scholarly journals Digital Twin for Power Plants, Energy Savings and other Complex Engineering Systems

2021 ◽  
Author(s):  
Ahmad K. Sleiti ◽  
Jayanta S. Kapat ◽  
Ladislav Vesely ◽  
Mohammed Al-Khawaja
Keyword(s):  
Power Plants ◽  
Energy Savings ◽  
High Reliability ◽  
Research Work ◽  
Engineering Systems ◽  
Energy Integration ◽  
Digital Twin ◽  
And Behavior ◽  
Capital Intensive ◽  
Similar Complex

Digital Twin (DT) is a digital representation of a machine, service, or production system that consists of models, information, and data used to characterize properties, conditions, and behavior of the system. Renewable energy integration will make future power plants more complex with addition of varieties of Power-to-X technologies, Electrolysis to green hydrogen, onsite storage and transport of hydrogen, and use of pure or blended hydrogen, etc. These future power plants need robust DT architecture to achieve high Reliability, Availability and Maintainability at lower cost. In this research work, a comprehensive and robust DT architecture for power plants is proposed that also can be implemented in other similar complex capital-intensive large engineering systems. The novelty and advantages of the proposed DT is asserted by reviewing the state-of-the-art of DT in energy industries and its potential to transform these industries. Then the proposed DT architecture and its five components are explained and discussed. More specifically, the main contributions of the present work include: 1. Overview of DT key research and development for energy savings applications to consider important findings, research gaps and the needed future development for the proposed DT for power plants. 2. Overview of DT key research for power plants including applications, frameworks and architectures to consider important findings and to confirm the novelty and robustness of the proposed DT. 3. Proposing and demonstrating new robust DT architecture for power plants and other similar complex capital-intensive large engineering systems.

Reliability analysis in the design of safe nuclear power plants

1989 ◽  
Vol 327 (1596) ◽  
pp. 549-564
Keyword(s):  
Reliability Analysis ◽  
Nuclear Power ◽  
Power Plants ◽  
Failure Modes ◽  
High Reliability ◽  
Fault Tree Analysis ◽  
Statistical Techniques ◽  
Engineering Systems ◽  
Equipment Reliability ◽  
Fundamental Part

The requirement for all potentially hazardous plant is to achieve high reliability of engineering systems by design . The process of reliability analysis is a fundamental part of the design process in the nuclear power industry. Such analysis recognizes that there is always some possibility of engineering equipment failing and therefore the ability of the plant to be reasonably tolerant of such failures is investigated. In this paper the methods and philosophy underlying reliability analysis are briefly explained with examples of qualitative techniques such as failure modes and effects analysis, and fault tree analysis. In addition some of the quantitative models of equipment reliability are discussed and the need for robust statistical techniques for data analysis explained.

"Recent Patents on the Triboelectrostatic Separation of Fly Ash"

2019 ◽  
Vol 13 ◽  
Author(s):  
Haisheng Li ◽  
Wenping Wang ◽  
Yinghua Chen ◽  
Xinxi Zhang ◽  
Chaoyong Li
Keyword(s):  
Fly Ash ◽  
Power Plants ◽  
High Efficiency ◽  
Separation Efficiency ◽  
High Reliability ◽  
Operating Conditions ◽  
Security Requirements ◽  
Unburned Carbon ◽  
Efficient Operation ◽  
Triboelectrostatic Separation

Background: The fly ash produced by coal-fired power plants is an industrial waste. The environmental pollution problems caused by fly ash have been widely of public environmental concern. As a waste of recoverable resources, it can be used in the field of building materials, agricultural fertilizers, environmental materials, new materials, etc. Unburned carbon content in fly ash has an influence on the performance of resource reuse products. Therefore, it is the key to remove unburned carbon from fly ash. As a physical method, triboelectrostatic separation technology has been widely used because of obvious advantages, such as high-efficiency, simple process, high reliability, without water resources consumption and secondary pollution. Objective: The related patents of fly ash triboelectrostatic separation had been reviewed. The structural characteristics and working principle of these patents are analyzed in detail. The results can provide some meaningful references for the improvement of separation efficiency and optimal design. Methods: Based on the comparative analysis for the latest patents related to fly ash triboelectrostatic separation, the future development is presented. Results: The patents focused on the charging efficiency and separation efficiency. Studies show that remarkable improvements have been achieved for the fly ash triboelectrostatic separation. Some patents have been used in industrial production. Conclusion: According to the current technology status, the researches related to process optimization and anti-interference ability will be beneficial to overcome the influence of operating conditions and complex environment, and meet system security requirements. The intelligent control can not only ensure the process continuity and stability, but also realize the efficient operation and management automatically. Meanwhile, the researchers should pay more attention to the resource utilization of fly ash processed by triboelectrostatic separation.

