Process Systems Engineering (PSE) is now a mature field with a well-established body of knowledge, computational-oriented frameworks and methodologies designed and implemented for addressing chemical processes related problems spanning a wide range of scales in time and space. A common feature of many PSE approaches relies in their mostly deductive nature, based on a deep understanding of the underlying Chemical Engineering Science. Given the current data-intensive industrial and societal contexts, new sources of process or product information are now easily made available and should be exploited to complement and expand the classical PSE paradigm with inductive data-driven reasoning and knowledge discovery methodologies. In this article, based upon our over 25 years of research and teaching experience in the field, we discuss the scope and trends of this PSE evolution, refer to several relevant Data-Centric PSE approaches, and identify the main components, applications and future opportunities of this PSE 4.0 perspective.

过程系统工程 (PSE) 现在是一个成熟的领域，拥有完善的知识体系、面向计算的框架和方法论，旨在解决跨越广泛时间和空间尺度的化学过程相关问题。许多 PSE 方法的一个共同特征在于，它们主要是基于对基础化学工程科学的深入理解的演绎性质。鉴于当前的数据密集型工业和社会背景，现在可以轻松获得过程或产品信息的新来源，并且应该利用归纳数据驱动推理和知识发现方法来补充和扩展经典 PSE 范式。在本文中，基于我们在该领域超过 25 年的研究和教学经验，