SYNTHESIS OF FLOTATION BASED SEPARATION SYSTEMS
In this work a methodology is presented for defining the optimal configuration for flotation circuit and selection of appropriate equipment for the process. The problem is represented by three hierarchized superstructures. The first level represents separation tasks, which include: feed processing, concentrate processing, and tail processing. The second level represents the stream network needed to carry out each tasks. Where for each task a superstructure is developed which includes several operational stages. At the third level, several equipment alternatives are considered for each stage. The equipment discussed in the model includes flotation column and flotation cell banks, with and without grinding. The optimal selection of the circuit is made with an appropriate objective function, upon which the values of the operational and structural variables may be determined. The problem is formulated using disjunctive programming, which is converted to a MILP problem. The model includes mass balance, equipment models, operational conditions, and logic relationship. The paper presents an example to illustrate its application for effective design of a copper concentration plant.
A MILP model for the design of mineral flotation circuits
Abstract This paper develops a procedure for the design or improvement of mineral flotation circuits, based on a mathematical programming model with disjunctive equations. The model developed corresponds to a mixed-integer linear programming (MILP), avoiding nonlinear expressions. The procedure is characterized by: (1) The development of two hierarchized superstructures, such that the first level represents processing systems, which must satisfy certain tasks. The second level represents circuits of equipment needed to carry out the tasks of each system. (2) The superstructures are modeled mathematically including the selection of equipment, mass balance, and operational conditions. (3) The objective function is the maximization of profits. With the help of examples that are included to demonstrate the advantages of the procedure, it is shown that the method can be useful in deciding the configuration of the flotation circuits and the operational conditions.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.