An efficient, statistically based methodology for development and optimization of multicomponent materials is presented. The approach is illustrated with a five‐component nuclear waste glass. A composition field is defined, test compositions are statistically chosen, and their measured property data are used to fit empirical models. These models are then used to predict the optimum composition. The following nuclear waste glass components were investigated: SiO 2 , B 2 O 3 , Na 2 O, CaO, and simulated nuclear waste. The following properties were modeled: viscosity, chemical durability, and crystallinity. Successful models were constructed by using data from 27 test melts. This methodology could be applied to a wide range of ceramic mixture problems.
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Chick et al. (1984) studied this question.
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