Reduced spurious solutions by using intersection algorithm in qualitative simulation

Abstract

Abstract A method is presented to incorporate the intersection algorithm into the elimination of spurious solution and shrinkage of ambiguous variables in qualitative simulations. Since, for a given model structure, the qualitative simulation is sound but incomplete, the spurious solutions are inevitably included in the solution set. To be appropriately applied to these solutions, it is important to reduce the spurious solutions. Since the true solution is always existing in the solution, by incorporating them into the intersection of solutions, one can quickly reduce the spurious solutions. A heat exchanger example is used to illustrate the effectiveness of this method. Through this complicated example, the simulation results show that spurious solutions can be reduced from 26 to 4, and the number of ambiguous variables is reduced from 9 to 2. Results also show that the proposed method is effective in reducing spurious solutions in qualitative simulation.