QUALITATIVE-MODEL SIMPLIFICATION WITH AN APPLICATION TO MULTISTAGE SEPARATION PROCESSES

Deep-model-based qualitative reasoning provides a systematic framework for describing qualitative physical phenomena. However, as physical s ystems become more and more complex, the computational load and comple xity of qualitative simulation increase dramatically. Multistage separ ation processes are among the most important unit operations in chemic al engineering. An industrial-scale multistage separation process is n ormally described by some tens or hundreds of quantitative equations. Qualitative reasoning for this type of process is difficult, if not im possible. Since multistage separation processes ave highly structured (e.g. cascaded stages), the concept of model simplification (or model reduction), often seen in the practice of process control, is applied to qualitative modeling. The objective of this work is to simplify the qualitative process model according to the structural properties of t he multistage separation processes. The simplified qualitative model c an, on the one hand, reduce the computational load in qualitative simu lation and, on the other hand, retain the qualitative description of t he system. In this work, the simplest qualitative model, the signed di rected graph (SDG), is used and the most common multistage separation process, using distillation columns, is studied. Qualitative simulatio n results show that qualitative-model simplification offers an attract ive approach for reasoning about important, practical problems.