SYNTHESIZING OPTIMAL WASTE BLENDS

Vitrification of tank wastes to form glass is a technique that will be used for the disposal of high-level waste at Hanford. The amount of g lass produced can be reduced by blending of the wastes. The optimal wa y to combine the tanks to minimize the volume of glass can be determin ed from a discrete blend calculation. However, this problem results in a combinatorial explosion as the number of tanks increases. Moreover, the property constraints make this problem highly non-convex where ma ny algorithms get trapped in local minima. In this paper we examine th e use of different combinatorial optimization approaches to solve this problem. A two stage approach using a combination of Simulated Anneal ing and nonlinear programming (NLP) is developed. The results of diffe rent methods such as heuristics approach based on human knowledge and judgment, mixed integer nonlinear programming (MINLP) approach with GA MS, and branch and bound with lower bound derived from the structure o f the given blending problem are compared with this coupled Simulated Annealing and NLP approach.