scholarly journals SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3338 ◽  
Author(s):  
Krzysztof Gaska ◽  
Agnieszka Generowicz
Keyword(s):  
Energy Efficiency ◽  
Waste Management ◽  
Real Time ◽  
Smart Cities ◽  
Measurement Data ◽  
Control Object ◽  
Diagnostic Tools ◽  
Network Systems ◽  
Technological Processes ◽  
Technology Processes

The paper presents advanced computational solutions for selected sectors in the context of the optimization of technology processes as an innovation and progress in improving energy efficiency of smart cities. The main emphasis was placed on the sectors of critical urban infrastructure, including in particular the use of algorithmic models based on artificial intelligence implemented in supervisory control systems (SCADA-type, including Virtual SCADA) of technological processes involving the sewage treatment systems (including in particular wastewater treatment systems) and waste management systems. The novelty of the presented solution involves the use of predictive diagnostic tools, based on multi-threaded polymorphic models supporting decision making processes during the control of a complex technological process and objects of distributed network systems (smart water grid, smart sewage system, smart waste management system) and solving problems of optimal control for smart dynamic objects with logical representation of knowledge about the process, the control object and the control itself, for which the learning process consists of successive validation and updating of knowledge and the use of the results of this updating to make control decisions. The advantage of the proposed solution in relation to the existing ones lies in the use of advanced models of predictive diagnostics, validation and reconstruction of data, implemented in functional tools, allowing the stabilization of the work of technological objects through the use of FTC technology (fault tolerant control) and soft sensors, predictive measurement path diagnostics (sensors, transducers), validation and reconstruction of measurement data from sensors in the measuring paths in real time. The dedicated tools (Intelligent Real Time Diagnostic System − iRTDS) built into the system of a hierarchical, multi-threaded control optimizing system of SCADA system allow to obtain advanced diagnostics of technological processes in real time using HPC technology. In effect of the application of the proprietary iRTDS tool, we obtain a significant rise of energy efficiency of technological processes in key sectors of the economy, which in global terms, e.g., urban agglomeration, increases the economic efficiency.

scholarly journals Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Simon Elias Bibri ◽  
John Krogstie
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Real Time ◽  
Environmental Sustainability ◽  
Smart Cities ◽  
Data Driven ◽  
Urban Metabolism ◽  
Pollution Reduction ◽  
Illustrative Case ◽  
Sustainable Cities

AbstractThe IoT and big data technologies have become essential to the functioning of both smart cities and sustainable cities, and thus, urban operational functioning and planning are becoming highly responsive to a form of data-driven urbanism. This offers the prospect of building models of smart sustainable cities functioning in real time from routinely sensed data. This in turn allows to monitor, understand, analyze, and plan such cities to improve their energy efficiency and environmental health in real time thanks to new urban intelligence functions as an advanced form of decision support. However, prior studies tend to deal largely with data-driven technologies and solutions in the realm of smart cities, mostly in relation to economic and social aspects, leaving important questions involving the underlying substantive and synergistic effects on environmental sustainability barely explored to date. These issues also apply to sustainable cities, especially eco-cities. Therefore, this paper investigates the potential and role of data-driven smart solutions in improving and advancing environmental sustainability in the context of smart cities as well as sustainable cities, under what can be labeled “environmentally data-driven smart sustainable cities.” To illuminate this emerging urban phenomenon, a descriptive/illustrative case study is adopted as a qualitative research methodology§ to examine and compare Stockholm and Barcelona as the ecologically and technologically leading cities in Europe respectively. The results show that smart grids, smart meters, smart buildings, smart environmental monitoring, and smart urban metabolism are the main data-driven smart solutions applied for improving and advancing environmental sustainability in both eco-cities and smart cities. There is a clear synergy between such solutions in terms of their interaction or cooperation to produce combined effects greater than the sum of their separate effects—with respect to the environment. This involves energy efficiency improvement, environmental pollution reduction, renewable energy adoption, and real-time feedback on energy flows, with high temporal and spatial resolutions. Stockholm takes the lead over Barcelona as regards the best practices for environmental sustainability given its long history of environmental work, strong environmental policy, progressive environmental performance, high environmental standards, and ambitious goals. It also has, like Barcelona, a high level of the implementation of applied data-driven technology solutions in the areas of energy and environment. However, the two cities differ in the nature of such implementation. We conclude that city governments do not have a unified agenda as a form of strategic planning, and data-driven decisions are unique to each city, so are environmental challenges. Big data are the answer, but each city sets its own questions based on what characterize it in terms of visions, policies, strategies, pathways, and priorities.

