Light olefins (ethylene and propylene) have become prominent in chemical industries. Forecasting of the yields of light olefins plays a crucial role in monitoring and optimizing the Methanol-to-olefins (MTO) process. In this work, we introduce an approach for forecasting the yields of ethylene and propylene in the MTO process with the Relevance Vector Machine (RVM) model, which is uniquely enhanced with hybrid kernels and a rolling window methodology. Through an in-depth analysis of 32 independent variables and their pairwise differences, our research pinpoints temperature and pressure as the most critical factors influencing the yields of ethylene and propylene, respectively. The model showcases satisfactory predictive accuracy and reasonable interpretability compared with the traditional statistical and popular machine learning models, marking a step forward in the predictive modeling of chemical engineering processes.

中文翻译：

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基于具有混合内核和滚动窗口的相关性向量机预测甲醇制烯烃产品产量


轻质烯烃（乙烯和丙烯）在化学工业中已占有重要地位。预测低碳烯烃的收率在监测和优化甲醇制烯烃 （MTO） 工艺中起着至关重要的作用。在这项工作中，我们介绍了一种使用相关性向量机 （RVM） 模型预测 MTO 过程中乙烯和丙烯产量的方法，该方法通过混合内核和滚动窗口方法进行了独特增强。通过对 32 个自变量及其成对差异的深入分析，我们的研究指出温度和压力分别是影响乙烯和丙烯产量的最关键因素。与传统的统计和流行的机器学习模型相比，该模型展示了令人满意的预测准确性和合理的可解释性，标志着化学工程过程的预测建模向前迈进了一步。