Towards CBR for bioprocess planning

Abstract

We present the current status of a recipe planner for bioprocesses. This case-based reasoner adapts previously successful recipes for each batch of the process. In this domain, recipe planning is difficult as actual numerical values of recipe parameters are crucial but quantitative information is scarce. However, adaptation, although far from trivial, is less complicated than planning from scratch. Like other case-based reasoners the system learns from experience, whenever the casebase grows. Therefore we expect that planners will automatically tune themselves to different plants. Case adaptation is fully automatic; process operators were never trained for this task. For adaptation, the case-based reasoner calls upon a semi-qualitative model of the process. The model, casebase and index are integrated and allow for indexing on inferences made by the model. All the software is implemented in an object-oriented framework that can be rapidly instantiated for different processes.