Process manufacturing serves as the pillar of the continuous manufacturing industry such as oil, gas, chemicals, nonferrous metals, iron, and steel, and thus is closely related to almost every aspect of human life. On the one hand, in order to meet several urgent but challenging demands of increasing profits, reducing materials consumption, enhancing safety, and protecting the environment, it is necessary to facilitate the development of process manufacturing with the usage of some novel and advanced techniques such as artificial intelligence (AI) and computation intelligence (CI). On the other hand, with the increasing scale of process manufacturing, another challenge is how to effectively deal with a huge amount of industrial big data in the process industry for environmental perception, modelling, optimization, decision-making, autonomous intelligent control, fault detection, and risk analysis. Therefore, it is of fundamental importance to deeply integrate AI, CI, and data sciences to achieve accurate control and optimal decision-making for process industries.
|Number of pages||2|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Publication status||Published - Aug 2021|