scholarly journals Monitoring and diagnostics of special-purpose turbomachines

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Małgorzata Gizelska
Keyword(s):  
Real Time ◽  
Measurement Data ◽  
Magnetic Bearing ◽  
Operating Conditions ◽  
Active Magnetic Bearing ◽  
Diagnostic Tools ◽  
Compression Process ◽  
Rotating System ◽  
On Line ◽  
Monitoring And Diagnostics

Abstract In the operation of special-purpose turbomachines, diagnostic tools are necessary. They enable control of the machine technical state and its operation parameters in the on-line mode. The acquisition and processing of the measurement data in real time is crucial as they are indicators of the machine functioning under various operating conditions. The paper presents two types of computer designed diagnostic tools to monitor in real time the dynamic and thermodynamic parameters of special-purpose turbomachines. The first one monitors the dynamics of the rotating system with an active magnetic bearing, the second - monitors the instant value of polytropic efficiency of the compression process, which was designed for the industrial machine.

Internet of Things and Long-Range-Based Smart Lampposts for Illuminating Smart Cities

2021 ◽  
Vol 13 (11) ◽  
pp. 6398
Author(s):  
Anita Gehlot ◽  
Sultan S. Alshamrani ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
Shaik Vaseem Akram ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Real Time ◽  
Long Range ◽  
Smart Cities ◽  
Renewable Energy Sources ◽  
Environmental Parameters ◽  
Lighting System ◽  
Broad Perspective ◽  
Image Sensing

Intelligent and resilient infrastructure is necessary for smart cities for contributing flexible and smart amenities to the citizens. Concerning the United Nations (UN) estimation, the global population residing in urban cities will reach 68% by 2050. Additionally, the Sustainable Energy Action Plans (SEAP) report suggests implementing energy efficiency technologies in smart cities to meet the rising urban population requirement. Internet of Things (IoT) technology empowers to achieve the goal of energy efficiency by integrating sensors, wireless technology, and renewable energy sources in the lighting system. At present, the IoT-based lighting system in urban cities is implemented with streetlamps and lampposts. In this study, we are focusing on lampposts, as it has the flexibility of establishing and implementing a multitude of applications on a single system. Due to technological advancement, the lamppost is embedded with multiple sensors, communication protocols, and energy distribution infrastructure for delivering smart and affordable amenities to the citizens residing in the smart cities. This motivates us to implement a smart lamppost that provides a multitude of applications such as smart light, digital signs, environmental monitoring conditions, electric vehicle (EV) charging port, wireless fidelity (Wi-Fi) hotspot, etc., on a single lamppost. This study proposed the IoT-assisted fog and edge-based smart lamppost for the smart cities to realize the smart infrastructure. Further, this smart lamppost is integrated with low power and long-range communication, i.e., Long Range (LoRa), enabling the smart lamppost to communicate the sensory data to a long-range. Additionally, LoRa is integrated with a Wi-Fi module for establishing the interconnection between the smart lamppost and IoT server. Generally, the proposed architecture is broad perspective; however, we have developed and implemented the hardware models of three components including lighting system, environmental parameters and image sensing in real time. Lighting system and environmental parameter monitoring are integrated on same hardware model for sensing and logging the real-time values of temperature, humidity, CO and light intensity on the IoT server. The developed image sensing prototype based on ESP 32 controller is also evaluated in real-time scenarios, and the performance of the prototype is efficient. The proposed system delivers reliable performance in terms of sensing and communicating environmental parameters and images to the IoT server. Moreover, in future, we will complete the development of other components of the smart lamppost for enhancing the smarter infrastructure in smart cities.

scholarly journals Color sensor application in high-temperature spectral ratio pyrometer

2022 ◽  
Vol 1211 (1) ◽  
pp. 012022
Author(s):  
A A Valke ◽  
D G Lobov ◽  
A G Shkaev
Keyword(s):  
Energy Efficiency ◽  
High Temperature ◽  
Infrared Spectrum ◽  
Control Object ◽  
Spectral Ratio ◽  
Thermal Control ◽  
Radiation Receiver ◽  
Sensor Application ◽  
Technological Processes ◽  
Color Sensor

