŒ์ƒ๋˜๋Š” ํƒ€๋‹น์„ฑ ๊ฒ€์ฆ์— ๋Œ€ํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ ์ž, ๊ณต์ • ๋ฐ ๋ฐ˜์‘๊ธฐ์˜ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜๊ณ  ๋‹ค์–‘ํ•œ ์ƒํ™ฉ์„ ๋ชจ์‚ฌํ•˜๋Š” ๋””์ง€ํ„ธ ํŠธ์œˆ ๊ธฐ์ˆ ์„ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ตฌ์ถ•๋œ ๋””์ง€ํ„ธ ํŠธ์œˆ ๋ชจ๋ธ์˜ ๊ฒ€์ฆ๋ถ€ํ„ฐ, ๊ฐ•ํ™”๋œ ๊ณต์ •์˜ ๊ฒฝ์ œ์  ํƒ€๋‹น์„ฑ ๋ฐ ์šด์ „ ํƒ€๋‹น์„ฑ ๊ฒ€์ฆ ๋ฐ ๊ฐœ์„ ๊นŒ์ง€์˜ ์ผ๋ จ์˜ ๊ณผ์ •๋“ค์„ ํฌํ•จํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋จผ์ €, ๋ฐ”์ด์˜ค๋””์ ค ์ƒ์‚ฐ ๊ณต์ •๊ณผ ํƒ„์†Œ ํฌ์ง‘ ๋ฐ ํ™œ์šฉ ๊ณต์ •๊ณผ ๊ฐ™์€ ๊ฐ•ํ™”๋œ ์นœํ™˜๊ฒฝ ๊ณต์ •์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜๊ณ , ํ•ด๋‹น ๊ณต์ •์˜ ๊ฒฝ์ œ์  ํƒ€๋‹น์„ฑ ๋˜๋Š” ์šด์ „ ํƒ€๋‹น์„ฑ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ๋น„๊ต ์—ฐ๊ตฌ ๋ฐ ์ตœ์ ํ™”๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ฒฝ์ œ์  ํƒ€๋‹น์„ฑ ๊ฒ€์ฆ ๋ฐฉ๋ฒ•๋ก ์„ ์ ์šฉํ•œ ํ•œ๊ฐ€์ง€ ์˜ˆ์ œ๋กœ์จ, ๊ณต์ • ์œ ๋‹›์„ ์ค„์ด๊ณ , ๋ฐ”์ด์˜ค๋””์ ค ํ’ˆ์งˆ ํ–ฅ์ƒ ๋ฐ ์›์žฌ๋ฃŒ ๋น„์šฉ์„ ๊ฐ์†Œ์‹œํ‚ด์œผ๋กœ์จ ๋ฐ”์ด์˜ค๋””์ ค ์ƒ์‚ฐ ๊ณต์ •์˜ ์ˆ˜์ต์„ฑ์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ๊ฐ•ํ™”๋œ ๋ฐ”์ด์˜ค๋””์ ค ์ƒ์‚ฐ ๊ณต์ •์˜ ๊ฒฝ์ œ์  ํƒ€๋‹น์„ฑ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์šด์ „ ํƒ€๋‹น์„ฑ ๊ฒ€์ฆ ๋ฐฉ๋ฒ•๋ก ์„ ์ ์šฉํ•œ ํ•œ๊ฐ€์ง€ ์˜ˆ์ œ๋กœ์จ, ๋ฐ˜์—ฐ์†์‹ ํƒ„์‚ฐํ™” ๊ณต์ •์˜ ์šด์ „ ์ค‘ ์ „์ฒด ์ด์‚ฐํ™”ํƒ„์†Œ ์ œ๊ฑฐ ํšจ์œจ๊ณผ ๋ฐ˜์‘ ์ข…๋ฃŒ ์‹œ๊ธฐ๋ฅผ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ๋ง ๋ฐ ๊ตฌ์ถ•๋œ ๋ชจ๋ธ์˜ ๊ฒ€์ฆ์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ตฌ์ถ•๋œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ, ๋ฐ˜์—ฐ์†์‹ ํƒ„์‚ฐํ™” ๊ณต์ •์˜ ์šด์ „ ํƒ€๋‹น์„ฑ์„ ํ™•์ธํ•˜๊ณ  ์ตœ์ ์˜ ์šด์ „ ๋ ˆ์‹œํ”ผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์ ์šฉํ•˜์˜€๋‹ค. ํšจ๊ณผ์ ์ธ ๋ฐ˜์—ฐ์†์‹ ํƒ„์‚ฐํ™” ๊ณต์ •์˜ ์šด์˜ ํƒ€๋‹น์„ฑ ๊ฐœ์„ ์„ ์œ„ํ•ด ๋‘๊ฐœ์˜ ์ƒˆ๋กœ์šด ์šด์ „ ๋ ˆ์‹œํ”ผ๋ฅผ ์ œ์‹œํ•˜์˜€๊ณ , ๋ฒ ์ด์ง€์•ˆ ์ตœ์ ํ™” ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์ตœ์ ํ™”ํ•˜์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ๋ฐ˜์‘์‹ ๋ฐ ๋ฐ˜์‘ ๊ณ„์ˆ˜ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์‹คํ—˜์‹ค ๊ทœ๋ชจ์˜ ์‹คํ—˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณต์ • ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜๊ณ , ๊ฒฝ์ œ์„ฑ ๋ถ„์„, ๋ฏผ๊ฐ๋„ ๋ถ„์„, ๋น„๊ต ๋ถ„์„์„ ํ†ตํ•ด ์ƒˆ๋กœ ์ œ์•ˆ๋œ ๊ฐ•ํ™”๋œ ๊ณต์ •์˜ ํ˜„์—…์—์˜ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ๊ฒ€์ฆํ•˜๊ณ  ๊ฐœ์„ ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‹ค์ œ ํ˜„์—…์— ์ ์šฉ๋˜๋Š” ํฌ๊ธฐ์˜ ๋ฐ˜์‘๊ธฐ๋ฅผ ๊ฐ€์ง„ ์ค‘๊ฐ„ ๊ทœ๋ชจ์˜ ์‹คํ—˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณต์ • ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜๊ณ  ์ตœ์ ํ™”ํ•˜์˜€๋‹ค. ํŠนํžˆ, ๋ฐ˜์—ฐ์†์‹ ๊ณต์ •์˜ ์ƒˆ๋กœ์šด ์šด์ „ ๋ฐฉ์‹์„ ์ œ์•ˆํ•จ๊ณผ ๋™์‹œ์— ๊ตฌ์ถ•๋œ ๋””์ง€ํ„ธ ํŠธ์œˆ ๋ชจ๋ธ์— ๋ธ”๋ž™๋ฐ•์Šค (Black-box) ์ตœ์ ํ™” ๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์ œ์•ˆ๋œ ๋ฐ˜์—ฐ์†์‹ ์นœํ™˜๊ฒฝ ๊ณต์ •์˜ ์šด์ „ ํƒ€๋‹น์„ฑ์„ ํšจ๊ณผ์ ์œผ๋กœ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ๋ชฉ์ ์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ๋‘ ๊ฐ€์ง€์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•˜์—ฌ, ์ƒˆ๋กญ๊ฒŒ ์ œ์•ˆ๋œ ๊ฐ•ํ™”๋œ ์นœํ™˜๊ฒฝ ๊ณต์ •์˜ ๊ฒฝ์ œ์  ๋˜๋Š” ์šด์ „ ํƒ€๋‹น์„ฑ ๊ฒ€์ฆ ๋ฐ ๊ฐœ์„ ์„ ์œ„ํ•œ ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•˜์˜€๋‹ค.Chapter 1 1 Introduction 1 1.1 Research motivation 1 1.2 Target process descriptions 4 1.2.1 Biodiesel production process 4 1.2.2 Aqueous mineral carbonation process 6 1.3 Outline of the thesis 8 1.4 Associated publications 8 Chapter 2 9 Economic feasibility study on biodiesel production process 9 2.1 Introduction 9 2.2 Reaction kinetics 13 2.3 Process simulation 15 2.3.1 Material and thermodynamic model 18 2.3.2 Assumptions 22 2.3.3 SC PFR 22 2.3.4 Cu-based PBR 26 2.3.5 Pd-based PBR 30 2.4 Economic analysis 33 2.4.1 Total capital investment 37 2.4.2 Total manufacturing cost 39 2.4.3 Sensitivity analysis 41 2.5 Summary 46 Chapter 3 48 Modeling and validation of pilot-scale aqueous mineral carbonation process 48 3.1 Introduction 48 3.2 Reaction kinetics 48 3.2.1 Calcium hydroxide dissolution in water 49 3.2.2 Mass transfer of CO2 gas into the alkali solution 50 3.2.3 Ionic reactions and precipitation of calcium carbonate 53 3.3 Process design and modeling 56 3.3.1 Assumptions 56 3.3.2 Reactor modelling 57 3.3.3 Sequence of reactant replenishment 59 3.3.4 ACM model validation 63 3.4 Summary 65 Chapter 4 66 Bayesian optimization approach to semi-continuous carbonation process operation recipe 66 4.1 Introduction 66 4.2 Problem descriptions 68 4.3 Multi-objective Bayesian optimization algorithm 76 4.4 Results and discussion 84 4.5 Summary 91 Chapter 5 Concluding remarks 94 References 97 Abstract in Korean (๊ตญ๋ฌธ์ดˆ๋ก) 106๋ฐ•

