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Advanced Digital, Modeling and Control Applies into Various Processes II

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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 (30 November 2023) | Viewed by 17620

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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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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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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

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

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (9 papers)

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Research

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17 pages, 3859 KiB

Open AccessArticle

Modeling of Particle Size Distribution in the Presence of Flocculant

by Elmira Fedorova, Elena Pupysheva and Vladimir Morgunov

Symmetry 2024, 16(1), 114; https://doi.org/10.3390/sym16010114 - 18 Jan 2024

Viewed by 776

Abstract

This study presents a mathematical description of the solid fraction aggregation process in the presence of a flocculant and its result. The basis is a population balance equation. The model is realized in Python language. Verification was carried out using red mud from the investigated enterprise; Flomin AL P 99 VHM was used as a flocculant. The mean square deviation for the parameter “mean aggregate diameter” is equal to 19.88 μm. The time required for the model calculation is about 3 min. The time spent on modeling depends on the number of calculation channels. In this study, 40 channels (20 with PSD source data, and 20 with empty values required for the calculation) were used for the calculation. The time spent on the model calculation is much shorter than the inertia via each of the communication channels for the studied symmetric radial type thickener. A user interface is developed, where the input parameters are the initial pulp particle size distribution, viscosity and density of pulp in the thickener, particle surface area, concentration and flow rate of flocculant, concentration of solid particles, inner diameter and height of the feed well, and simulation time. The result of the simulation is particle size distribution in the feed well of the washer and the mean flocculus diameter. Full article

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

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15 pages, 3499 KiB

Open AccessArticle

Controlling of Unmanned Underwater Vehicles Using the Dynamic Planning of Symmetric Trajectory Based on Machine Learning for Marine Resources Exploration

by Yuriy Kozhubaev, Victor Belyaev, Yuriy Murashov and Oleg Prokofev

Symmetry 2023, 15(9), 1783; https://doi.org/10.3390/sym15091783 - 18 Sep 2023

Cited by 1 | Viewed by 787

Abstract

Unmanned underwater vehicles (UUV) are widely used tools in ocean development, which can be applied in areas such as marine scientific research, ocean resources exploration, and ocean security. However, as ocean exploration advances, UUVs face increasingly challenging operational environments with weaker communication signals. Consequently, autonomous obstacle avoidance planning for UUVs becomes increasingly important. With the deepening of ocean exploration, the operational environment of UUVs has become increasingly difficult to access, and the communication signals in the environment have become weaker. Therefore, autonomous obstacle avoidance planning of UUVs has become increasingly important. Traditional dynamic programming methods face challenges in terms of accuracy and real-time performance, requiring the design of auxiliary strategies to achieve ideal avoidance and requiring cumbersome perception equipment to support them. Therefore, exploring an efficient and easy-to-implement dynamic programming method has significant theoretical and practical value. In this study, an LSTM-RNN network structure suitable for UUVs was designed to learn the dynamic programming mode of UUVs in an unknown environment. The research was divided into three main aspects: collecting the required sample dataset for training deep networks, designing the LSTM-RNN network structure, and utilizing LSTM-RNN to achieve dynamic programming. Experimental results demonstrated that LSTM-RNN can learn planning patterns in unknown environments without the need for constructing an environment model or complex perception devices, thus providing significant theoretical and practical value. Consequently, this approach offers an effective solution for autonomous obstacle avoidance planning for UUVs. Full article

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

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18 pages, 5794 KiB

Open AccessArticle

Integrated Approach to Obtain Gas Flow Velocity in Convection Reflow Soldering Oven

by Bubu Xie, Cai Chen, Yihao Lin, Dong Chen, Wei Huang, Kailin Pan and Yubing Gong

Symmetry 2023, 15(9), 1739; https://doi.org/10.3390/sym15091739 - 11 Sep 2023

Viewed by 751

Abstract

The nozzle-matrix gas flow velocity has a great influence on the accuracy of the temperature field of a printed circuit board assembly (PCBA) during the hot air convection reflow soldering process. This paper proposes a new approach that integrates the theoretical calculation, numerical simulation and an experimental test to accurately determine the nozzle-matrix gas flow velocity. First, the temperature profile of the aluminum alloy thin plate in convection reflow ovens is measured using a Wiken tester. Second, the nozzle-matrix gas flow velocity is theoretically calculated with the Martin formula. The computational fluid dynamic (CFD)simulation is performed according to the Icepak code, where a single oven chamber model is established to represent the 10 zones of soldering ovens to reduce computational resources considering the supry of the soldering ovens. The simulated temperature profile of the aluminum alloy thin plate is obtained and the specific response surface model (RSM) is established to represent the deviation between the simulated temperature and the measured temperature. Finally, based on reverse problem analysis, non-linear programming by quadratic Lagrangian (NLPQL) is used to solve the mathematical optimization model with the objective of minimizing the temperature deviation to obtain the corrected nozzle-matrix gas flow velocity. To validate the accuracy, the temperature test and the modeling using the corrected gas flow velocity for a new PCBA component for the soldering ovens is conducted separately. The temperature comparison between the simulation and the test shows that the maximum temperature deviation is within 10 °C. This provides evidence that the nozzle-matrix gas flow velocity obtained by the new approach is accurate and effective. Full article