Abstract Contactless thermal control tools play an important role in solving the high-temperature technological processes improving energy efficiency problems. In order to create such controls, the authors analyzed the developing possibility of spectral ratio high-temperature pyrometer using a multispectral radiation receiver (color sensor) TCS34725. In the paper this receiver application coefficients are determined, signals ratio graphs in different spectral intervals on temperature are given for two applications: without additional filtration of the control object radiation infrared component and using an opaque in the infrared spectrum part external filter.

scholarly journals SYNTHESIS OF THERMAL DIAGNOSTIC EXPERT COMPONENTS WITH AN ARTIFICIAL NEURON

2021 ◽  
Vol 1 (161) ◽  
pp. 148-156
Author(s):  
S. Yesaulov ◽  
О. Babicheva ◽  
D. Akinshyn
Keyword(s):  
Initial Data ◽  
Real Time ◽  
Technical Condition ◽  
Rate Of Change ◽  
Diagnostic Tools ◽  
Fault Identification ◽  
Network Systems ◽  
Artificial Neuron ◽  
Electronic Model ◽  
Electromechanical Equipment

The article notes the growing popularity of digital programmable technology in diagnostic monitoring systems of electromechanical equipment (EME) for various purposes due to the ability to monitor the technical condition of operating devices in real time. The main reasons that restrain the use of DMS with artificial neural networks in the municipal sphere are considered. It has been noted the directions of improvement of popular means of thermal parameters monitoring and hardware solutions to increase the initial data validity used in the possible EMO fault identification. The purpose of this work was to study and develop components for the formation of initial information, including artificial neurons, which make it possible to increase the reliability of possible fault identification accompanied by heating of individual parts of the operated electromechanical equipment. Based on the adopted algorithm for approximating the initial data arrays, the priority of using the logistic function for modeling the rate of temperature change in the EME was justified. It have been proposed the electronic model structure of an artificial neuron (AN) and an algorithm for generating information output signal, depending on the rate of change of a controlled parameter at a technological object. It have been presented the electronic modeling results in the Simulink environment and the physical implementation of the AN electronic model, which confirmed the suitability of the proposed device in the diagnostic thermal expert of the EME technical condition during its operation in real time. Electronic experiments with AN made it possible to obtain a calibration characteristic for a practical assessment of the tendency for the development of non-standardized thermal events that may cause possible faults in certain parts of the equipment. It have been considered possible options for using AN in local thermal diagnostic tools for the analysis and assessment of events indicating the feasibility of performing unscheduled maintenance or preceding possible and unknown electromechanical equipment faults. It has been presented the results of experiments and simulation of thermal processes, confirming the expandability of the functional diagnostic devices properties with neural network systems, which popularity is constantly growing.

scholarly journals Multivariate Statistical Network Monitoring–Sensor: An effective tool for real-time monitoring and anomaly detection in complex networks and systems

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092130
Author(s):  
Roberto Magán-Carrión ◽  
José Camacho ◽  
Gabriel Maciá-Fernández ◽  
Ángel Ruíz-Zafra
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Smart Grids ◽  
Detection System ◽  
Smart Cities ◽  
Low Cost ◽  
Health Care Systems ◽  
Network Monitoring ◽  
Network Systems ◽  
Multivariate Statistical

Technology evolves quickly. Low-cost and ready-to-connect devices are designed to provide new services and applications. Smart grids or smart health care systems are some examples of these applications. In this totally connected scenario, some security issues arise due to the large number of devices and communications. In this way, new solutions for monitoring and detecting security events are needed to address new challenges brought about by this scenario, among others, the real-time requirement allowing quick security event detection and, consequently, quick response to attacks. In this sense, Intrusion Detection Systems are widely used though their evaluation often relies on the use of predefined network datasets that limit their application in real environments. In this work, a real-time and ready-to-use tool for monitoring and detecting security events is introduced. The Multivariate Statistical Network Monitoring–Sensor is based on the Multivariate Statistical Network Monitoring methodology and provides an alternative way for evaluating Multivariate Statistical Network Monitoring–based Intrusion Detection System solutions. Experimental results based on the detection of well-known attacks in hierarchical network systems prove the suitability of this tool for complex scenarios, such as those found in smart cities or Internet of Things ecosystems.

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