Quantitative Analysis of Heat Exchanger Network Synthesis for Applying Theoretical Networks to the Gas Industry

Jonggeol Na(나종걸),Jaeheum Jung(정재흠),Chansaem Park(박찬샘),Chonghun Han(한종훈) 한국가스학회 2013 한국가스학회 학술대회논문집 Vol.2013 No.11

Toxic gas release modeling for real-time analysis using variational autoencoder with convolutional neural networks

Na, Jonggeol,Jeon, Kyeongwoo,Lee, Won Bo Elsevier 2018 Chemical engineering science Vol.181 No.-

<P><B>Abstract</B></P> <P>High-accuracy gas dispersion models are necessary for predicting toxic gas movement, and for reducing the damage caused by toxic gas release accidents in chemical processes. In urban areas, where obstacles are large and abundant, computational fluid dynamics (CFD) would be the best choice for simulating and analyzing scenarios of accidental release of toxic chemicals. However, owing to the large computation time required for CFD simulation, it is inappropriate in emergency situations and in real-time alarm systems. In this study, a non-linear surrogate model based on deep learning is proposed using a variational autoencoder with deep convolutional layers and a deep neural network with batch normalization (VAEDC-DNN) for real-time analysis of the probability of death (P<SUB>death</SUB>). VAEDC can extract representation features of the P<SUB>death</SUB> contour with complicated urban geometry in the latent space, and DNN maps the variable space into the latent space for the Pdeath image data. The chlorine gas leak accident in the Mipo complex (city of Ulsan, Republic of Korea) is used for verification of the model. The proposed model predicts the P<SUB>death</SUB> image within a mean squared error of 0.00246, and compared with other models, it exhibits superior performance. Furthermore, through the smoothness of image transition in the variable space, it is confirmed that image generation is not overfitting by data memorization.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The gas leak models of industrial scale using CFD is developed. </LI> <LI> Variational autoencoder is used for data reducing and feature extraction. </LI> <LI> Deep neural network is used for mapping the variable space into the latent space. </LI> <LI> 2D contour of probability of death is generated by surrogate model. </LI> <LI> Proposed surrogate model can predict CFD result with superior performance. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Optimal design and operation of Fischer-Tropsch microchannel reactor for pilot-scale compact Gas-to-Liquid process

Na, Jonggeol,Kshetrimayum, Krishnadash S.,Jung, Ikhwan,Park, Seongho,Lee, Yongkyu,Kwon, Okbae,Mo, Yonggi,Chung, Jongtae,Yi, Jongyeol,Lee, Ung,Han, Chonghun Elsevier 2018 CHEMICAL ENGINEERING AND PROCESSING Vol.128 No.-

<P><B>Abstract</B></P> <P>Design and operation of pilot-scale (1.0 BDP) compact GTL process comprising of reforming section, CO<SUB>2</SUB> separating section, and Fischer-Tropsch (FT) synthesis section is presented. Detailed systematic computer-aided design procedure adopted to design a modular 0.5 BPD pilot-scale microchannel reactor used in the pilot plant operation is also presented. The modular microchannel FT reactor block design consists of 528 process channels and numerous coolant channels arranged in cross-cocurrent-cross configuration for adequate heat removal. On average 98.27% CH<SUB>4</SUB> conversion to syngas in reforming section comprising of a pre-reformer unit and a tri-reformer unit, and CO<SUB>2</SUB> separation rate of 36.75% along with CO/H<SUB>2</SUB> reduction from 2.67 to 2.08 in CO<SUB>2</SUB> membrane separation section were achieved from the entire pilot plant operation duration of 450 h. Parallel operation of FT microchannel reactor and multitubular fixed-bed type FT reactor for comparison showed that multitubular fixed-bed type reactor undergoes reaction runaway for the applied process conditions, while microchannel reactor showed adequate temperature control. Overall CO conversion of 83% and adequate temperature control at three different applied operating temperatures of 220 °C, 230 °C, and 240 °C subsequently during the 139 h FT reactor operation demonstrated the appreciable performance of the present microchannel FT reactor designed.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Design and operation of pilot scale (1.0 BDP) compact GTL process. </LI> <LI> Systematic computer-aided design procedure is formulated. </LI> <LI> Reactor modeling and optimization using CFD and cell-coupling methods. </LI> <LI> Parallel operation FT microchannel reactor and multitubular fixed bed reactor. </LI> <LI> The designed reactor gives satisfactory temperature control. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Design of carbon dioxide dehydration process using derivative-free superstructure optimization

An, Jinjoo,Na, Jonggeol,Lee, Ung,Han, Chonghun Elsevier 2018 Chemical engineering research & design Vol.129 No.-

