Key Takeaways & Executive Findings
- •• • The dataset compiles experimental data from 10 literature sources, standardizing catalyst composition, reaction conditions, and outcomes (conversion, selectivity, yield) to enable direct comparative analysis and machine learning modeling. • • Data are provided in Excel (.xlsx) format, accessible via GitHub (https://github.com/ttoono-takaki/Code-and-dataset.git) and ScienceDB (DOI: 10.57760/sciencedb.27385), ensuring reproducibility and reuse. • • The dataset supports statistical analysis and visualization (Figure 1) to identify key influencing factors and correlations, facilitating data-driven experimental design and catalyst screening. • • Quality control procedures address biases from inter-laboratory differences in apparatus and analysis, enhancing data reliability for subsequent modeling and industrial scale-up considerations.
Abstract
Cyclohexene is a crucial raw material for nylon production, and the selective hydrogenation of benzene is a key route for its preparation. To promote data sharing and reuse in this field, we collected and standardized experimental data on the hydrogenation of benzene to cyclohexene from publicly available literature, constructing a comprehensive dataset containing catalyst composition, reaction conditions, and reaction results (conversion, selectivity, and yield). This data descriptor details the source, field definitions, generation and processing workflow, quality control, sharing approach, and usage recommendations of the dataset, aiming to provide a reusable data foundation for subsequent statistical analysis, machine learning modeling, experimental design, and catalyst screening. The dataset is provided in Excel format and is accessible via GitHub and ScienceDB. It addresses the lack of unified field definitions, unit systems, and organizational formats in scattered literature data, enabling direct statistical analysis, correlation mining, and predictive modeling. The dataset is expected to accelerate research in optimizing ruthenium-based catalytic systems for selective benzene hydrogenation, which currently suffer from limited cyclohexene yield despite the use of aqueous-phase systems and inorganic salt additives. By offering a quality-controlled, structured dataset, this work supports data-driven approaches to overcome the thermodynamic favorability of complete hydrogenation to cyclohexane and to improve process economics.
1. Introduction
The selective hydrogenation of benzene to cyclohexene is industrially significant, yet thermodynamically, complete hydrogenation to cyclohexane is favored. This necessitates precise kinetic control through catalyst and process optimization. Existing ruthenium-based catalytic systems, often employing aqueous-phase conditions and inorganic salt additives, have achieved only limited cyclohexene yields. The complexity of heterogeneous catalysis and the high cost of generating large, comparable experimental datasets impede progress. Literature data, while abundant, suffer from inconsistent field definitions, units, and formats, hindering data-driven analysis and modeling.
This work addresses these bottlenecks by constructing a comprehensive, quality-controlled dataset from publicly available literature, standardizing catalyst composition, reaction conditions, and performance metrics. The dataset enables direct statistical analysis, correlation mining, and machine learning modeling, providing a reusable foundation for experimental design and catalyst screening. By overcoming data fragmentation, this resource accelerates the development of more selective and productive catalysts for cyclohexene production, targeting improved process economics and industrial viability.
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SUN Chao, ZHANG Bin (2026). Collection, Processing, and Sharing of a Dataset for the Selective Hydrogenation of Benzene to Cyclohexene. Journal of Fuel Chemistry and Technology. https://doi.org/10.1016/S1872-5813(26)60748-7
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Frequently Asked Questions
What specific catalyst compositions and reaction conditions are covered in the dataset, and how are they standardized?
The dataset includes catalyst composition (e.g., ruthenium-based systems), reaction conditions (temperature, pressure, solvent, additives), and performance metrics (conversion, selectivity, yield). Fields are standardized with unified units and definitions, as detailed in the data descriptor, ensuring comparability across entries.
How does the dataset address potential biases from inter-laboratory variations in experimental apparatus and analysis?
The data descriptor acknowledges biases from differences in experimental apparatus and analytical methods. Quality control procedures include source documentation and standardized processing, but users are advised to consider these biases when performing statistical analysis or modeling.
What is the volume of the dataset in terms of number of data points or experiments?
The abstract and text do not specify the exact number of data points. However, the dataset aggregates data from at least 10 literature sources, providing a substantial collection for analysis.
Can the dataset be used to train machine learning models for predicting cyclohexene yield?
Yes, the dataset is structured to support machine learning/regression modeling. It includes catalyst composition, reaction conditions, and outcomes, enabling predictive modeling for yield and selectivity. The data descriptor recommends usage for such purposes.
What are the limitations of the dataset regarding industrial scalability?
The dataset is derived from laboratory-scale experiments reported in literature. While it provides a foundation for catalyst screening and condition optimization, users must validate findings under industrially relevant conditions (e.g., higher pressures, continuous operation) and consider mass transfer effects not captured in the dataset.
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