Natural Language Modelling in Process Synthesis and Optimization

The paper addresses the development of synthesis and optimization models by adopting a natural language approach that assumes less context knowledge and is more amenable to the formulation of multi-disciplinary applications. The approach deploys ontologies and enables the integration of data from different resources, the re-use of generic mathematical formulations, and the deployment of object-oriented models. The approach is illustrated for screening of biochemical paths, a task that involves domains in engineering, agricultural sciences, biochemistry and economics. Concerning bioenergy and biomass, there is a large list of available knowledge, data and models from heterogeneous sources. The paper explains the use of ontologies to integrate and automate decisions, also to automate the synthesis of superstructrures. Illustrations include 82 different chemistries that lead to the production of more than 80 intermediate and final products based on biomass. The use of ontologies explains means to systematize the development of the superstructure, to customize features, also to invoke and optimize for the preferred paths and technologies. The work is extended to include scheduling and planning problems.