[논문]암모니아 합성 및 분해를 위한 촉매 탐색의 최근 연구 동향

김종영; 여병철

doi:10.9713/kcer.2023.61.4.487

[국내논문] 암모니아 합성 및 분해를 위한 촉매 탐색의 최근 연구 동향
Recent Research Trends of Exploring Catalysts for Ammonia Synthesis and Decomposition 원문보기

Korean chemical engineering research = 화학공학, v.61 no.4, 2023년, pp.487 - 495

김종영 (부경대학교 에너지자원공학과) , 여병철 (부경대학교 에너지자원공학과)

초록
AI-Helper

암모니아는 인류의 식량문제를 해결할 수 있는 비료 생산의 주요 원료임과 동시에 무탄소 연료이면서 친환경적인 수소 운반자로서 중요한 에너지원으로 알려져 있다. 그래서 지금까지도 암모니아를 합성하거나 분해하는 기술들이 각광을 받고 있다. 암모니아 합성 및 분해 반응을 촉진시키기 위해서는 반드시 촉매 재료가 필요하다. 고성능 및 값싼 암모니아 합성 및 분해용 신촉매를 설계하기 위해서는 무수히 많은 합성 가능한 촉매 후보군들을 다루어야만 하는데 전통적인 접근법만으로 탐색 및 분석을 하기엔 시간적, 경제적인 비용이 많이 들 수밖에 없다. 최근에 4차 산업혁명의 핵심기술에 속하는 머신러닝을 이용하여 이용하여 고성능 촉매를 빠르고 정확하게 찾을 수 있는 탐색 모델이 개발되어 왔다. 본 연구에서는 암모니아 합성 및 분해용 반응 메커니즘에 대해서 알아보고, 고성능 및 경제적인 암모니아 합성 및 분해 촉매를 효율적으로 탐색할 수 있는 머신러닝 기반 방법에 대한 최신 연구 및 전망을 기술하였다.

Abstract ▼ AI-Helper

Ammonia is either a crucial resource of fertilizer production for solving the food problem of mankind or an important energy source as both an eco-friendly hydrogen carrier and a carbon-free fuel. Therefore, nowadays ammonia synthesis and decomposition become promising. Then, a catalyst is required to effectively perform the ammonia synthesis and decomposition. In order to design high-performing as well as cheap novel catalysts for ammonia synthesis and decomposition, it is necessary to test huge amount of catalyst candidates, but it is inevitably time-consuming and expensive to search and analyze using only traditional approaches. Recently, new methods using machine learning which is one of the core technologies of the 4th industrial revolution that can quickly and accurately search high-performance catalysts has been emerging. In this paper, we investigate reaction mechanisms of ammonia synthesis and decomposition, and we described recent research and prospects of machine learning-driven methods that can efficiently find high-performing and economical catalysts for ammonia synthesis and decomposition.

주제어

표/그림 (11)

그림 Fig. 1. Potential utilization of ammonia synthesis and decomposition in energy system [9].
그림 Fig. 2. Conventional process for ammonia production [19].
그림 Fig. 3. Schematic diagrams of dissociative mechanism for ammonia synthesis on Ru(0001) and Fe(111)/Fe(211) surfaces [19].
그림 Fig. 4. Schematic depiction of distal, alternating, and enzymatic mechanisms for nitrogen reduction reaction [26].
그림 Fig. 5. Correlation between the rate of ammonia decomposition on several metals and the relative rate of N-H bond scission activation energy [29].
그림 Fig. 6. Schematic diagram of both G-M mechanism and O-S mechanism for AOR reaction from existing studies [32].
그림 Fig. 7. Schematic representation of convolutional neural network (CNN) architecture [44].
표 Table 1. Recent machine learning algorithms for predicting catalytic properties
그림 Fig. 9. Schematic representation of the slab graph convolutional neural network (SGCNN) architecture [17].
그림 Fig. 8. (a) Distribution of the adsorption energies in the database (N, H, NH, NH, and NH₃) and (b) comparison between adsorption energy prediction based on ML and DFT values on the testing set for N [18].
그림 Fig. 10. (a) Distributions of the adsorption energies (H, N₂, N₂H, NH, and NH₂) and (b) comparison between the predicted adsorption energies using SGCNN model and DFT computation [17].

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동의어: Packet Collision Rate

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