scholarly journals Real-World Implementation of an ICT-Based Platform to Promote Energy Efficiency

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2416
Author(s):  
Marina Dorokhova ◽  
Fernando Ribeiro ◽  
António Barbosa ◽  
João Viana ◽  
Filipe Soares ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Real World ◽  
Energy Savings ◽  
Quality Parameters ◽  
Horizon 2020 ◽  
Flexible Devices ◽  
Information And Communication ◽  
Energy Consuming ◽  
And Behavior ◽  
And Control

The energy efficiency requirements of most energy-consuming sectors have increased recently in response to climate change. For buildings, this means targeting both facility managers and building users with the aim of identifying potential energy savings and encouraging more energy-responsible behaviors. The Information and Communication Technology (ICT) platform developed in Horizon 2020 FEEdBACk project intends to fulfill these goals by enabling the optimization of energy consumption, generation, and storage and control of flexible devices without compromising comfort levels and indoor air quality parameters. This work aims to demonstrate the real-world implementation and functionality of the ICT platform composed of Load Disaggregation, Net Load Forecast, Occupancy Forecast, Automation Manager, and Behavior Predictor applications. Particularly, the results obtained by individual applications during the test phase are presented alongside the specific metrics used to evaluate their performance.

Combining CaO–SiO2–TiO2 and CaO–SiO2–Al2O3 ternary phase systems for design of bimetallic welds

2021 ◽  
Vol 235 (8) ◽  
pp. 1271-1283
Author(s):  
Deepak Bhandari ◽  
Rahul Chhibber ◽  
Lochan Sharma ◽  
Navneet Arora ◽  
Rajeev Mehta
Keyword(s):  
Weld Metal ◽  
Power Plants ◽  
Ternary Phase ◽  
Desirability Function ◽  
Manganese Content ◽  
Research Work ◽  
Welding Process ◽  
Delta Ferrite ◽  
Electrode Coating ◽  
Phase Systems

The bimetallic welds are frequently utilized for pipeline transport system of the nuclear power plants. The occurrences of welding defects generally depend on the filler electrode as well as the electrode coatings during shielded metal arc welding process. This study involves the design of austenitic stainless steel welding electrodes for SS304L–SA516 bimetallic welds. The objective of research work includes the novel design of Al2O3–TiO2–CaO–SiO2 coatings by combining two ternary phase systems using extreme vertices mixture design methodology to analyze the effect of key coating constituents on the weld metal chemistry and mechanical properties of the welds. The significant effect of electrode coating constituent CaO on weld metal manganese content is observed which further improves the toughness of bimetallic weld joints. Various regression models have been developed for the weld responses and multi objective optimisation approach using composite desirability function has been adopted for identifying the optimized set of electrode coating compositions. The role of delta ferrite content in promoting the favourable solidification mode has been studied through microstructural examination.

Feasibility Study of a Supercritical Cycle as a Waste Heat Recovery System

2013 ◽  
Author(s):  
Antonio Agresta ◽  
Antonella Ingenito ◽  
Roberto Andriani ◽  
Fausto Gamma
Keyword(s):  
Power Systems ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Power Plants ◽  
Energy Savings ◽  
Waste Heat ◽  
Rankine Cycle ◽  
Waste Heat Recovery System ◽  
Recovery System ◽  
Organic Fluids

Following the increasing interest of aero-naval industry to design and build systems that might provide fuel and energy savings, this study wants to point out the possibility to produce an increase in the power output from the prime mover propulsion systems of aircrafts. The complexity of using steam heat recovery systems, as well as the lower expected cycle efficiencies, temperature limitations, toxicity, material compatibilities, and/or costs of organic fluids in Rankine cycle power systems, precludes their consideration as a solution to power improvement for this application in turboprop engines. The power improvement system must also comply with the space constraints inherent with onboard power plants, as well as the interest to be economical with respect to the cost of the power recovery system compared to the fuel that can be saved per flight exercise. A waste heat recovery application of the CO2 supercritical cycle will culminate in the sizing of the major components.