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

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14 pages, 1058 KiB

Open AccessArticle

Modeling and Improving the Efficiency of Crushing Equipment

by Natalia Vasilyeva, Uliana Golyshevskaia and Aleksandra Sniatkova

Symmetry 2023, 15(7), 1343; https://doi.org/10.3390/sym15071343 - 30 Jun 2023

Cited by 1 | Viewed by 2508

Abstract

Over the last few decades, the demand for energy-efficient mineral-processing methods has continued. The necessity to develop energy-efficient technologies for the mineral industry will increase in the future, considering the exhaustion of high-quality resources and severe environmental limitations. The subject of this study is crushing equipment. It is a complex of units designed to reduce the fraction of ore and non-metallic solid materials. It is also designed to make them more symmetrical in order to facilitate their transport and later use in production. Thus, the urgency of using crushers in mining and processing plants is clear, so it is relevant to find ways to optimize their operation and reduce energy consumption. This article presents a systematic review of the task of improving the energy efficiency of crushing units. This is achieved by studying modelling methods and results, the automation of crushing and grinding processes, and the wear reduction of crusher components. On the grounds of the reviewed sources, the main methods of increasing the efficiency of crushing units are identified. A mathematical model of the cone crusher was designed. The simulation error is less than 6%. A simulation experiment was carried out on the mathematical model. The dependences of the current and power of the crusher electric drive on the feeder capacity are determined; the graphs have a symmetrical position relative to the approximating curve (R² ≈ 0.9). Full article

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

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19 pages, 5817 KiB

Open AccessArticle

Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical Chambers

by Yuriy Kozhubaev, Elena Ovchinnikova, Ivanov Viacheslav and Svetlana Krotova

Symmetry 2023, 15(6), 1206; https://doi.org/10.3390/sym15061206 - 05 Jun 2023

Cited by 1 | Viewed by 1197

Abstract

Soft robotics is a specialized field of robotics that focuses on the design, manufacture, and control of robots made of soft materials, as opposed to those made of rigid links. One of the primary challenges for the future use of continuous or hyper-redundant robotics systems in industrial and medical technology is the development of suitable modeling and control approaches. Due to the complex non-linear behavior of soft materials and the unpredictable motion of actuators, the task of modeling complex soft actuators is very time-consuming. As a result, earlier studies have undertaken research into model-free methods for controlling soft actuators. In recent years, machine learning (ML) methods have become widely popular in research. The adaptability of an ML model to a non-linear soft drive system alongside the varying actuation behavior of soft drives over time as a result of material characteristics and performance requirements is the key rationale for including an ML model. The system requires the online updating of the ML model in order to work with the non-linear system. Sequential data collected from the test bench and converted into a hypothesis are used to perform incremental learning. These methods are called lifelong learning and progressive learning. Real-time data flow training is combined with incremental learning (IL), and a neural network model is tuned sequentially for each data input. In this article, a method for the intelligent control of soft pneumatic actuators based on an incremental learning algorithm is proposed. A soft pneumatic actuator was subjected to three distinct test conditions in a controlled test environment for a specified duration of data gathering. Additionally, data were collected through finite element method simulations. The collected data were used to incrementally train a neural network, and the resulting model was analyzed for errors with both training and test data. The training and testing errors were compared for different incremental learning (IL) algorithms, including K-nearest neighbors, a decision tree, linear regression, and a neural network. The feasibility of the modulo-free intelligent control of soft pneumatic actuators based on an incremental learning algorithm was verified, solving the problem of the control of software actuators. Full article

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

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13 pages, 708 KiB

Open AccessArticle

Error State Extended Kalman Filter Localization for Underground Mining Environments

