Semantic mining is an essential part in knowledgebase and decision support systems where it enables the extraction of useful knowledge form available databases with the ultimate goal of supporting the decision making process. In process systems engineering, decisions are made throughout plant / process / product life cycles. The provision of smart semantic mining techniques will improve the decision making process for all life cycle activities. In particular, safety and environmental related decisions are highly dependent on process internal and external conditions and dynamics with respect to equipment geometry and plant layout. This chapter discusses practical methods for semantic mining using systematic knowledge representation as integrated with process modeling and domain knowledge. POOM or plant/process object oriented modeling methodology is explained and used as a basis to implement semantic mining as applied on process systems engineering. Case studies are illustrated for biological process engineering, in particular MoFlo systems focusing on process safety and operation design support.
Homogenous multi-agent optimization for process systems engineering problems with a case study of computer aided molecular design
