First, clarify the core analytical indicators and grasp the key data

The cone calorimeter can accurately output a variety of key data, and the first step in the analysis is to lock in the core indicators. The heat release rate (HRR) is the core, which reflects the heat released by the material per unit time. The higher the peak, the greater the risk of fire spread; the effective heat of combustion (EHC) reflects the effective heat released by the combustion of the material per unit mass, which can judge the combustion reaction intensity; the mass loss rate (MLR) records the mass change of the material during combustion, which can visually show the material combustion loss rate; in addition, the flue gas generation rate (SPR) and the total flue gas generation (TSP) are used to evaluate the degree of harm of the flue gas during the combustion of the material. These indicators together form the basic framework of data analytics.

Disassemble the data step by step and clarify the analysis logic

1. Data preprocessing: first check the integrity of the original data source, eliminate abnormal values caused by instrument errors and environmental disturbances, ensure that the data is true and reliable, and build a solid foundation for subsequent analysis.

2. Single index depth analysis: disassemble each core indicator one by one. For example, to analyze the heat release rate, it is necessary to pay attention to the peak time, peak size and curve trend. If the peak occurs early and the value is high, it means that the material is easy to burn quickly; analyze the quality loss rate, combined with the curve slope, the greater the slope, the faster the material combustion speed, and accurately judge the material combustion stability.

3. Multi-index correlation analysis: It is difficult for a single index to fully reflect the combustion performance of materials, and it is necessary to comprehensively judge multiple indicators. For example, high heat release rate and high effective combustion heat indicate that the material is fully burned and the fire is fierce; if the heat release rate is high but the flue gas generation rate is also high, it means that the material combustion is not only large, but also accompanied by a large amount of toxic smoke, which is more harmful. Through correlation analysis, the material combustion characteristics can be fully evaluated.

III. Combining application scenarios to realize the value of data

The ultimate goal of data analytics is to serve practical applications. If it is used for building material evaluation, it is necessary to focus on controlling the heat release rate and flue gas generation to ensure that the material meets fire safety standards; if it is used for flame retardant material research and development, it can compare the material data of different formulas to find the optimal formula with low heat release rate and slow quality loss, providing an accurate basis for research and development, so that the data can be effectively converted into practical application value.

By mastering the above methods, the analysis logic of cone calorimeter data can be quickly sorted out, and the material combustion performance evaluation can be completed efficiently, providing strong support for Product Research & Development, safety testing and other work.