Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
2.8
Journals
Processes
Special Issues
Active Flow Control Processes with Machine Learning and the Internet...
share announcement

Active Flow Control Processes with Machine Learning and the Internet of Things

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 44973

Special Issue Editors

Prof. Dr. Valentina E. Balas

E-Mail Website
Guest Editor
Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, Ro-310025 Arad, Romania
Interests: intelligent systems; soft computing; fuzzy control; modeling and simulation; nonlinear systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dipankar Deb

E-Mail Website
Guest Editor
Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India
Interests: active flow control; aircraft flight control; machine learning; wind farm controls
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The topic of this Special Issue concerns the latest developments and investigations in the fields of flow control with a focus on data intensive studies involving machine learning, deep learning, and possibly applied using the Internet of Things. Boundary layer separation entails great energy losses and limits the performance of most flow-related devices. It imposes severe limitations not only on the design, but it also affects the operation and performance. Therefore, the control or realignment of boundary layer separation is warranted. Active flow control (AFC) is a fast-growing multidisciplinary science and technology aimed at altering a natural flow state or development path into a more desired state. These methods are used majorly to achieve transition delay, drag reduction, lift enhancement, turbulence management, separation postponement, noise suppression, etc. The potential benefits of flow control may include improved performance, affordability, fuel consumption economy, and environmental compliance. In recent years, attention has also been focused on the control and suppression of combustion instabilities by actively and continuously perturbing certain combustion parameters in order to interrupt the growth and persistence of resonant oscillations. Implementing active flow control methods also permits improving the performance of wind turbines. Remarkable developments in control theory have considerably expanded the selection of available tools which may be applied to regulate physical systems. These techniques show great benefits for several applications in fluid mechanics, including the delay of flow transition, and thus of turbulence. In the field of biomedical engineering, modification of the flow properties of the blood for drag reduction in the arteries by addition of polymers/chemistry control in the blood could reduce the number of heart attacks or strokes due to clotting. Advanced drug delivery systems could be designed on the basis of our ability to control certain fluid properties and trajectories by direct physical manipulation or remote control of either the delivery systems or the fluids of interest. Potential topics include but are not limited to:

  • Accurate and efficient active and passive flow control devices for aeronautical applications;
  • Sensor-actuator integrated systems, with a possible focus on MEMS devices;
  • Smart structures combined with drag reduction techniques;
  • Laminar flow and engine integration technologies;
  • Synergy of active or passive flow and noise control technologies;
  • Flow control in propulsive systems;
  • Experimental characterization and reliable numerical simulation of flow field in the presence of actuators;
  • Wireless networks for active flow devices;
  • Energy management systems and networks for active flow devices;
  • Smart environment monitoring and control;
  • Smart management of active flow devices;
  • Innovative applications and services for active flow devices;
  • Machine learning methods applied to active flow devices;
  • Artificial neural networks for active flow control.

Prof. Dr. Valentina Emilia Balas
Guest Editor

Manuscript Submission Information

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

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.

Keywords

  • active flow control
  • machine learning
  • Internet of Things
  • biomedical processes
  • flow separation
  • control theory
  • drug delivery systems

Published Papers (14 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

3 pages, 181 KiB  
Editorial
Special Issue “Active Flow Control Processes with Machine Learning and the Internet of Things”
by Dipankar Deb, Valentina Emilia Balas and Mrinal Kaushik
Processes 2023, 11(5), 1359; https://doi.org/10.3390/pr11051359 - 28 Apr 2023
Viewed by 896
Abstract
The desired changes in flow characteristics are obtained by flow control, which implies manipulating flow behavior such as drag reduction, mixing augmentation, or noise attenuation, employing active or passive devices [...] Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)

