Flotation Equipment technology design includes data calculating and flowsheet drawing. To implement data calculating, relative matrix is used to express the flotation flowsheet, special mineral processing models and optimizing mathematical models have been used. The "binary tree" principle has been used to demonstrate the flotation flowsheet structure, and the coding principle according to the order of preorder traversing to "binary tree" is determined for the implement of the flotation flowsheet drawing. As a result, the computerization of flotation technology design was completed.

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model.

The traditional approach to flotation plant design involves the extensive testing of a single large composite sample or a small number of composite samples that are reputed to represent the ore body. Laboratory tests are conducted to establish a process with potential flowsheets and reagent suites. The process is then confirmed by pilot plant work on a large bulk sample or a few samples. The size of equipment required to achieve a specified throughput, recovery and product grade is then calculated from one of a variety of scale-up models.

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