The proliferation of modelling tools has enabled an intensive application of simulation and optimization technology. Process synthesis and design applications employ tools to address the systematic development of process operations and often rely on generic representations (such as superstructures) that are used to integrate the available options in exhaustive layouts. Although design solutions can be approached in a single stage, practice dictates the employment of multiple stages - first with the deployment of simple conceptual models and then with the use of more detailed formulations – as intermediate solutions can be reviewed, analyzed and understood.

Even in cases where the development of solutions is rigorous and proven, synthesis and design experiments can prove rather inconclusive and difficult to translate. Process synthesis typically yields a multitude of solutions with similar design performance but different structural and operational characteristics. In a similar vane, minor changes in the design parameters often yield major changes in the selected designs (types of units, connections employed) which are difficult to interpret, comprehend or summarize. The systematic interpretation of the solution can yield not only to a better understanding of the solution space but also to a systematic reduction of the superstructure employed (both in size and complexity) and, thus, to much simpler synthesis experiments.

The paper presents a systematic approach for the extraction, interpretation and exploitation of chemical process knowledge. The work combines optimization, semantic models (in the form of ontologies) and analytical tools. The research is presented against a background of stochastic optimisation techniques that enable the development of solution clusters for chemical reactors synthesis. The work explains the systematic extraction of information with a view to simplify and interpret design results. The simplification is achieved with a gradual evolution of the superstructure and corresponding adjustments of the optimization search. The interpretation is accomplished with the use of analytical tools to translate data into descriptive terms understood by users. Both stages make use of ontology models bridged with analytical tools to extract information patterns from solution data. Data mining and clustering techniques are employed in order to identify design trends (units, connectivities) and to build the components required for the knowledge models. Ontologies feature concepts and relationships processed by external models (agents). They can be interfaced with synthesis workers and can be used to enable solution summaries or the visualization of the results. The approach is illustrated with several examples from the literature.