<P><B>Abstract</B></P> <P>A comprehensive optimal design for the CO<SUB>2</SUB> dehydration process created by decomposition-based superstructure optimization is proposed. To reach the most economical process configuration, the superstructure model has been developed including binary interaction parameter regression of the NRTL-RK thermodynamic model, unit operation modeling, and identification of the connectivity of each of the unit operations in the superstructure. The superstructure imbeds 30,720 possible process alternatives and unit operation options. To simplify the optimization problem, the process simulation was explicitly carried out in a sequential process simulator, and the constrained optimization problem was solved externally using a genetic algorithm and an Aspen Plus-MATLAB interface. The optimal process includes a five-stage contactor, a nine-stage still column (with the feed stream entering at the seventh stage), a lean/rich solvent heat exchanger, and a cold rich solvent split flow fed to the first stage of still column. The total annualized cost of the optimum process is 6.70M$/year, which corresponds to the specific annualized cost of 1.88$/t CO<SUB>2</SUB>. As part of the process optimization, a Monte Carlo simulation was performed to analyze the sensitivity of utility cost volatility; the refrigerant and steam present the most influential utility costs.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Optimal design of a CO<SUB>2</SUB> dehydration process is proposed using superstructure optimization. </LI> <LI> Binary interaction parameter regression for the NRTL-RK thermodynamic model is performed. </LI> <LI> Optimization problem is solved using a genetic algorithm and Aspen Plus-MATLAB interface. </LI> <LI> The optimal process includes a contactor, still column, lean/rich solvent heat exchanger, and cold rich solvent split flow. </LI> <LI> Sensitivity analysis for utility cost volatility using a Monte Carlo simulation is carried out. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Robust design of ambient-air vaporizer based on time-series clustering

Lee, Yongkyu,Na, Jonggeol,Lee, Won Bo Elsevier 2018 Computers & chemical engineering Vol.118 No.-

<P><B>Abstract</B></P> <P>A methodology for the robust design of an ambient-air vaporizer under time-series weather conditions is proposed. Two techniques are used to extract representative features in the time-series data. (i) The major trend of a day is rapidly identified by the discrete wavelet transform (DWT), in which a high level of Haar function reflects the trend of a day and drastically reduces the data size. (ii) The <I>k</I>-means clustering method groups the similar features of a year, and the reconstructed time-series dataset extracted by the centroids of clusters represents the weather conditions of a year. The results of the multi-feature-based optimization were compared with non-wavelet based and multi-period optimization by simulation under a year of data. The design structure from the feature extraction shows 22.92% better performance than the original case and is 12 times more robust in different weather conditions than clustering with raw data.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Proposed the robust design of an ambient-air vaporizer. </LI> <LI> Representative features are extracted in the time-series data. </LI> <LI> Discrete wavelet transform is used for extracting feature of day. </LI> <LI> <I>k</I>-means clustering is used for grouping the similar features of a year. </LI> <LI> Robust design shows 22.92% better performance than the original case. </LI> </UL> </P>

Process Design and Assessment of Electrochemical Carbon and Nitrogen Reduction Processes

Juyeon KIM,Jonggeol NA 한국생물공학회 2021 한국생물공학회 학술대회 Vol.2021 No.4

Multi-objective Bayesian optimization of chemical reactor design using computational fluid dynamics

Park, Seongeon,Na, Jonggeol,Kim, Minjun,Lee, Jong Min Elsevier 2018 Computers & chemical engineering Vol.119 No.-

<P><B>Abstract</B></P> <P>This study presents a computational fluid dynamics (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. Detailed methods used to automate the process by connecting CFD with MBO are also 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 aspect ratio of the tank, the diameter and clearance of each of the two impellers, and the gas sparger. The saturated Pareto front is obtained after 100 iterations. The resulting Pareto front consists of many near-optimal designs with significantly enhanced performances compared to conventional reactors reported in the literature. We anticipate that this design approach can be applied to any process unit design problems that require a large number of CFD simulation runs.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Multi-objective Bayesian optimization (MBO) using a CFD chemical reactor model is conducted. </LI> <LI> Automated optimal design procedure of chemical reactors based on CFD is proposed. </LI> <LI> Six design variables of gas-sparged stirred tank reactor are optimized and the saturated Pareto front are obtained within 90 CFD simulations. </LI> <LI> The optimized designs showed significantly enhanced power consumption and gas holdup compared to initial and conventional designs. </LI> </UL> </P>

마이크로채널 반응기를 이용한 강화된 저온 피셔-트롭쉬 합성반응의 전산유체역학적 해석

Krishnadash S. Kshetrimayum,나종걸(Jonggeol Na),박성호(Seongho Park),정익환(Ikhwan Jung),이용규(Yongkyu Lee),한종훈(Chonghun Han) 한국가스학회 2017 한국가스학회지 Vol.21 No.4

