Application Analysis of Orthogonal Design Based on Machine Learning

本文探讨了化学实验数据在优化工艺条件方面的应用。首先对实验数据进行了预处理。随后，应用多种机器学习算法进行建模，并对各模型的性能进行了比较。结果表明，随机森林模型有较高的准确性。同时，本文还引入了正交试验设计方法。该方法通过优化实验组合减少试验次数，降低实验成本。在正交设计的基础上，确定了最优的工艺条件：Co/SiO2与HAP的装料比为200:200，乙醇浓度为0.3 ml/min，反应温度为400℃。此外，通过贡献率分析，研究发现温度(因子C)对C4烯烃收率的影响最大，其贡献率高达76.41%。这一发现为进一步优化实验条件提供了重要参考。结合机器学习算法与正交试验设计，不仅能够有效减少试验次数，还能显著降低实验成本，为工业化学实验的优化提供了一种科学高效的途径，推动了机器学习算法在工业领域的应用，助力数字化转型。This paper explores the application of chemical experimental data in optimizing process conditions. The experimental data is first preprocessed. Subsequently, several machine learning algorithms are applied to model the data, and the performance of each model is compared. The results show that the random forest model has high accuracy. Additionally, this paper introduces the orthogonal experimental design method. This method optimizes experimental combinations, reducing the number of trials and lowering experimental costs. Based on orthogonal design, the optimal process conditions are determined: a Co/SiO2 to HAP loading ratio of 200:200, an ethanol concentration of 0.3 ml/min, and a reaction temperature of 400˚C. Furthermore, through contribution rate analysis, it is found that temperature (factor C) has the greatest impact on the C4 olefin yield, with a contribution rate of 76.41%. This discovery provides an important reference for further optimizing experimental conditions. The combination of machine learning algorithms and orthogonal experimental design not only effectively reduces the number of trials but also significantly lowers experimental costs, offering a scientifically efficient approach to optimizing industrial chemical experiments. This contributes to the application of machine learning algorithms in the industrial sector and aids in digital transformation.