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.

XAI, Process analysis, Surrogate modeling, Process systems engineering

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