Abstract
Previous research has shown that knowledge-based optimization models in process synthesis applications are more robust in both providing final outputs and improving computational performance. This expands this approach by implementing a general knowledge models which in turn enables interpretation of solutions so that non-experts understand detailed procedures of optimization. To this end, an automatic ontology based optimization system that links rule-based optimization model and ontology has been introduced for the purpose to both improve optimization performance and to present new extracted knowledge at optimization run-time. A benchmark reactor network design synthesis case is studied for comparison of performance.The concomitant results show that not only can ontology-based optimization system improve robustness of solutions and computational performance, but also it enables a more accurate understanding of the process synthesis procedures and presents extracted knowledge in a decent format.