Research

Jump to: Editorial, Review

31 pages, 11490 KiB  
Article
Fabrication and Analysis of Polydimethylsiloxane (PDMS) Microchannels for Biomedical Application
by Shahzadi Tayyaba, Muhammad Waseem Ashraf, Zubair Ahmad, Ning Wang, Muhammad Javaid Afzal and Nitin Afzulpurkar
Processes 2021, 9(1), 57; https://doi.org/10.3390/pr9010057 - 29 Dec 2020
Cited by 12 | Viewed by 3774
Abstract
In this research work, Polydimethylsiloxane (PDMS) has been used for the fabrication of microchannels for biomedical application. Under the internet of things (IoT)-based controlled environment, the authors have simulated and fabricated bio-endurable, biocompatible and bioengineered PDMS-based microchannels for varicose veins implantation exclusively to [...] Read more.
In this research work, Polydimethylsiloxane (PDMS) has been used for the fabrication of microchannels for biomedical application. Under the internet of things (IoT)-based controlled environment, the authors have simulated and fabricated bio-endurable, biocompatible and bioengineered PDMS-based microchannels for varicose veins implantation exclusively to avoid tissue damaging. Five curved ascending curvilinear micro-channel (5CACMC) and five curved descending curvilinear micro-channels (5CDCMC) are simulated by MATLAB (The Math-Works, Natick, MA, USA) and ANSYS (ANSYS, The University of Lahore, Pakistan) with actual environments and confirmed experimentally. The total length of each channel is 1.6 cm. The diameter of both channels is 400 µm. In the ascending channel, the first to fifth curve cycles have the radii of 2.5 mm, 5 mm, 7.5 mm, 10 mm, and 2.5 mm respectively. In the descending channel, the first and second curve cycles have the radii of 12.5 mm and 10 mm respectively. The third to fifth cycles have the radii of 7.5 mm, 5 mm, and 2.5 mm respectively. For 5CACMC, at Reynolds number of 185, the values of the flow rates, velocities and pressure drops are 19.7 µLs−1, 0.105 mm/s and 1.18 Pa for Fuzzy simulation, 19.3 µLs−1, 0.1543 mm/s and 1.6 Pa for ANSYS simulation and 18.23 µLs−1, 0.1332 mm/s and 1.5 Pa in the experiment. For 5CDCMC, at Reynolds number 143, the values of the flow rates, velocities and pressure drops are 15.4 µLs−1, 0.1032 mm/s and 1.15 Pa for Fuzzy simulation, 15.0 µLs−1, 0.120 mm/s and 1.22 Pa for ANSYS simulation and 14.08 µLs−1, 0.105 mm/s and 1.18 Pa in the experiment. Both channels have three inputs and one output. In order to observe Dean Flow, Dean numbers are also calculated. Therefore, both PDMS channels can be implanted in place of varicose veins to have natural blood flow. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

16 pages, 3481 KiB  
Article
Active Control Parameters Monitoring for Freight Trains, Using Wireless Sensor Network Platform and Internet of Things
by Adrian Brezulianu, Cristian Aghion, Marius Hagan, Oana Geman, Iuliana Chiuchisan, Alexandra-Ligia Balan, Doru-Gabriel Balan and Valentina Emilia Balas
Processes 2020, 8(6), 639; https://doi.org/10.3390/pr8060639 - 27 May 2020
Cited by 10 | Viewed by 3789
Abstract
Operating in a dynamic and competitive global market, railway companies have realized many years ago that better management of their logistical operations will enhance their strategic positions on the market. The financial component of daily operations is of utmost importance these days and [...] Read more.
Operating in a dynamic and competitive global market, railway companies have realized many years ago that better management of their logistical operations will enhance their strategic positions on the market. The financial component of daily operations is of utmost importance these days and many companies concluded that maximizing the profit relies on the integration of logistical activities with better income management. This paper presents a system consisting of three components: Ferodata BOX, Ferodata MOBILE, and Ferodata SYS, used to transmit to a web-server the status and operating information of an electric or diesel train. Train information includes data from locomotives, wagons, train driver, route, direction, fuel or electric consumption, speed, etc. All this information is processed in real-time and can be viewed in the web-server application. Additionally, the web-server application could manage and report details that are coming from the wagons, such as valuable information regarding the bogie wear, the identification of the wagons attached to a gasket, and identification the situations in which a wagon or group of wagons comes off the gasket configuration. All information about the status of trains is available on-line and at any moment the person responsible for management can use these data in their work. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

