Development of neural network model of the multiparametric technological object

At present, there are a large number of methods for identifying the technological objects on the basis of data of their industrial operation [1-3]. The most promising direction is the construction of a model, which will allow to take into account the multifactorial nature of the object, and the nonlinearity of interrelation between variables. This will make it possible to control the object, taking into account the change in its states, and based on the current data,to predict the change in the output value with different input characteristics[4-6]. All this will provide the opportunity to create an operating system, based on the currently measured technological indicators. In order to implement this approach, a comparative study of the regression analysis models, using polynomials of various types and neural network algorithms, for the synthesis of a complex technological unit model, was carried out in the work. In the regression analysis, the following models were investigated: polynomials, linear, fractional and exponential functions, Kolmogorov-Gabor polynomial. In the process of the research of neural networks to solve this problem, their structure was varied, with subsequent learning according to the Levenberg-Marcardt algorithm. In the process of simulation of the object models in the Matlab package, the degree of similarity of the outputs for each of the obtained models and the actual output of the object were estimated. Quadratic criterion and the coefficient of correlation were calculated, that made it possible to judge the accuracy of the constructed models. The best structure of the model was established for identifying a complex multiparameter object, using the example of statistics for the operation of a ball mill.It was a network with three hidden layers and 50, 35 and 25 neurons in them, with activation functions, respectively by layers - hyperbolic tangent, sigmoid function in 2 layers, and a linear activation function in the output layer. The vector, including 15 parameters, was supplied to the network input: the volume of ore supply to the mill, the volume of water supply to the mill and the mill’strommel, the signals with the first-, the second-, and the thirdorder lags, and the signal of current with the first-, the second-, and the third-order lags. This approach to identification has increased the accuracy of the object model, that ultimately will affect the quality of the developed control system of the unit as a whole, allowing to improve the quality of the ball millcontrol

Monitoring of grinding condition in drum mills based on resulting shaft torque

Grinding is the most energy-intensive process among all stages of raw material preparation and determines the course of subsequent ore beneficiation stages. Level of electricity consumption is determined in accordance with load characteristics forming as a result of ore destruction in the mill. Mill drum speed is one of process variables due to which it is possible to control ore destruction mechanisms when choosing speed operation mode of adjustable electric mill drive. This study on increasing energy efficiency due to using mill electric drive is based on integrated modelling of process equipment – grinding process and electromechanic equipment – electric drive of grinding process. Evaluating load torque by means of its decomposition into a spectrum, mill condition is identified by changing signs of frequency components of torque spectrum; and when studying electromagnetic torque of electric drive, grinding process is monitored. Evaluation and selection of efficient operation mode of electric drive is based on the obtained spectrum of electromagnetic torque. Research results showed that with increasing mill drum speed – increasing impact energy, load torque values are comparable for the assigned simulation parameters. From the spectra obtained, it is possible to identify mill load condition – speed and fill level. This approach allows evaluating the impact of changes in process variables of grinding process on parameters of electromechanical system. Changing speed operation mode will increase grinding productivity by reducing the time of ore grinding and will not lead to growth of energy consumption. Integration of digital models of the technological process and automated electric drive system allows forming the basis for developing integrated methods of monitoring and evaluation of energy efficiency of the entire technological chain of ore beneficiation

Dynamic Modeling and Simulation of SAG Mill Circuits with Pebble Crushing

Grinding is one of the most energy-consuming processes in the mining industry. As a critical part of the comminution process, autogenous grinding (AG) or semi-autogenous grinding (SAG) mills are often used for primary grinding. However, the breakage mechanism of an AG/SAG mill is inefficient in grinding particles of a certain size, typically in the range of 25-55 mm, i.e., pebbles. Therefore, cone crushers are often used as pebble crushers and integrated into AG/SAG mill circuits to break the critical size particles that accumulate in the mill and to increase the performance of the primary grinding circuits.Many studies have been carried out, mainly focusing on optimizing of SAG mills and cone crushers, respectively, but only a few have investigated the dynamic interactions between a SAG mill and its pebble crushers. The scope of this thesis is to examine the dynamic relations between the SAG mill and the pebble crusher in a closed circuit and thus to optimize the circuit efficiency by controlling the pebble crusher operational settings.In this thesis, two modeling techniques are proposed for simulating the dynamics in the grinding process. The first method is the fundamental modeling method, where the underlying physics of the comminution process has been considered. The proposed mill model is divided into sub-processes that include breakage behavior in each sub-division, particle transportation within the mill chamber, and the discharge rate from the mill. The dynamic cone crusher model describes the crusher chamber as a surge bin and predicts the product particle sizes based on crusher CSS and eccentric speed. In the simulation model, other production units such as screens and conveyors are included to describe the dynamics of the circuit better. The flexibility of this method allows one to apply this simulation library to a variety of plants with different configurations.The second modeling technique presented in this study is based on data-driven methods, where two SAG mill power models are developed. The first model calculates the mill power draw by combining several individual data-driven algorithms. The second model uses historical data to forecast the mill power draw in advance. These data-driven methods can make high accuracy predictions based on a specific plant dataset, and find complex nonlinear relations between input variables and target outputs.The results from both simulations and industrial data analysis show that significant dynamic impact can be induced by altering the pebble crusher operational settings. Therefore, the performance (throughput or specific energy) of an AG/SAG closed circuit can be improved with the optimized utilization of its recycle pebble crusher. While the present work is based on simulation and analysis of plant data, full-scale tests and further model development are needed as part of a future study