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Advanced Digital, Modeling and Control Applies into Various Processes

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A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 40699

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Prof. Dr. Rudolf Kawalla

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Institute of Metal Forming (IMF), Technische Universitat Bergakademie Freiberg, Freiberg, Germany
Interests: rolling strategies; material processing; material characterization; metallurgical engineering; nonoriented electrical steels
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Dr. Beloglazov Ilya

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The Automation of Technological Processes and Production Department, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia
Interests: fluidized bed; CFD; DEM; automation control system
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Special Issue Information

Dear colleagues,

Advanced digital technology refers to a process control strategy and represents studies of various technological and physical systems that are simultaneously affected by various disturbances. Digital transformation brings together leading research addressing the global challenges of transitioning to a resource-efficient, process-safe and sustainable future. By analyzing the symmetry of the flows of liquids and gases, the distribution of bulk material, mechanical damage, temperature drops and electromagnetic radiation, it is possible to study the operation of technological processes and control systems. If the task is limited to only one discipline or several disciplines in control and design, then there is a high probability that the forecast of the system's behavior will be insufficiently accurate or completely incorrect. Interdisciplinary analysis solutions can help engineers investigate the effects of symmetric or asymmetric actions individually or collectively, figuring out the most detailed solution when it is needed.

In this special issue on symmetry, we mainly discuss the application of symmetry to process modeling and control systems. For example, when modeling a process by obtaining a static or dynamic characteristics of an object using various methods of numerical modeling or using artificial intelligence or neural networks. These process modeling techniques can also be effectively applied to control system design, Big Data collection and synthesis, data processing, and problem identification. For this reason, it is necessary to take into account a large number of parameters and knowledge of the dynamics of transient processes, which will contribute to the rapid development of advanced control systems.

Digitalization;
Process modeling;
Advanced process control;
Digital twin;
Computer-aided design;
Visualization;
Computational fluid dynamics;
Discrete element method;
Green technology;
Carbon footprint

Prof. Dr. Rudolf Kawalla
Dr. Beloglazov Ilya
Guest Editors

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Published Papers (13 papers)

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Editorial

Jump to: Research

4 pages, 125 KiB

Open AccessEditorial

Review of Advanced Digital Technologies, Modeling and Control Applied in Various Processes

by Ilia Beloglazov

Symmetry 2024, 16(5), 536; https://doi.org/10.3390/sym16050536 - 30 Apr 2024

Cited by 1 | Viewed by 799

Abstract

This special issue reviews advanced digital technologies in modeling and control of technological processes [...] Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

Research

Jump to: Editorial

12 pages, 3971 KiB

Open AccessArticle

A Study of the Critical Velocity of the Droplet Transition from the Cassie to Wenzel State on the Symmetric Pillared Surface

by Zhulong Wu, Yingqi Li, Shaohan Cui, Xiao Li, Zhihong Zhou and Xiaobao Tian

Symmetry 2022, 14(9), 1891; https://doi.org/10.3390/sym14091891 - 9 Sep 2022

Cited by 4 | Viewed by 2038

Abstract

A droplet hitting a superhydrophobic surface will undergo the Cassie to Wenzel transition when the wetting force exceeds the anti-wetting force. The critical velocity of the droplet’s Cassie to Wenzel state transition can reflect the wettability of the surface. However, the critical velocity research is still at the microscale and has not been extended to the nanoscale mechanism. A cross-scale critical velocity prediction model for superhydrophobic surfaces with symmetric structures is proposed here based on a mechanical equilibrium system. The model’s applicability is verified by experimental data. It demonstrates that the mechanical equilibrium system of droplet impact with capillary pressure and Laplace pressure as anti-wetting forces is more comprehensive, and the model proposed in this study predicts the critical velocity more precisely with a maximum error of 12% compared to the simulation results. Furthermore, the correlation between the simulation at the nanoscale and the evaluation of the macroscopic symmetrical protrusion surface properties is established. Combined with the model and the correlation, the relationship between the microscopic mechanism and the macroscopic examination of droplet dynamics on the superhydrophobic surface be presented, and the wettability evaluation method of macroscopic surfaces based on the molecular simulation mechanism can be realized. Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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13 pages, 13134 KiB

