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

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

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

OCM 2021 - Optical Characterization of Materials

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

OCM 2021 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

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

Mining Technologies Innovative Development

The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Modelling tumbling ball milling based on DEM simulation and machine learning

Tumbling ball milling is a critical comminution process in materials and mineral processing industries. It is an energy intensive process with low energy efficiency. It is important that ball mills and the milling process are properly designed and operated. To achieve this, models at different scales are needed to provide accurate prediction of mill performance under various conditions. This study aimed to develop a combined discrete element method (DEM) and machine learning (ML) modelling framework to link mill design, operation parameters with particle flow and mill efficiency. A scale-up model was developed based on DEM simulations to link mill size ratio, rotation rate, and filling level with power draw and grinding rate. Then, an ML model using the Support Vector Machine (SVM) algorithm was developed to predict the angle of repose (AoR) and collision energy based on various operation conditions. The ML model was trained by the data generated from the DEM simulations and able to predict the AoR and collision energy. In the process monitoring, an artificial neural network (ANN) was firstly proposed to predict internal particle flow properties of a rotating mill based on acoustic emission (AE) signal generated using the DEM. Main features of AE signals and power draw were fed into the ANN to predict flow properties such as particle size distributions, collision energy distribution and filling levels. Further, a convolution neural network (CNN) was used to replace the ANN to extract more efficient features of AE signals non-linearly based on different local frequency ranges in a ball milling process partially filled with steel balls and grinding particles. Last, a physics-informed ML model was developed based on continuous convolution neural network (CCNN) to learn particle contact mechanisms provided by DEM data at different rotation speeds. The ML model coupled with DEM simulation can accelerate DEM simulation to accurately predict particle flow in a long time series. In summary, this work has demonstrated that combining physics-based numerical models DEM to ML models not only improves the efficiency and accuracy of predictions of complicated processes but also provides more insight to the process and makes predictions more transparent