22 pages, 5083 KiB  
Article
New Model-Based Analysis Method with Multiple Constraints for Integrated Modular Avionics Dynamic Reconfiguration Process
by Zeyong Jiang, Tingdi Zhao, Shihai Wang and Hongyan Ju
Processes 2020, 8(5), 574; https://doi.org/10.3390/pr8050574 - 13 May 2020
Cited by 5 | Viewed by 2862
Abstract
With the development of integrated modular avionics (IMA), the dynamic reconfiguration of IMA not only provides great advantages in resource utilization and aircraft configuration, but also acts as a valid means for resource failure management. It is vital to ensure the correction of [...] Read more.
With the development of integrated modular avionics (IMA), the dynamic reconfiguration of IMA not only provides great advantages in resource utilization and aircraft configuration, but also acts as a valid means for resource failure management. It is vital to ensure the correction of the IMA dynamic reconfiguration process. The analysis of the dynamic reconfiguration process is a significant task. The Architecture Analysis & Design Language (AADL) is widely used in complicated real-time embedded systems. The language can describe the system configuration and the execution behaviors, such as configuration changes. Petri net is a widely used tool to conduct simulation analysis in many aspects. In this study, a model-based analyzing method with multiple constraints for the IMA dynamic reconfiguration process was proposed. First, several design constraints on the process were investigated. Second, the dynamic reconfiguration process was modeled based on the AADL. Then, a set of rules for the transition of the model from AADL to Petri net was generated, and the multi-constraints proposed were incorporated into Petri net for analysis. Finally, a simulation multi-constraint analysis with Petri net for the process of IMA dynamic reconfiguration was conducted. Finally, a case study was employed to demonstrate this method. This method is advantageous to the validity of IMA dynamic reconfiguration at the beginning of the system design. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

16 pages, 1195 KiB  
Article
FRED—Flexible Framework for Frontend Electronics Control in ALICE Experiment at CERN
by Milan Tkáčik, Ján Jadlovský, Slávka Jadlovská, Lukáš Koska, Anna Jadlovská and Michele Donadoni
Processes 2020, 8(5), 565; https://doi.org/10.3390/pr8050565 - 11 May 2020
Cited by 4 | Viewed by 2994
Abstract
A substantial part of Distributed Control Systems are SCADA systems that require connection to low level electronics through standard industrial interfaces and protocols. When implementing Distributed Control Systems for physics experiments, it is often necessary to use custom made electronics that do not [...] Read more.
A substantial part of Distributed Control Systems are SCADA systems that require connection to low level electronics through standard industrial interfaces and protocols. When implementing Distributed Control Systems for physics experiments, it is often necessary to use custom made electronics that do not have the ability to communicate using standard protocols, but instead use custom communication protocols. This paper describes the new Front End Device (FRED) framework, which provides the possibility of connecting custom electronics to standard SCADA systems, thus filling the gap in the implementation of Distributed Control Systems that deploy custom electronics. The FRED framework also serves as a translation layer, which provides translation of raw values acquired from electronics to real physical quantities and vice versa. At the same time, it is easy to use, since there is no need for additional programming when used in the simple mode, and its entire functionality can be configured in several configuration files. In case of the need to perform more complex operations over electronics, it is possible to use the provided API for the implementation of additional program functionalities. Tests of the FRED framework have shown that it is fast and scalable enough for use within the Distributed Control Systems of large physics experiments. Based on experience with the implementation of the FRED framework in real-world systems of physics experiments, it can be stated that it meets all requirements for data processing throughput. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

14 pages, 2873 KiB  
Article
Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction
by Jonggeun Kim, Hansoo Lee, Jeong Woo Jeon, Jong Moon Kim, Hyeon Uk Lee and Sungshin Kim
Processes 2020, 8(4), 456; https://doi.org/10.3390/pr8040456 (registering DOI) - 12 Apr 2020
Cited by 11 | Viewed by 2837
Abstract
Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine [...] Read more.
Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine tool should be maintained appropriately to working environment. In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs. Tool monitoring and diagnosis are some of the most important components of the condition based maintenance. This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool. The diagnosis model, which only uses cutting force data, cannot sufficiently reflects tool condition. Hence, we modeled diagnosis model using features extracted from a cutting force, a current signal, and coefficients of the discrete wavelet transform. The experimental results showed that the model which uses feature data has better performance than the model that uses only cutting force data. The feature based models are lower false negative rate (FNR) and false positive rate. Moreover, squared prediction error using normalized residual vector also reduced FNR because normalization reduces weight bias. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

