Glass melting may be optimized significandy via its intrinsic chemical steps. Raw materials may be chosen with respect to their impact on energy demand, batches may be designed with respect to high conversion rates, and glass compositions may be adjusted with respect to low liquidus. Positive effects have been clearly verified by lab experiments. Yet, the question remains on how such measures translate to the industrial scale. The present contribution outlines an answer. It rests on the complementary analysis of the performance of glass furnaces, typically recorded over periods of 1/2-2 years. The data required are: power input by fuel and boosting; pull rate; melt exit temperature; batch composition; cullet content. This is easily available information for any glass factory on a shift-by-shift basis. What will be shown is that the response to any change of the intrinsic chemical process (glass composition, choice of raw materials, design of the batch) is a highly sensitive discriminator of the performance of a given furnace. Case studies will be presented that demonstrate how the above procedure may be used to predict correctly, on the industrial scale, the effects of a reduced energy demand of melting, an enhanced turnover rate, a lowered temperature of conversion, or a lower liquidus temperature of the glass.
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