Investigation of the Down-Scaling Effects on the Low Swirl Burner and its Application to Microturbines

2018 ◽  
Author(s):  
Alex Frank ◽  
Peter Therkelsen ◽  
Miguel Sierra Aznar ◽  
Vi H. Rapp ◽  
Robert K. Cheng ◽  
...  
Keyword(s):  
Power Systems ◽  
Power Plants ◽  
Energy Savings ◽  
The United States ◽  
Primary Energy ◽  
Department Of Energy ◽  
Small Scale ◽  
United States Department ◽  
Scaling Effects ◽  
Swirl Burner

About 75% of the electric power generated by centralized power plants feeds the energy needs from the residential and commercial sectors. These power plants waste about 67% of primary energy as heat emitting 2 billion tons of CO2 per year in the process (∼ 38% of total US CO2 generated per year) [1]. A study conducted by the United States Department of Energy indicated that developing small-scale combined heat and power systems to serve the commercial and residential sectors could have a significant impact on both energy savings and CO2 emissions. However, systems of this scale historically suffer from low efficiencies for a variety of reasons. From a combustion perspective, at these small scales, few systems can achieve the balance between low emissions and high efficiencies due in part to the increasing sensitivity of the system to hydrodynamic and heat transfer effects. Addressing the hydrodynamic impact, the effects of downscaling on the flowfield evolution were studied on the low swirl burner (LSB) to understand if it could be adapted to systems at smaller scales. Utilizing particle image velocimetry (PIV), three different swirlers were studied ranging from 12 mm to 25.4 mm representing an output range of less than 1 kW to over 23 kW. Results have shown that the small-scale burners tested exhibited similar flowfield characteristics to their larger-scale counterparts in the non-reacting cases studied. Utilizing this data, as a proof of concept, a 14 mm diameter LSB with an output of 3.33 kW was developed for use in microturbine operating on a recuperated Brayton cycle. Emissions results from this burner proved the feasibility of the system at sufficiently lean mixtures. Furthermore, integration of the newly developed LSB into a can style combustor for a microturbine application was successfully completed and comfortably meet the stringent emissions targets. While the analysis of the non-reacting cases was successful, the reacting cases were less conclusive and further investigation is required to gain an understanding of the flowfield evolution which is the subject of future work.

Development of Sediment Brick Utilizing Reservoir Sediment and Fly Ash

2013 ◽  
Vol 420 ◽  
pp. 276-280
Author(s):  
Bashar S. Mohammed ◽  
Ean Lee Woen ◽  
M.A. Malek ◽  
Wong Leong Sing ◽  
Nor Aishah Abbas ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Power Plants ◽  
Gravitational Force ◽  
Electrical Power ◽  
Research Work ◽  
Flowing Water ◽  
Reservoir Sediment ◽  
Load Bearing ◽  
Hydroelectric Plants

Electrical companies generate electricity mainly from two major types of plant; hydroelectric plants and thermal plants. Hydroelectric is the term referring to electricity generated by hydropower; the production of electrical power through the use of the gravitational force of falling or flowing water through dams operation. The sedimentation of such dams over years will cause large capacity losses of the dams. Thermal plants generate electricity through coal-fired power plants which produce millions tons of fly ash yearly. This fly ash accumulates rapidly and causes enormous problems of disposal. Therefore, the research work presented in this paper is dealing with utilizing reservoir sediment and fly as to form brick under pressure. Sediment brick can be produced as a load bearing brick with compressive strength is greater than 7 N/mm2.

scholarly journals An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Muhammad Waqar ◽  
Hassan Dawood ◽  
Hussain Dawood ◽  
Nadeem Majeed ◽  
Ameen Banjar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Heart Attack ◽  
High Reliability ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Unequal Distribution ◽  
The Given

Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

scholarly journals A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6852
Author(s):  
Grant Buster ◽  
Paul Siratovich ◽  
Nicole Taverna ◽  
Michael Rossol ◽  
Jon Weers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
High Reliability ◽  
Geothermal Systems ◽  
Low Carbon ◽  
Modeling Framework ◽  
Operational Optimization ◽  
Modeling Software ◽  
Geothermal Power ◽  
Nominal Performance

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.

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