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|A prototype weightometer soft sensor is under development at DebTech, De Beers in partnership with Venetia Mine. Its main function consists of an online data source; data validation; dynamic weightometer models; online detection of weightometer failure; alarming and replacing a weightometer when it fails. Thirty-one dynamic models have been developed. All models with R2=85% are accepted as good models and they generally have satisfactory results with a daily error of less than 8% within a 90% confidence level. By using those models, a set of soft sensors can be set up to cover all weightometers in the entire processes starting from primary crushing to the stock piles of dense medium separation (DMS). Some practical issues and concerns on model development are discussed, including data collection, data analysis, data pre-processing, variable selection, time delay, model training and model validation. When deploying the weightometer soft sensor system, the potential benefits could include reduction in plant downtime; reduction in unplanned maintenance and even replacement of non-critical weightometers, particularly where nuclear weightometersare used. Keywords: soft sensor, weightometer, dynamic modelling, neural network modelling|