Value-added Transformation of Ethanol to n-Butanol: a Machine Learning Study

doi:10.6023/A26040110

Abstract

The conversion of bioethanol to n-butanol represents a crucial pathway for the long-term storage and high-value utilization of renewable energy. Traditional trial-and-error approaches to obtain efficient catalysts for n-butanol synthesis from vast synthetic spaces are time-consuming, labor-intensive, and inefficient, while being incapable of covering extensive material combinations. This study introduces machine learning methodologies to explore a data-driven research paradigm for catalyst screening and design aimed at improving n-butanol yield. We collected literature data on heterogeneous catalysis for ethanol-to-n-butanol conversion since 2000, establishing a comprehensive catalyst dataset encompassing catalyst composition, preparation methods, structural characteristics, reaction conditions, and corresponding catalytic performance. Nine regression algorithms were evaluated based on this dataset, with the eXtreme Gradient Boosting (XGBR) model achieving coefficient of determination (R²) values of 0.986 and 0.897 for the training and test sets, respectively, demonstrating superior predictive performance and generalization capability compared to other models. Coupled with Bayesian optimization, five high-performance candidate catalysts were efficiently identified within the n-butanol synthesis catalyst parameter space. This work provides novel insights for the efficient screening and rational design of catalysts for ethanol-to-n-butanol conversion and opens a data-driven avenue for the high-value and efficient utilization of ethanol.

Key words: ethanol catalytic conversion, n-butanol, machine learning, catalyst screening, Bayesian optimization

Cite this article

Yuming Zhang, Shitao Sun, Shijia Zhou, Junqing Li, Lei He, Dongqi Wang. Value-added Transformation of Ethanol to n-Butanol: a Machine Learning Study[J]. Acta Chimica Sinica, 2026, 84(9): 1464-1474.

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Fig. & Tab. 11

Figure 1. Distribution of catalyst components in the dataset: (a) major active metals; (b) types of support materials

Figure 2. A scatter plot of ethanol conversion rate, n-butanol selectivity, and n-butanol yield (where yield values are represented by different colors according to their magnitude)

Figure 3. Histogram of catalyst performance distribution in the dataset

Figure 4. ETR models for predicting missing values of specific characteristics: (a) specific surface area; (b) pore volume; (c) base-to-acid ratio; (d) gravimetric space velocity

Figure 5. Correlation analysis among all input features: (a) Pearson correlation; (b) Spearman correlation

Figure 6. Correlation analysis between screened features: (a) Pearson correlation; (b) Spearman correlation

ML model	R²-train	R²-test	RMSE(%)-train	RMSE(%)-test
LR	0.350	0.373	7.39	7.62
RR	0.350	0.372	7.39	7.62
LLR	0.311	0.307	7.61	8.01
KNN	0.856	0.753	3.48	4.78
DTR	0.851	0.707	3.54	5.20
RFR	0.942	0.854	2.21	3.68
ETR	0.987	0.868	1.04	3.50
GBDT	0.975	0.869	1.46	3.48
XGBR	0.986	0.897	1.07	3.09

Table 1. Comparison of regression results for different machine learning algorithms

Figure 7. Validation results of different machine learning models. LR: Linear Regression; RR: Ridge Regression; LLR: Lasso Regression; KNN: K-Nearest Neighbor; DTR: Decision Tree Regression; RFR: Random Forest Regression; ETR: Extremely-randomized Trees Regression; GBDT: Gradient Boosting Decision Tree; XGBR: eXtreme Gradient Boosting Regression

Figure 8. Evaluation of prediction accuracy for different machine learning models: (a) Model coefficient of determination, R²; (b) Root mean square error, RMSE (%)

Figure 9. The feature importance derived from SHAP analysis (from top to bottom, importance decreases progressively)

No.	Metal	Support	Reaction temperature/℃	Butanol yield/%
1	Au	MgAlO_x	334	57.1
2	Ag	NaX	322	53.9
3	Cu	NC	344	51.1
4	Ca	NC	332	48.7
5	Be	NC	327	46.9

Table 2. Model prediction results for candidate catalysts for high-performance n-butanol synthesis

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