14 pages, 2920 KiB  
Article
Infrared Infusion Monitor Based on Data Dimensionality Reduction and Logistics Classifier
by Xiaoli Wang, Haonan Zhou and Yong Song
Processes 2020, 8(4), 437; https://doi.org/10.3390/pr8040437 - 7 Apr 2020
Cited by 2 | Viewed by 2625
Abstract
This paper presents an infrared infusion monitoring method based on data dimensionality reduction and a logistics classifier. In today’s social environment, nurses with hospital infusion work are under excessive pressure. In order to improve the information level of the traditional medical process, hospitals [...] Read more.
This paper presents an infrared infusion monitoring method based on data dimensionality reduction and a logistics classifier. In today’s social environment, nurses with hospital infusion work are under excessive pressure. In order to improve the information level of the traditional medical process, hospitals have introduced a variety of infusion monitoring devices. The current infusion monitoring equipment mainly adopts the detection method of infrared liquid drop detection to realize non-contact measurements. However, a large number of experiments have found that the traditional infrared detection method has the problems of low voltage signal amplitude variation and low signal-to-noise ratio (SNR). Conventional threshold judgment or signal shaping cannot accurately judge whether droplets exist or not, and complex signal processing circuits can greatly increase the cost and power consumption of equipment. In order to solve these problems, this paper proposes a method for the accurate measurement of droplets without increasing the cost, that is, a method combining data drop and a logistics classifier. The dimensionalized data and time information are input into the logistics classifier to judge the drop landing. The test results show that this method can significantly improve the accuracy of droplet judgment without increasing the hardware cost. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

16 pages, 5117 KiB  
Article
Aerodynamic Studies on Non-Premixed Oxy-Methane Flames and Separated Oxy-Methane Cold Jets
by Tamal Jana, Mrinal Kaushik, Dipankar Deb, Vlad Mureşan and Mihaela Ungureşan
Processes 2020, 8(4), 429; https://doi.org/10.3390/pr8040429 - 3 Apr 2020
Cited by 4 | Viewed by 2865
Abstract
Both cold and flame jets find numerous applications in different fields, ranging from domestic applications to aerospace and space technology. Indeed, the applications of isothermal and non-isothermal jets in the flame heating industry fascinated the researchers to gain an in-depth understanding. Nevertheless, these [...] Read more.
Both cold and flame jets find numerous applications in different fields, ranging from domestic applications to aerospace and space technology. Indeed, the applications of isothermal and non-isothermal jets in the flame heating industry fascinated the researchers to gain an in-depth understanding. Nevertheless, these benefits are not standalone, rather, they are associated with major disadvantages such as improper jet mixing and flame instabilities that require careful remedies. In the present investigation, three-inline jets, having methane jet at the center and two oxygen jets at the periphery, are studied computationally in a three-dimensional domain, with and without considering the effects of combustion. To study the mixing characteristics of cold jets, the radial velocity distributions at different streamwise locations have been analyzed at the jet inlet velocity of 27 m/s. However, for oxygen and methane flame jets, inlet velocities are varied as 27 m/s and 54 m/s. Moreover, to investigate the effects of temperature variation on mixing characteristics at a particular jet velocity, the inlet temperatures of reactants are varied as 300 K, 500 K, and 700 K, at the jet inlet velocity of 27 m/s. Combustion is found to have a profound impact on the mixing characteristics. At the inlet temperature of 300 K, a higher centerline velocity decay is observed for non-reactive jets as compared to reactive flame jets. Moreover, the turbulent kinetic energy distribution is relatively higher in the case of non-reactive jets, which is the direct evidence of an augmented mixing. As is obvious, the turbulent kinetic energy at the jet shear layer is maximum due to the formation of large-scale coherent eddies. The decay in centerline velocity is found to be increasing with an increase of inlet temperature. Additionally, with an increase of jet velocity, the radial velocity profiles become more asymmetrical, consequently yielding an unstable flame. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Graphical abstract

