This paper describes the modified stochastic model to represent the segregation patterns of the entire batch jig bed in terms of partition surfaces at various slice positions in a single analysis. While King’s stratification model requires a priori information of the bivariate feed distribution in terms of the particle size and density to explain the partition surfaces at various slice positions, the modified stochastic model does not require this information to explain the partition surfaces at various slice positions. The model uses a six-parameter representation to explain the partition surfaces of the entire segregated bed, while the earlier proposed stochastic model with its four parameters could only explain the partition surface at any single slice position of the segregated jig bed. The model presented here can handle sparse tracer data and the separation key parameters derived from the improved model are in agreement with the earlier findings.

本文描述了改进的随机模型，以在一次分析中根据不同切片位置的分隔表面来表示整个批次夹具床的分离模式。虽然 King 分层模型需要在粒度和密度方面的二元进料分布的先验信息来解释各个切片位置的分区表面，但修改后的随机模型不需要此信息来解释各个切片位置的分区表面。该模型使用六参数表示来解释整个隔离床的隔断面，而早期提出的具有四个参数的随机模型只能解释隔离跳汰床任何单个切片位置的隔断面。