    ํ™”ํ•™ ๋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•

    Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis

    Full text link
    Microgels are cross-linked, colloidal polymer networks with great potential for stimuli-response release in drug-delivery applications, as their size in the nanometer range allows them to pass human cell boundaries. For applications with specified requirements regarding size, producing tailored microgels in a continuous flow reactor is advantageous because the microgel properties can be controlled tightly. However, no fully-specified mechanistic models are available for continuous microgel synthesis, as the physical properties of the included components are only studied partly. To address this gap and accelerate tailor-made microgel development, we propose a data-driven optimization in a hardware-in-the-loop approach to efficiently synthesize microgels with defined sizes. We optimize the synthesis regarding conflicting objectives (maximum production efficiency, minimum energy consumption, and the desired microgel radius) by applying Bayesian optimization via the solver ``Thompson sampling efficient multi-objective optimization'' (TS-EMO). We validate the optimization using the deterministic global solver ``McCormick-based Algorithm for mixed-integer Nonlinear Global Optimization'' (MAiNGO) and verify three computed Pareto optimal solutions via experiments. The proposed framework can be applied to other desired microgel properties and reactor setups and has the potential of efficient development by minimizing number of experiments and modelling effort needed.Comment: Manuscript: 24 pages, 8 figures; SI: 9 pages, 3 figure
    • โ€ฆ
    corecore