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(PDF) AN ALGORITHM FOR INVESTIGATING THE CLOSED

The efficiency of the ore grinding technological process is mainly conditioned by implementation of separate operations ensuring it. The sequence of carrying out those operations is determined by the technological scheme topology used in that

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AN ALGORITHM FOR INVESTIGATING THE CLOSED CYCLE

That is why, in the present work, an algorithm for investigating the scheme used in the ore grinding technological process ensuring the automated reproduction of the technological scheme topology and a successive calculation of operations included in it is proposed. By analyzing the parameter states of the vector characterizing the properties of the input flow of the operations included in the

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Modern Systems of Automatic Control of Processes of

01/01/2013· Algorithm of management of ore quality and control of ore enrichment process. Table 3. Pre-set functions in automatic control system processes for grinding and classification Fig. 5. Depending on the composition of ore processed: 1 ± massive primary ore, 2 mixed secondary sulphide ore. 3 mixed oxide ores, 4 mixed sericitized ore, 5 Poor

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Methods of Research and Optimization of the Mineral Raw

Study of Operating Modes of Ore-Grinding Mills . Chapter 3. Investigating the Electrical Drive System of the Ore-Grinding Mill. Chapter 4. Methods for investigating the Operation Modes of the Electrical Drive Motor Used in the Mineral Raw Material Grinding Process. Chapter 5. Algorithms for Optimal Control of the Mineral Raw Material Grinding

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Integrated Modeling and Intelligent Control Methods of

Then, a set point optimization control strategy of grinding process based on case-based reasoning (CBR) method is adopted to obtain the optimized velocity set-point of ore feed and pump water feed in the grinding process controlled loops. Finally, a self-tuning PID decoupling controller optimized is used to control the grinding process. Simulation results and industrial application experiments

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A Hybrid Multiobjective Differential Evolution Algorithm

The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential

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A Hybrid Multiobjective Differential Evolution Algorithm

The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential

Get Price

AN ALGORITHM FOR INVESTIGATING THE CLOSED CYCLE

That is why, in the present work, an algorithm for investigating the scheme used in the ore grinding technological process ensuring the automated reproduction of the technological scheme topology and a successive calculation of operations included in it is proposed. By analyzing the parameter states of the vector characterizing the properties of the input flow of the operations included in the

Get Price

A linear-quadratic-Gaussian control algorithm for sulphide

01/05/1987· The paper suggests an algorithm for the multivariable control of an industrial sulphide ore grinding plant. The algorithm is based on the linear-quadratic-Gaussian control theory with a moving time horizon. The dynamic process model with two inputs and two outputs is obtained by fitting simple low order linear time-invariant models to measured input-output pairs. Characteristic to the model

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SYNTHESIS OF THE NOISE IMMUNE ALGORITHM FOR

synthesis of the noise immune algorithm for adaptive control of ore concentration Annotation Context. Consideration of the transient process characteristics under current industrial conditions, i.e. a controlled object’s static and dynamic characteristics when forming controlling impacts in automatic control systems of ore grinding and sizing is one of the advanced ways to increase their

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Multi-Agent Behavioral Control System for the Ore

Multi-Agent Behavioral Control System for the Ore Grinding Process. Article Preview . Abstract: In this paper, a small multi-agent system (MAS) is proposed based on behavioral approach for the complex grinding processes. Causal association agents were established according to the material balance of grinding processes, and prediction agents and stability control agents were built by adding

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An Intelligent Control System for Complex Grinding Processes

The main purpose of the grinding job is to process the ore into smaller particles, so that it is at or near the state of monomer separation, this process is to be mined ore into a final product in multiple processing of the most important steps working the stability of the process, the overflow particle size is checked so directly affect the subsequent sorting operations production targets [4

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Minerals Free Full-Text Experiments on Rare-Earth

Ancient hydrothermal metalliferous sediments (umber) have recently attracted attention as a new rare-earth element resource. We conducted chemical leaching experiments on three different umber ores to optimize the hydrometallurgical extraction process, especially regarding the grinding process. The three umber ore samples, which were collected from Japanese accretionary complexes (Kuminiyama

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(PDF) Processing of Magnetite Iron Ores–Comparing

In this work, the purpose was to study the impact of variations in feed ore properties on the performance of a primary autogenous grinding circuit by ore characterisation and simulation. Samples

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Optimization of the second and third stages of grinding

Developed algorithm of grinding complex control with the use of fuzzy logic. Key words: grinDing, AuTomATion, Fuzzy Logic most works on the grinding process automation pay most attention to management of ball mill grind-ing in the first stage, because the loss in the first stage is the biggest 30-55% of the total iron losses in tails [1, 5-9]. To control the three-staged grinding complex is

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Parameter Identification for Breakage Distribution

ore,the grinding conditions,and the internal energy transfer during grinding process, Wang X L [1] selected the pop ulation balance model to establish the bauxite particle distribution prediction model of ball milling process. As a model ing framework, the populati on model [2, 3] mainly includes the key parameters such as the breakage distribution function, the breakage rate function and

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