피셔-트롭쉬 합성반응은 CO와 H2의 혼합가스로 이루어진 합성가스를 부가가치가 높은 탄화수소 제품으로 변환시킨다. 본 논문에서는 저온 피셔-트롭쉬 합성반응과 단일, 다중 마이크로채널 반응기에 패킹시킨 촉매를 기반으로 강화된 반응조건의 열전달을 고려하여 전산유체역학 기반의 시뮬레이션을 진행하고 분석하였다. 단일채널모델을 통하여 CO 전환률이 ~65% 이상, C<SUB>5+</SUB> 선택도가 ~74% 이상을 달성하면서도 Co 기반의 super-active 촉매를 통해 GHSV를 30000 hr<SUP>-1</SUP>을 달성할 수 있음을 보였다. 다중 마이크로채널 반응기모델에서는 열전달 시뮬레이션을 동시에 해석하여, 3가지의 다른 반응기구조에 대해서, 직교류 wall boiling 냉매를 사용시 △T<SUB>max </SUB>가 23 K였으며 평행유동 subcooled 냉매와 평행유동 wall boiling 냉매의 경우 각각 15 K와 13 K의 △T <SUB>max</SUB> 를 보였다. 반응기 전체적으로 498 - 521 K에서 온도제어가 가능했으며 계산된 사슬성장 가능성은 저온 피셔-트롭쉬 합성에 적합한 것으로 보인다. Fischer-Tropsch synthesis reaction converts syngas (mixture of CO and H2) to valuable hydrocarbon products. Simulation of low temperature Fischer -Tropsch Synthesis reaction and heat transfer at intensified process condition using catalyst filled single and multichannel microchannel reactor is considered. Single channel model simulation indicated potential for process intensification (higher GHSV of 30000 hr<SUP>-1</SUP> in presence of theoretical Cobalt based super-active catalyst) while still achieving CO conversion greater than ~65% and C<SUB>5+</SUB> selectivity greater than ~74%. Conjugate heat transfer simulation with multichannel reactor block models considering three different combinations of reactor configuration and coolant type predicted ΔTmax equal to 23 K for cross-flow configuration with wall boiling coolant, 15 K for co-current flow configuration with subcooled coolant, and 13 K for co-current flow configuration with wall boiling coolant. In the range of temperature maintained (498 - 521 K), chain growth probability calculated is desirable for low-temperature Fisher-Tropsch Synthesis.

CFD를 이용한 마이크로채널 FT 반응기 기체 분산판의 구조영향 분석

정익환(Ikhwan Jung),나종걸(Jonggeol Na),이용규(Yongkyu Lee),박성호(Seongho Park),이종열(Yi Jong Yeol),정종태(Jongtae Chung),한종훈(Chonghun Han) 한국가스학회 2014 한국가스학회 학술대회논문집 Vol.2014 No.11

NARX modeling for real-time optimization of air and gas compression systems in chemical processes

Lee, Won Je,Na, Jonggeol,Kim, Kyeongsu,Lee, Chul-Jin,Lee, Younggeun,Lee, Jong Min Elsevier 2018 Computers & chemical engineering Vol.115 No.-

<P><B>Abstract</B></P> <P>This study considers the Nonlinear Autoregressive eXogenous Neural Net model (NARX NN) based real-time optimization (RTO) for industrial-scale air & gas compression system in a commercial terephthalic acid manufacturing plant. NARX model is constructed to consider time-dependent system characteristics using actual plant operation data. The prediction performance is improved by extracting the thermodynamic characteristics of the chemical process as a feature of this model. And a systematic RTO method is suggested for calculating an optimal operating condition of compression system by recursively updating the NARX model. The performance of the proposed NARX model and RTO methodology is exemplified with a virtual plant that simulates the onsite commercial plant with 99.6% accuracy. NARX with feature extraction model reduces mean squared prediction error with the actual plant data 43.5% compared to that of the simple feed-forward multi-perceptron neural networks. The proposed RTO method suggests optimal operating conditions that reduce power consumption 4%.</P> <P><B>Highlights</B></P> <P> <UL> <LI> NARX NN modeling for dynamic multi-stage compression system. </LI> <LI> First principle feature extraction method for circumventing overfitting and extrapolation. </LI> <LI> Real-time optimization framework of air and gas supply network with NARX NN. </LI> <LI> Improvement of prediction performance about 43.5% compared to a conventional method. </LI> <LI> Reduced energy consumption by approximately 4% compared to conventional operation. </LI> </UL> </P>