by Igor Brigadnov, Aleksandr Lutonin and Kseniia Bogdanova

Symmetry 2023, 15(2), 344; https://doi.org/10.3390/sym15020344 - 26 Jan 2023

Cited by 14 | Viewed by 4171

Abstract

The article addresses the issue of mobile robotic platform positioning in GNSS-denied environments in real-time. The proposed system relies on fusing data from an Inertial Measurement Unit (IMU), magnetometer, and encoders. To get symmetrical error gauss distribution for the measurement model and achieve better performance, the Error-state Extended Kalman Filter (ES EKF) is chosen. There are two stages of vector state determination: vector state propagation based on accelerometer and gyroscope data and correction by measurements from additional sensors. The error state vector is composed of the velocities along the x and y axes generated by combining encoder data and the orientation of the magnetometer around the axis z. The orientation angle is obtained from the magnetometer directly. The key feature of the algorithm is the IMU measurements’ isolation from additional sensor data, with its further summation in the correction step. Validation is performed by a simulation in the ROS (Robot Operating System) and the Gazebo environment on the grounds of the developed mathematical model. Trajectories for the ES EKF, Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF) algorithms are obtained. Absolute position errors for all trajectories are calculated with an EVO package. It is shown that using the simplified version of IMU’s error equations allows for the achievement of comparable position errors for the proposed algorithm, EKF and UKF. Full article

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

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27 pages, 15239 KiB

Open AccessArticle

A Comprehensive Analysis of Smart Grid Stability Prediction along with Explainable Artificial Intelligence

by Ferhat Ucar

Symmetry 2023, 15(2), 289; https://doi.org/10.3390/sym15020289 - 20 Jan 2023

Cited by 5 | Viewed by 2411

Abstract

As the backbone of modern society and industry, the need for a more efficient and sustainable electrical grid is crucial for proper energy management. Governments have recognized this need and have included energy management as a key component of their plans. Decentralized Smart Grid Control (DSGC) is a new approach that aims to improve demand response without the need for major infrastructure upgrades. This is achieved by linking the price of electricity to the frequency of the grid. While DSGC solutions offer benefits, they also involve several simplifying assumptions. In this proposed study, an enhanced analysis will be conducted to investigate how data analytics can be used to remove these simplifications and provide a more detailed understanding of the system. The proposed data-mining strategy will use detailed feature engineering and explainable artificial intelligence-based models using a public dataset. The dataset will be analyzed using both classification and regression techniques. The results of the study will differ from previous literature in the ways in which the problem is handled and the performance of the proposed models. The findings of the study are expected to provide valuable insights for energy management-based organizations, as it will maintain a high level of symmetry between smart grid stability and demand-side management. The proposed model will have the potential to enhance the overall performance and efficiency of the energy management system. Full article

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

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12 pages, 2647 KiB

Open AccessArticle

Modelling of Red-Mud Particle-Solid Distribution in the Feeder Cup of a Thickener Using the Combined CFD-DPM Approach

by Elmira Fedorova, Elena Pupysheva and Vladimir Morgunov

Symmetry 2022, 14(11), 2314; https://doi.org/10.3390/sym14112314 - 04 Nov 2022

Cited by 12 | Viewed by 1604

Abstract

The paper evaluates the behavior of a red-mud solid fraction in a thickener feeder cup, aiming to identify the main characteristics of particle distribution in the flocculation zone and to determine the dependencies affecting the further process taking place in the particle-free sedimentation zone in the thickener-thickening unit. This work used mathematical and numerical modeling to study the influence of such parameters as the flow rate of the feed pulp in the thickener, the flow rate of the flocculant, the density of pulp at the inlet to the unit, and the viscosity and temperature of the pulp on the particle-size distribution from under the feeder cup. The results and dependencies obtained are intended to be used as nominal values in the red-mud thickening process performed on a lab-scale unit. Full article

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

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Review

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12 pages, 1261 KiB

Open AccessReview

The Present Issues of Control Automation for Levitation Metal Melting

by Aleksei Boikov and Vladimir Payor

Symmetry 2022, 14(10), 1968; https://doi.org/10.3390/sym14101968 - 21 Sep 2022

Cited by 16 | Viewed by 1934

Abstract

This article is a review of current scientific problems in the field of automation of the electromagnetic levitation melting process control of non-ferrous metals and potential solutions using modern digital technologies. The article describes the technological process of electromagnetic levitation melting as a method of obtaining ultrapure metals and the main problems of the automation of this process taking into account domestic and international experience. Promising approaches to control the position of the melt in the inductor in real time on the basis of vision systems are considered. The main problems and factors preventing the mass introduction of levitation melting in the electromagnetic field to the industry are highlighted. The problem of passing the Curie point by the heated billet and the effect of the billet’s loss of magnetism on the vibrational circuit of the installation and the temperature of the inductor are also considered. The article also reflects key areas of research development in the field of levitation melting, including: optimization of energy costs, stabilization of the position of the melt in the inductor, predictive process control, and scaling of levitation melting units. The concept of a digital twin based on a numerical model as a component of an automatic process control system for the implementation of inductor control and prediction of process parameters of the melt is presented. The possibility of using vision for visual control of the melt position in the inductor based on video images for its further stabilization in the inductor and increasing the accuracy of numerical simulation results by specifying the real geometry of the melt in parallel with the calculation of the model itself is considered. Full article

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

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