This study aims to develop a deep-learning-based and plant data-driven framework for process modeling to help understanding plant-wide processes. The systematic framework consists of the following steps: data processing based on domain-knowledge, deep-learning model development, model selection using information criteria, and global sensitivity analysis with Monte-Carlo simulations. The assessment of the quality of the optimal deep-learning model to support plant-wide process understanding is the key emphasis of this framework. The proposed framework was applied for analyzing long-term data from wastewater treatment plants to predict nitrous oxide emission characteristics. The results showed a promising potential of the framework to systematically and efficiently develop fit-for-purpose deep-learning models with highly favorable cross-validation statistics (R²). The framework is expected to facilitate the development of versatile deep-learning models based on plant data encompassing nonlinear and complex process phenomena, where especially mechanistic models are not available.

这项研究旨在为过程建模开发基于深度学习和工厂数据驱动的框架，以帮助理解工厂范围的过程。该系统框架包括以下步骤：基于领域知识的数据处理，深度学习模型开发，使用信息标准的模型选择以及使用蒙特卡洛模拟的全局敏感性分析。评估最佳深度学习模型的质量以支持整个工厂的过程理解是此框架的重点。拟议的框架被用于分析废水处理厂的长期数据，以预测一氧化二氮的排放特征。²）。该框架有望促进基于工厂数据的通用深度学习模型的开发，其中包括非线性和复杂的过程现象，尤其是在没有机械模型的情况下。