Open AccessArticle

Monitoring of the Behaviour and State of Nanoscale Particles in a Gas Cleaning System of an Ore-Thermal Furnace

by Vladimir Bazhin and Olga Masko

Symmetry 2022, 14(5), 923; https://doi.org/10.3390/sym14050923 - 1 May 2022

Cited by 13 | Viewed by 1802

Abstract

The aim of this paper is to define and select stable zones in the off-gas duct of an ore-thermal furnace using a mathematical model. This is needed to increase the effectiveness of exhaust gas composition control in metallurgical silicon production. Methods. The goals of this study were achieved by means of computational fluid dynamics. A model with a water-cooled furnace roof as well as a model comprising steel gas passes with a sliding shutter was developed using ANSYS Fluent software. Both models were symmetrical to ensure a uniform gas-dust distribution, which allowed us to test the adequacy of the obtained models. The models were based on the Navier–Stokes equations system as well as on a discrete phase model (DPM) that was developed using the Euler–Lagrange method. Results. As a result of the modelling, a transition flow mode (Re 0-7437) was revealed behind the sliding shutter. As such, it can be assumed that the most suitable place for measuring equipment to be installed is directly behind the closed part of the sliding shutter. Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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37 pages, 17832 KiB

Open AccessArticle

Self-Optimizing Path Tracking Controller for Intelligent Vehicles Based on Reinforcement Learning

by Jichang Ma, Hui Xie, Kang Song and Hao Liu

Symmetry 2022, 14(1), 31; https://doi.org/10.3390/sym14010031 - 27 Dec 2021

Cited by 9 | Viewed by 4111

Abstract

The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, human drivers. While many methods provide state-of-the-art tracking performance, they tend to emphasize constant PID control parameters, calibrated by human experience, to improve tracking accuracy. A detailed analysis shows that PID controllers inefficiently reduce the lateral error under various conditions, such as complex trajectories and variable speed. In addition, intelligent driving vehicles are highly non-linear objects, and high-fidelity models are unavailable in most autonomous systems. As for the model-based controller (MPC or LQR), the complex modeling process may increase the computational burden. With that in mind, a self-optimizing, path tracking controller structure, based on reinforcement learning, is proposed. For the lateral control of the vehicle, a steering method based on the fusion of the reinforcement learning and traditional PID controllers is designed to adapt to various tracking scenarios. According to the pre-defined path geometry and the real-time status of the vehicle, the interactive learning mechanism, based on an RL framework (actor–critic—a symmetric network structure), can realize the online optimization of PID control parameters in order to better deal with the tracking error under complex trajectories and dynamic changes of vehicle model parameters. The adaptive performance of velocity changes was also considered in the tracking process. The proposed controlling approach was tested in different path tracking scenarios, both the driving simulator platforms and on-site vehicle experiments have verified the effects of our proposed self-optimizing controller. The results show that the approach can adaptively change the weights of PID to maintain a tracking error (simulation: within ±0.071 m; realistic vehicle: within ±0.272 m) and steering wheel vibration standard deviations (simulation: within ±0.04°; realistic vehicle: within ±80.69°); additionally, it can adapt to high-speed simulation scenarios (the maximum speed is above 100 km/h and the average speed through curves is 63–76 km/h). Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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14 pages, 1234 KiB

Open AccessArticle

Key Validity Using the Multiple-Parameter Fractional Fourier Transform for Image Encryption

by Tieyu Zhao and Yingying Chi

Symmetry 2021, 13(10), 1803; https://doi.org/10.3390/sym13101803 - 28 Sep 2021

Cited by 2 | Viewed by 1397

Abstract

As a symmetric encryption algorithm, multiple-parameter fractional Fourier transform (MPFRFT) is proposed and applied to image encryption. The MPFRFT with two vector parameters has better security, which becomes the main technical means to protect information security. However, our study found that many keys of the MPFRFT are invalid, which greatly reduces its security. In this paper, we propose a new reformulation of MPFRFT and analyze it using eigen-decomposition-type fractional Fourier transform (FRFT) and weighted-type FRFT as basis functions, respectively. The results show that the effective keys are extremely limited. Furthermore, we analyze the extended encryption methods based on MPFRFT, which also have the security risk of key invalidation. Theoretical analysis and numerical simulation verify our point of view. Our discovery has important reference value for a class of generalized FRFT image encryption methods. Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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19 pages, 2164 KiB

Open AccessArticle

Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

by Shamil Islamov, Alexey Grigoriev, Ilia Beloglazov, Sergey Savchenkov and Ove Tobias Gudmestad

Symmetry 2021, 13(7), 1293; https://doi.org/10.3390/sym13071293 - 18 Jul 2021

Cited by 38 | Viewed by 5422

Abstract

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models. Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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14 pages, 1699 KiB

