Artificial intelligence (AI) has recently gained prominence for addressing complex problems in chemical plants. Despite its enthusiastic attention, the industrial application of AI is limited due to a lack of both reliability and diversity in its operation data at the plant scale. To address this issue, a framework that integrates the machine learning (ML) model and first-principles approach is proposed herein. The performance of the proposed framework is demonstrated by its application to the control system of the liquefied natural gas fuel gas supply system. In this framework, commercial simulation software was used to implement a high-accuracy first-principles model using operation data. Thereafter, a wide range of data was generated that cannot be obtained in an industrial plant. The generated data was fed to the ML model that predicted the control performance with variations of the control parameters. The ML model, built with high-quality data, can predict the control performance with high accuracy. The optimal control parameters were quickly found using the ML model, thereby improving the control performance. This study presents a solution that can overcome the limitations of using an ML model alone by exploiting the advantages of both the first-principles and data-driven approaches at the plant scale.

中文翻译：

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用于优化化学过程控制参数的第一原理模型和机器学习的混合框架


人工智能 (AI) 最近因解决化工厂的复杂问题而受到重视。尽管受到热烈关注，但由于工厂规模的运行数据缺乏可靠性和多样性，人工智能的工业应用受到限制。为了解决这个问题，本文提出了一种集成机器学习（ML）模型和第一原理方法的框架。该框架的性能通过其在液化天然气燃气供应系统控制系统中的应用得到了证明。在此框架中，使用商业仿真软件利用运行数据实现高精度第一原理模型。此后，产生了大量在工厂中无法获得的数据。生成的数据被输入到机器学习模型中，该模型根据控制参数的变化来预测控制性能。使用高质量数据构建的机器学习模型可以高精度预测控制性能。利用机器学习模型快速找到最优控制参数，从而提高控制性能。这项研究提出了一种解决方案，可以通过在工厂规模上利用第一原理和数据驱动方法的优势来克服单独使用机器学习模型的局限性。