21 pages, 3042 KiB  
Article
A Molecular Force Field-Based Optimal Deployment Algorithm for UAV Swarm Coverage Maximization in Mobile Wireless Sensor Network
by Xi Wang, Guan-zheng Tan, Fan-Lei Lu, Jian Zhao and Yu-si Dai
Processes 2020, 8(3), 369; https://doi.org/10.3390/pr8030369 - 22 Mar 2020
Cited by 7 | Viewed by 2993
Abstract
In the mobile wireless sensor network (MWSN) field, there exists an important problem—how can we quickly form an MWSN to cover a designated working area on the ground using an unmanned aerial vehicle (UAV) swarm? This problem is of significance in many military [...] Read more.
In the mobile wireless sensor network (MWSN) field, there exists an important problem—how can we quickly form an MWSN to cover a designated working area on the ground using an unmanned aerial vehicle (UAV) swarm? This problem is of significance in many military and civilian applications. In this paper, inspired by intermolecular forces, a novel molecular force field-based optimal deployment algorithm for a UAV swarm is proposed to solve this problem. A multi-rotor UAV swarm is used to carry sensors and quickly build an MWSN in a designated working area. The necessary minimum number of UAVs is determined according to the principle that the coverage area of any three UAVs has the smallest overlap. Based on the geometric properties of a convex polygon, two initialization methods are proposed to make the initial deployment more uniform, following which, the positions of all UAVs are subsequently optimized by the proposed molecular force field-based deployment algorithm. Simulation experiment results show that the proposed algorithm, when compared with three existing algorithms, can obtain the maximum coverage ratio for the designated working area thanks to the proposed initialization methods. The probability of falling into a local optimum and the computational complexity are reduced, while the convergence rate is improved. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

13 pages, 340 KiB  
Article
Improved Q-Learning Method for Linear Discrete-Time Systems
by Jian Chen, Jinhua Wang and Jie Huang
Processes 2020, 8(3), 368; https://doi.org/10.3390/pr8030368 - 22 Mar 2020
Cited by 1 | Viewed by 2760
Abstract
In this paper, the Q-learning method for quadratic optimal control problem of discrete-time linear systems is reconsidered. The theoretical results prove that the quadratic optimal controller cannot be solved directly due to the linear correlation of the data sets. The following corollaries have [...] Read more.
In this paper, the Q-learning method for quadratic optimal control problem of discrete-time linear systems is reconsidered. The theoretical results prove that the quadratic optimal controller cannot be solved directly due to the linear correlation of the data sets. The following corollaries have been made: (1) The correlation of data is the key factor in the success for the calculation of quadratic optimal control laws by Q-learning method; (2) The control laws for linear systems cannot be derived directly by the existing Q-learning method; (3) For nonlinear systems, there are some doubts about the data independence of current method. Therefore, it is necessary to discuss the probability of the controllers established by the existing Q-learning method. To solve this problem, based on the ridge regression, an improved model-free Q-learning quadratic optimal control method for discrete-time linear systems is proposed in this paper. Therefore, the computation process can be implemented correctly, and the effective controller can be solved. The simulation results show that the proposed method can not only overcome the problem caused by the data correlation, but also derive proper control laws for discrete-time linear systems. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

