The chemical processing industry has relied on modeling techniques for process monitoring, control, diagnosis, optimization, and design, especially since the third industrial revolution and the emergence of Process Systems Engineering. The fourth industrial revolution, connected to massive digitization, made it possible to collect and process large volumes of data triggering the development of data-driven frameworks for knowledge extraction. However, one must not leave behind the successful solutions developed over decades based on first principle mechanistic modeling approaches. At present, both industry and researchers are realizing the need for new ways to incorporate process and phenomenological knowledge in big data and machine learning frameworks, leading to more robust and intelligible artificial intelligence solutions, capable of assisting the target stakeholders in their activities and decision processes. In this article, we review hybrid modeling techniques, associated system identification methodologies and model assessment criteria. Applications in chemical and biochemical processes are also referred.

化学加工行业一直依靠建模技术来进行过程监控，控制，诊断，优化和设计，特别是自第三次工业革命和过程系统工程的出现以来。与大规模数字化相关的第四次工业革命使收集和处理大量数据成为可能，从而触发了由数据驱动的知识提取框架的开发。但是，绝不能忘记基于第一原理力学建模方法数十年来开发的成功解决方案。目前，行业和研究人员都意识到了将过程和现象学知识整合到大数据和机器学习框架中的新方法的需求，从而带来了更强大，更易懂的人工智能解决方案，能够协助目标利益相关者的活动和决策过程。在本文中，我们回顾了混合建模技术，相关的系统识别方法和模型评估标准。还提到了在化学和生化过程中的应用。