Open AccessArticle

Estimation of Electricity Generation by an Electro-Technical Complex with Photoelectric Panels Using Statistical Methods

by Anna Turysheva, Irina Voytyuk and Daniel Guerra

Symmetry 2021, 13(7), 1278; https://doi.org/10.3390/sym13071278 - 16 Jul 2021

Cited by 10 | Viewed by 2123

Abstract

This paper presents a computational tool for estimating energy generated by low-power photovoltaic systems based on the specific conditions of the study region since the characteristic energy equation can be obtained considering the main climatological factors affecting these systems in terms of the symmetry or skewness of the random distribution of the generated energy. Furthermore, this paper is aimed at determining any correlation that exists between meteorological variables with respect to the energy generated by 5-kW solar systems in the specific climatic conditions of the Republic of Cuba. The paper also presents the results of the influence of each climate factor on the distribution symmetry of the generated energy of the solar system. Studying symmetry in statistical models is important because they allow us to establish the degree of symmetry (or skewness), which is the probability distribution of a random variable, without having to make a graphical representation of it. Statistical skewness reports the degree to which observations are distributed evenly and proportionally above and below the center (highest) point of the distribution. In the case when the mentioned distribution is balanced, it is called symmetric. Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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17 pages, 1614 KiB

Open AccessArticle

Virtual Soft Sensor of the Feedstock Composition of the Catalytic Reforming Unit

by Natalia Koteleva and Ilya Tkachev

Symmetry 2021, 13(7), 1233; https://doi.org/10.3390/sym13071233 - 9 Jul 2021

Cited by 1 | Viewed by 2301

Abstract

The paper discusses a method for obtaining a matrix of individual and group composition of a hydrotreated heavy gasoline fraction in industrial conditions based on the fractional composition obtained by the distillation method according to the ASTM D86 (the Russian analogue of such a standard is GOST 2177). A method for bounds estimation of the retention index (RI) change is considered on the basis of the symmetry of the RI change range relative to its arithmetic mean. Implementation of this method is performed by simulation of individual composition of C6–C12 feedstock of the catalytic reforming unit in the software package. For this purpose, the boiling curve of individual composition of hydrocarbon mixture is converted into the corresponding curve of fractional composition. The presented technique of creating a virtual soft sensor makes it possible to establish a correct relationship between the fractional composition and the individual hydrocarbon composition obtained according to the IFP 9301 (GOST R 52714) (Russian GOST R 52714 and international IFP 9301 standards for the determination of individual and group composition of hydrocarbon mixtures by capillary gas chromatography). The virtual soft sensor is based on chemical and mathematical principles. The application of this technique on the data of a real oil refinery is shown. Obtaining accurate data by means of a virtual soft sensor on the individual composition of feedstock will make it possible to optimize the catalytic reforming process and thus indirectly improve its environmental friendliness and enrichment efficiency. Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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15 pages, 6688 KiB

Open AccessFeature PaperArticle

Optical Inspection Systems for Axisymmetric Parts with Spatial 2D Resolution

by Aleksandr Kulchitskiy

Symmetry 2021, 13(7), 1218; https://doi.org/10.3390/sym13071218 - 7 Jul 2021

Cited by 8 | Viewed by 1957

Abstract

The article proposes a solution to the problem of increasing the accuracy of determining the main shaping dimensions of axisymmetric parts through a control system that implements the optical method of spatial resolution. The influence of the projection error of a passive optical system for controlling the geometric parameters of bodies of revolution from the image of its sections, obtained by a digital camera with non-telecentric optics, on the measurement accuracy is shown. Analytical dependencies are derived that describe the features of the transmission of measuring information of a system with non-telecentric optics in order to estimate the projection error. On the basis of the obtained dependences, a method for compensating the projection error of the systems for controlling the geometry of the main shaping surfaces of bodies of revolution has been developed, which makes it possible to increase the accuracy of determining dimensions when using digital cameras with a resolution of 5 megapixels or more, equipped with short-focus lenses. The possibility of implementing the proposed technique is confirmed by the results of experimental studies. Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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10 pages, 2162 KiB

Open AccessArticle

Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning

by Aleksei Boikov, Vladimir Payor, Roman Savelev and Alexandr Kolesnikov

Symmetry 2021, 13(7), 1176; https://doi.org/10.3390/sym13071176 - 29 Jun 2021

Cited by 103 | Viewed by 8050

Abstract

The paper presents a methodology for training neural networks for vision tasks on synthesized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81. Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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13 pages, 4302 KiB

Open AccessArticle

Universal Approach for DEM Parameters Calibration of Bulk Materials

by Aleksei Boikov, Roman Savelev, Vladimir Payor and Alexander Potapov

Symmetry 2021, 13(6), 1088; https://doi.org/10.3390/sym13061088 - 18 Jun 2021

Cited by 11 | Viewed by 2828

Abstract

DEM parameters calibration is the most important step in preparing a DEM model. At the same time, the lack of a universal approach to DEM parameters calibration complicates this process. The paper presents the author’s approach to creating a universal calibration approach based on the physical meaning of the friction coefficients and conducting symmetrical experiments at full scale and in a simulation, as well as the implementation of the approach in the form of a physical test rig. Several experiments were carried out to determine the DEM parameters of six material–boundary pairs. The resulting parameters were adjusted using a refinement experiment. The results confirmed the adequacy of the developed approach, as well as its applicability in various conditions. The limitations of both the approach itself and its specific implementation in the form of a test rig were identified. Full article