21 pages, 5625 KiB  
Article
Intelligent Colored Token Petri Nets for Modeling, Control, and Validation of Dynamic Changes in Reconfigurable Manufacturing Systems
by Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li and Reggie Davidrajuh
Processes 2020, 8(3), 358; https://doi.org/10.3390/pr8030358 - 20 Mar 2020
Cited by 22 | Viewed by 4582
Abstract
The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new [...] Read more.
The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new products, removal of old machines, and changes in processing routes induced by the competitive global market. This paper proposes a new model, the intelligent colored token Petri net (ICTPN), to simulate dynamic changes or reconfigurations of a system. The main idea is that intelligent colored tokens denote part types that represent real-time knowledge about changes and status of a system. Thus, dynamic configurations of a system can be effectively modeled. The developed ICTPN can model dynamic changes of a system in a modular manner, resulting in the development of a very compact model. In addition, when configurations appear, only the changed colored token of the part type from the current model has to be modified. Based on the resultant ICTPN model, deadlock-free, conservative, and reversible behavioral properties, among others, are guaranteed. The developed ICTPN model was tested and validated using the GPenSIM tool and compared with existing methods from the literature. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

18 pages, 3432 KiB  
Article
Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities
by Adrian Korodi, Ruben Crisan, Andrei Nicolae and Ioan Silea
Processes 2020, 8(3), 282; https://doi.org/10.3390/pr8030282 - 2 Mar 2020
Cited by 10 | Viewed by 3186
Abstract
The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or [...] Read more.
The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

19 pages, 6334 KiB  
Article
On Parameter Stability Region of LADRC for Time-Delay Analysis with a Coupled Tank Application
by Dazi Li, Xun Chen, Jianqing Zhang and Qibing Jin
Processes 2020, 8(2), 223; https://doi.org/10.3390/pr8020223 - 14 Feb 2020
Cited by 12 | Viewed by 2733
Abstract
The control of time-delay systems is a hot research topic. Ever since the theory of linear active disturbance rejection control (LADRC) was put forward, considerable progress has been made. LADRC shows a good control effect on the control of time-delay systems. The problem [...] Read more.
The control of time-delay systems is a hot research topic. Ever since the theory of linear active disturbance rejection control (LADRC) was put forward, considerable progress has been made. LADRC shows a good control effect on the control of time-delay systems. The problem about the parameter stability region of LADRC controllers has been seldom discussed, which is very important for practical application. In this study, the dual-locus diagram method, which is used to solve the upper limit of the LADRC controller bandwidth, is studied for both first-order time-delay systems and second-order time-delay systems. The characteristic equation roots distribution is firstly transformed into the problem of finding the frequency of the dual-locus diagram intersection point. To solve the problem for second-order time-delay system LADRC controllers, which is a dual 10-order nonlinear equation, a transformation has been made through Euler’s formula and genetic algorithm (GA) has been adopted to search for the optimal parameters. Simulation results and experimental results on coupled tanks show the effectivity of the proposed method. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

Review

Jump to: Editorial, Research

18 pages, 511 KiB  
Review
A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells
by Dipankar Deb, Ravi Patel and Valentina E. Balas
Processes 2020, 8(5), 583; https://doi.org/10.3390/pr8050583 - 14 May 2020
Cited by 24 | Viewed by 5131
Abstract
A microbial fuel cell (MFC) is a potentially viable renewable energy option which promises effective and commercial harvesting of electrical power by bacterial movement and at the same time also treats wastewater. Microbial fuel cells are complicated devices and therefore research in this [...] Read more.
A microbial fuel cell (MFC) is a potentially viable renewable energy option which promises effective and commercial harvesting of electrical power by bacterial movement and at the same time also treats wastewater. Microbial fuel cells are complicated devices and therefore research in this field needs interdisciplinary knowledge and involves diverse areas such as biological, chemical, electrical, etc. In recent decades, rapid strides have taken place in fuel cell research and this technology has become more efficient. For effective usage, such devices need advanced control techniques for maintaining a balance between substrate supply, mass, charge, and external load. Most of the research work in this area focuses on experimental work and have been described from the design perspective. Recently, the development in mathematical modeling of such cells has taken place which has provided a few mathematical models. Mathematical modeling provides a better understanding of the operations and the dynamics of MFCs, which will help to develop control and optimization strategies. Control-oriented bio-electrochemical models with mass and charge balance of MFCs facilitate the development of advanced nonlinear controllers. This work reviews the different mathematical models of such cells available in the literature and then presents suitable parametrization to develop control-oriented bio-electrochemical models of three different types of cells with their uncertain parameters. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-14
Back to TopTop