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Received June 12, 2023
Revised July 31, 2023
Accepted August 3, 2023
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화학 공정 설계 및 분석을 위한 설명 가능한 인공지능 대안 모델

Explainable Artificial Intelligence (XAI) Surrogate Models for Chemical Process Design and Analysis

Ewha Womans University
Korean Chemical Engineering Research, November 2023, 61(4), 542-549(8), 10.9713/kcer.2023.61.4.542 Epub 1 November 2023
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대안 모델링에 대한 관심이 커진 이후 데이터 기반의 기계학습을 이용하여 비선형 화학 공정을 모사하고자 하는

연구가 지속되고 있다. 그러나 기계 학습 모델의 black box 성질로 인하여 모델의 해석 가능성에 한계는 산업 적

용에 걸림돌이 되고 있다. 따라서, 모델의 정확도가 보장된 상태에서 해석력을 부여하는 개념인 설명 가능한 인공

지능(explainable artificial intelligence, XAI)을 이용하여 화학 공정 분석을 시도하고자 한다. 기존의 화학 공정 민

감도 분석이 변수의 민감도 지수를 계산하고 순위를 매기는 데에 그쳤다면, XAI를 이용하여 전역적, 국소적 민감

도 분석뿐만 아니라 변수들 간의 상호작용에 대하여 분석하여 데이터로부터 물리적 통찰을 얻어내는 방법론을 제

안한다. 사례 연구의 대상공정인 암모니아 합성 공정에 대하여 첫번째 반응기로 향하는 흐름에 대한 예열기(preheater)

의 온도, 세 반응기로 향하는 cold-shot의 분배 비율을 공정 변수로 설정하였다. Matlab과 Aspen plus를 연동하여

공정 변수를 바꿔가면서 암모니아의 생산량과 세 반응기의 최고 온도에 대한 데이터를 얻었으며, tree 기반의 모델

들을 훈련시켰다. 그리고 성능이 좋은 모델에 대하여 XAI 기법 중 하나인 SHAP 기법을 이용하여 민감도 분석을

수행하였다. 전역적 민감도 분석 결과, 예열기의 온도가 가장 큰 영향을 미쳤으며 국소적 민감도 분석 결과에서 생

산성 향상 및 과열 방지를 위한 공정 변수들의 범위를 규정할 수 있었다. 이처럼 화학 공정의 대안 모델을 구축하

고 설명 가능한 인공지능을 이용해 민감도 분석을 진행하는 방법론을 통해 공정 최적화에 대한 정량적, 정성적 피

드백을 제안하는 데 도움을 줄 것이다.

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating

nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning

models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study

aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability

while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating

and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global

and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data.

For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater

leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating

Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors

while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis

using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed

that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the

ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and

qualitative feedback for process optimization.


1. Nentwich, C. and Engell, S., “Application of Surrogate Models
for the Optimization and Design of Chemical Processes,” 2016
International Joint Conference on Neural Networks (IJCNN),
IEEE, 1291-1296(2016).
2. McBride, K. and Sundmacher, K., “Overview of Surrogate
Modeling in Chemical Process Engineering,” Chemie Ingenieur
Technik 91(3), 228-239(2019).
3. Murphy, K. P., Machine Learning: A Probabilistic Perspective,
MIT press(2012).
4. Chung, W. and Lee, J. H., “Input-output Surrogate Models for
Efficient Economic Evaluation of Amine Scrubbing co2 Capture
Processes,” Industrial & Engineering Chemistry Research
59, 18951-18964(2020).
5. Shokry, A., Baraldi, P., Zio, E. and Espuña, A., “Dynamic Surrogate
Modeling for Multistep-ahead Prediction of Multivariate
Nonlinear Chemical Processes,” Industrial & Engineering Chemistry
Research 59, 15634-15655(2020).
6. Gunning, D. and Aha, D., “Darpa’s Explainable Artificial Intelligence
(xai) Program,” AI Magazine 40, 44-58(2019).
7. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A.,
Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D. and
Benjamins, R., “Explainable Artificial Intelligence (xai): Concepts,
Taxonomies, Opportunities and Challenges Toward Responsible
Ai,” Information Fusion 58, 82-115(2020).
8. Dobbelaere, M. R., Plehiers, P. P., Van de Vijver, R., Stevens, C.
V. and Van Geem, K. M., “Machine Learning in Chemical Engineering:
Strengths, Weaknesses, Opportunities, and Threats,”
Engineering 7(9), 1201-1211(2021).
9. Lundberg, S. M. and Lee, S.-I., “A Unified Approach to Interpreting
Model Predictions,” Advances in Neural Information Processing
Systems 30, 4765-4774(2017).
10. Antwarg, L., Miller, R. M., Shapira, B. and Rokach, L., “Explaining
Anomalies Detected by Autoencoders Using Shapley Additive
Explanations,” Expert Systems with Applications 186, 115736(2021).
11. Jang, K., Pilario, K. E. S., Lee, N., Moon, I. and Na, J., “Explainable
Artificial Intelligence for Fault Diagnosis of Industrial Processes,”
IEEE Transactions on Industrial Informatics(2023).
12. Jung, W., Kim, H., Ryu, H. W., Lim, Y. H., Kim, D. H. and Lee,
J., “Process Analysis for the Simultaneous Production of Aromatics
and Syngas from Shale Gas and CO2,” Energy Conversion
and Management 276, 116480(2023).
13. Chakkingal, A., Janssens, P., Poissonnier, J., Virginie, M., Khodakov,
A. Y. and Thybaut, J. W., “Multi-output Machine Learning
Models for Kinetic Data Evaluation: A Fischer–tropsch Synthesis
Case Study,” Chemical Engineering Journal 446, 137186(2022).
14. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.
M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N. and Lee, S.-I.,
“From Local Explanations to Global Understanding with Explainable
ai for Trees,” Nature Machine Intelligence 2(1), 56-57(2020).
15. Borgonovo, E. and Plischke, E., “Sensitivity Analysis: A Review
of Recent Advances,” European Journal of Operational Research
248(3), 869-887(2016).
16. Na, J., Lim, Y. and Han, C., “A Modified Direct Algorithm for
Hidden Constraints in An Lng Process Optimization,” Energy
126, 488-500(2017).
17. Seider, W. D., Lewin, D. R., Seader, J., Widagdo, S., Gani, R.
and Ng, K. M., Product and Process Design Principles: Synthesis,
Analysis, and Evaluation, John Wiley & Sons(2017).
18. Temkin, M., “Kinetics of Ammonia Synthesis on Promoted Iron
Catalysts,” Acta physiochim. URSS 12, 327-356(1940).
19. Modak, J. M., “Haber Process for Ammonia Synthesis,” Resonance
7, 69-77(2002).
20. Breiman, L., Friedman, J., Olshen, R. and Stone, C., “Classification
and Regression Trees. Wadsworth Int,” Group 37(15),
21. Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P. and
Feuston, B. P., “Random Forest: A Classification and Regression
Tool for Compound Classification and Qsar Modeling,” Journal
of Chemical Information and Computer Sciences 43, 1947-1958
22. Friedman, J. H., “Greedy Function Approximation: A Gradient
Boosting Machine,” Annals of Statistics 29(5), 1189-1232(2001).
23. Friedman, J. H., “Greedy Function Approximation: A Gradient
Boosting Machine,” Annals of Statistics, 1189(2001).
24. McRae, G. J., Tilden, J. W. and Seinfeld, J. H., “Global Sensitivity
Analysis—a Computational Implementation of the Fourier Amplitude
Sensitivity Test (FAST),” Computers & Chemical Engineering
6, 15-25(1982).

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