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11 pages, 1937 KiB

Open AccessArticle

Big Data as a Tool for Building a Predictive Model of Mill Roll Wear

by Natalia Vasilyeva, Elmira Fedorova and Alexandr Kolesnikov

Symmetry 2021, 13(5), 859; https://doi.org/10.3390/sym13050859 - 12 May 2021

Cited by 59 | Viewed by 3543

Abstract

Big data analysis is becoming a daily task for companies all over the world as well as for Russian companies. With advances in technology and reduced storage costs, companies today can collect and store large amounts of heterogeneous data. The important step of extracting knowledge and value from such data is a challenge that will ultimately be faced by all companies seeking to maintain their competitiveness and place in the market. An approach to the study of metallurgical processes using the analysis of a large array of operational control data is considered. Using the example of steel rolling production, the development of a predictive model based on processing a large array of operational control data is considered. The aim of the work is to develop a predictive model of rolling mill roll wear based on a large array of operational control data containing information about the time of filling and unloading of rolls, rolled assortment, roll material, and time during which the roll is in operation. Preliminary preparation of data for modeling was carried out, which includes the removal of outliers, uncharacteristic and random measurement results (misses), as well as data gaps. Correlation analysis of the data showed that the dimensions and grades of rolled steel sheets, as well as the material from which the rolls are made, have the greatest influence on the wear of rolling mill rolls. Based on the processing of a large array of operational control data, various predictive models of the technological process were designed. The adequacy of the models was assessed by the value of the mean square error (MSE), the coefficient of determination (R²), and the value of the Pearson correlation coefficient (R) between the calculated and experimental values of the mill roll wear. In addition, the adequacy of the models was assessed by the symmetry of the values predicted by the model relative to the straight line Ypredicted = Yactual. Linear models constructed using the least squares method and cross-validation turned out to be inadequate (the coefficient of determination R² does not exceed 0.3) to the research object. The following regressions were built on the basis of the same operational control database: Linear Regression multivariate, Lasso multivariate, Ridge multivariate, and ElasticNet multivariate. However, these models also turned out to be inadequate to the object of the research. Testing these models for symmetry showed that, in all cases, there is an underestimation of the predicted values. Models using algorithm composition have also been built. The methods of random forest and gradient boosting are considered. Both methods were found to be adequate for the object of the research (for the random forest model, the coefficient of determination is R² = 0.798; for the gradient boosting model, the coefficient of determination is R² = 0.847). However, the gradient boosting algorithm is recognized as preferable thanks to its high accuracy compared with the random forest algorithm. Control data for symmetry in reference to the straight line Ypredicted = Yactual showed that, in the case of developing the random forest model, there is a tendency to underestimate the predicted values (the calculated values are located below the straight line). In the case of developing a gradient boosting model, the predicted values are located symmetrically regarding the straight line Ypredicted = Yactual. Therefore, the gradient boosting model is preferred. The predictive model of mill roll wear will allow rational use of rolls in terms of minimizing overall roll wear. Thus, the proposed model will make it possible to redistribute the existing work rolls between the stands in order to reduce the total wear of the rolls. Full article

(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)

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19 pages, 3200 KiB

Open AccessArticle

Novel Approach to Collect and Process Power Quality Data in Medium-Voltage Distribution Grids

by Sergei Kryltcov, Aleksei Makhovikov and Mariia Korobitcyna

Symmetry 2021, 13(3), 460; https://doi.org/10.3390/sym13030460 - 12 Mar 2021

Cited by 19 | Viewed by 2495

Abstract

The paper is devoted to the development of the structure of a fast and flexible data collecting system based on the proposed approach to measure power quality indicators in three-phase medium-voltage distribution grids with an example of a Mikhailovsky mining and processing plant. The approach utilizes the properties of a space vector, obtained from grid currents and voltages with disturbed waveform, to allow faster extraction of the harmonic components compared to traditional approaches, based on the direct Fourier-transform applied to a line or phase values. During the study, the concept of a universal measurement device was introduced, which allows fast estimation of the following values at the grid node: magnitudes and phases of voltage and current harmonic components, active and reactive power of harmonics and fundamental components, positive and negative instantaneous powers. The structure of interconnected measurement and control units for the considered grid node with simultaneous operation of two active variable frequency drives with active rectifiers was proposed in accordance with a concept of the Internet of things. The benefits of the proposed solution are shown by the example of the model of the grid node with two operating draglines and nonlinear load, which was developed in MATLAB/Simulink software. The proposed approach was utilized to produce distributed references for control systems of grid inverters to compensate nonlinear currents, which allowed to significantly improve THDi of the grid node input power. Full article

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