Abstract:The objective of process control in the mineral industry is to optimise the recovery of the valuable minerals, while maintaining the quality of the concentrates delivered to the metal extraction plants. The paper presents a survey of the control approaches for ore size reduction and mineral separation processes. The present limitations of the measurement instrumentation are discussed, as well as the methods to upgrade the information delivered by the sensors. In practice, the overall economic optimisation goal… Show more
“…and Φ θ, u, u (1) , u (2) , ..., u (k) , y, y (1) , y (2) , ..., y (k) = 0 (41) hold, on [0, T ], for all (θ, u, u (1) , u (2) , ..., u (k) , y, y (1) , y (2) , ..., y (k) ), where (θ, x 0 , u) belong to an open and dense subset of…”
Section: Identifiability Of a Nonlinear Systemmentioning
confidence: 99%
“…The derivatives of the input function u are u (1) , u (2) , ..., u (k) and the derivatives of the output function y are y (1) , y (2) , ..., y (k) where y = (t, θ, x 0 , u).…”
Section: Identifiability Of a Nonlinear Systemmentioning
confidence: 99%
“…The ability of downstream processes to extract the greatest benefit from the milled ore is dependent on the particle size of the product that leaves the mill. Therefore, the optimal level of operation is achieved when the mill produces a product of consistent fineness at the maximum possible rate and efficiency [1], [2]. Because grinding mills are considered to be one of the most cost-intensive and energy-intensive operations in the mineral processing industry, a better product from the mill could result in improved economic performance [3], [4].…”
A nonlinear phenomenological model of a grinding mill is used to determine five run-of-mine (ROM) grinding milling circuit parameters. The five parameters are fraction of fines and fraction of rocks in the ore fed to the mill, rock and steel abrasion factor and power needed for a ton of fines produced. The model is analyzed according to an identifiability procedure to identify the system data needed to calculate the parameter values. It is shown that a minimum of 18 measurements for a single-stage closed circuit and a minimum of 16 measurements for a singlestage open circuit are needed to determine the five milling circuit parameters. For the closed circuit, the time signals of 8 quantities in the mill will be sufficient to determine all five parameters since the other 10 measurements are time derivatives of these signals. The measurements require the holdup quantities of states within the mill. Since these measurements are not readily available in real-time at plants, it may be difficult to estimate the parameters on-line.
“…The purpose of the operational control of flotation is to ensure that the concentrate grade and the tailing grade are inside their target ranges within which the control objectives are to maximise the concentrate grade and minimise the tailing grade so as to improve the concentrate grade and the metal recovery. As a result, recent research has been focused on how to increase the concentrate grade and decrease the tailing grade by applied new flotation control methods in Hodouin, Jamsa-Jounela, Carvalho and Bergh (2001) and Bergh and Yianatos (2003).…”
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.
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