关键词: 机器学习, 催化剂, 氧化, 性能预测, 优化设计

Abstract:

Ammonia energy catalytic combustion technology has become a hot topic in clean energy research due to its high thermal efficiency and low pollution characteristics. The core challenge lies in the development of efficient catalysts. However, traditional catalyst design predominantly relies on experience-driven trial-and-error methodologies, which are often plagued by extended development cycles and elevated costs. A machine learning (ML)-enabled approach aimed at the targeted development of catalysts has been introduced. By integrating both experimental data and literature findings, two comprehensive databases were constructed: one comprising 597 samples related to ammonia conversion rates and another containing 529 samples pertaining to nitrogen selectivity. Both databases encompass various catalyst compositions (e.g., Fe₂O₃, CuO, CeO₂) as well as reaction parameters (e.g., temperature, equivalent ratio). Three ensemble learning models,random forest regression (RFR), gradient boosting decision tree (GBDT), and categorical boosting (CatBoost),were employed to predict catalytic performance. The results demonstrated that the RFR model exhibited superior comprehensive performance in dual-target prediction. On the test set, the model achieved a coefficient of determination (R²) of 0.912 with a mean absolute error (MAE) of 0.047 for ammonia conversion rate prediction, while for nitrogen selectivity prediction, it attained an R² of 0.918 and MAE of 0.033, indicating enhanced accuracy with reduced prediction errors. Feature importance analysis revealed reaction temperature as the dominant factor influencing both ammonia conversion rate and nitrogen selectivity. Partial dependence plot (PDP) analysis uncovered significant synergistic effects between CuO and CeO₂ loadings. The model predicted nonlinear correlations between bimetallic loading ratios and catalytic performance at 500, 700, and 900℃. Experimental validation confirmed strong agreement between predicted and measured values, with prediction errors below 3%, achieving precise catalyst performance evaluation. This machine learning-driven paradigm revolutionizes traditional catalyst development approaches, reducing development cycles by over 60% and substantially lowering costs. The established transferable methodological framework provides critical insights for designing catalytic systems in clean energy applications, demonstrating significant engineering value through its generalizability and operational efficiency enhancement.

Key words: machine learning, catalyst, oxidation, performance prediction, optimal design