A hybrid intelligent optimal control method for complex flotation process

Abstract

In mineral industry, flotation is used to separate utilised ore from gangue. The relationships between the technical indexes, i.e. the concentrate grade and the tailing grade, and the reagent feeding appear to have strong nonlinearities and uncertainties in dynamic behaviours, which can hardly be described using any accurate mathematical model. For non-automatic and non-precision of manual control of reagent feeding, technical indexes fluctuate and are difficult to be controlled within their target ranges. This article has proposed a novel hybrid intelligent optimal control method which consists of a flotation reagent intelligent setting model, a slurry consistency controller, a supply ore mass controller, an air flow rate controller and a flotation level controller, among which the flotation reagent intelligent setting model is the key which consists of a unit reagent pre-setting model-based Rule-based reasoning (RBR), a feedback compensator and a feed forward compensator RBR, and a reagent feeding computational model. The method can automatically adjust the reagent feeding when the workcondition varies, so that the technical indexes can be controlled within their target ranges. The successful application has shown that the proposed method has practical significance and high potential of being further applied in optimal control of mineral industry.

Soft computing-based modeling of flotation processes – A review

“…The hybrid approach -which among other approaches, includes the concept of fuzzy logic modeling of flotation processes within the expert systems -had also been described by Edwards (1990), Poirier andMeech (1993), Poirier (1995), Osorio et al (1999), Chai et al (2009), Rojas et al (2009) and Núñez et al (2010.…”

Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process

“…Therefore, for complex industrial processes, it is an inevitable tendency to develop hybrid intelligent control schemes by integrating information from mechanism analysis, plant data mining, data-driven or semi-empirical modeling, and expert knowledge, etc. Application examples of such methods have been reported in literature [40][41][42][43].…”

Operator-based robust nonlinear control and fault detection for a Peltier actuated thermal process

“…Therefore, nonlinear control design methodologies have been considered. Until now, there exist some well-developed nonlinear control approaches for the nonlinear processes, such as gain scheduling method, feedback linearization method, Lyapunov redesign method, backstepping technique, sliding mode control theory and so on [6][7][8]. However, most of the techniques for nonlinear design assume that measurements of all states are available [8].…”

“…Especially, the robust stability against perturbations can be guaranteed under an inequality condition [11]. Moreover, it seems useful to establish signal-processing-based analytical approach to analyze and design the nonlinear fault detection system [2][3][4]8].…”