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Development and validation of a robust integrated thermal power plant model for load loss analysis and identification

Alton Marx, Pieter Rousseau, Ryno Laubscher, S. Skatulla
2021 MATEC Web of Conferences  
This provides the basis for generating model-based performance data that can be combined with real plant data to facilitate the development of deep learning analytics tools for load loss fault diagnosis  ...  The development of deep learning methodologies for the analysis of thermal power plant load losses requires a combination of real plant data and data derived from fundamental physics-based process models  ...  Low-and high-pressure feedwater heaters The modelled power plant has six FWH stages.  ... 
doi:10.1051/matecconf/202134700011 fatcat:h53xnsomwndpvnpa7gay22pkp4

Steam power plant configuration, design, and control

Xiao Wu, Jiong Shen, Yiguo Li, Kwang Y. Lee
2015 Wiley Interdisciplinary Reviews: Energy and Environment  
These new technologies are collected from both the academic studies and industrial practices, which can improve the performance of the FFPP control system for more economic and safe plant operation.  ...  Closed-loop data-driven modeling. Modeling is the first and foremost important step in advanced controller design.  ...  Besides the traditional model based controller, the neural network technique can also be used to design the model-free controllers directly, which is called the neural network inverse control (NNIC).  ... 
doi:10.1002/wene.161 fatcat:56vsu6oad5dshfqj2errb52lvu

Infinity Sensor: Temperature Sensing in GaN Power Devices using Peak di/dt

Jianjing Wang, Mohammad H. Hedayati, Dawei Liu, Salah-Eddine Adami, Harry C. P. Dymond, Jeremy J. O. Dalton, Bernard H. Stark
2018 2018 IEEE Energy Conversion Congress and Exposition (ECCE)  
and retrofits, possible methodology development for evaluation and synthesis, and the importance of good modeling practice.  ...  Commonly-used working fluids for Rankine cycle are mainly water/steam for large-scale applications and high-temperature heat source, and various organic fluids for small-scale applications and intermediate  ...  For the steam network implemented in OSMOSE, the superstructure of steam turbine network is generated based on the predefined pressure levels at the upper level: For any pressure level i, a steam turbine  ... 
doi:10.1109/ecce.2018.8558287 fatcat:6dtqnvu3tjef5nih4jqtztot34

A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants

Ligang Wang, Zhiping Yang, Shivom Sharma, Alberto Mian, Tzu-En Lin, George Tsatsaronis, François Maréchal, Yongping Yang
2018 Energies  
and retrofits, possible methodology development for evaluation and synthesis, and the importance of good modeling practice.  ...  The theoretical basis of the most commonly-used multi-objective techniques and recent developments are given to offer high-quality Pareto front for decision makers, with an emphasis on evolutionary algorithms  ...  For the steam network implemented in OSMOSE, the superstructure of steam turbine network is generated based on the predefined pressure levels at the upper level: For any pressure level i, a steam turbine  ... 
doi:10.3390/en12010073 fatcat:br25krgnzrb4jcvuzcjkiruq6e

Digital Twin-Driven Machine Condition Monitoring: A Literature Review

He Liu, Min Xia, Darren Williams, Jianzhong Sun, Hongsheng Yan, Xueliang Xiao
2022 Journal of Sensors  
Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework  ...  The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift.  ...  They used virtual sensors based on DT to construct performance prediction models for a feedwater heater.  ... 
doi:10.1155/2022/6129995 fatcat:lug3c7dyynacjlor34j24bhgz4

Machine-Learning Methods in Prognosis of Ageing Phenomena in Nuclear Power Plant Components

Miki Sirola, John Einar Hulsund
2021 International Scientific Journal of Computing  
Prognosis models for predicting possible developing ageing features in nuclear power plant data utilizing machine learning methods or closely related methods are demonstrated.  ...  K-means clustering is used in cluster analysis of nuclear power plant data. A method for detecting trends in selected clusters is developed. Prognosis models are developed and tested.  ...  Self-organizing map (SOM) and back propagation neural network methods are introduced for residual life predictions for ball bearings in [27] .  ... 
doi:10.47839/ijc.20.1.2086 fatcat:52agqoxkhvh3jcliookzyutmmq

Application of Computational Intelligence to Energy Systems

Matteo De Felice
2011 Zenodo  
Neural Networks Ensembles The term 'ensemble' describes a group of learning machines that work together on the same task, in the case of neural networks they are trained on the same data, run together  ...  In learning and modelization problems, given that CI methods are generally data-driven, the main shortcoming is that to obtain a good model the data provided has to be a good representation of the problem  ...  NEURAL NETWORKS  ... 
doi:10.5281/zenodo.4068383 fatcat:ee6uyhkcdzh3hlvty33twlqnva

Applications of machine learning methods for engineering risk assessment – A review

Jeevith Hegde, Børge Rokseth
2020 Safety Science  
Artificial neural networks are the most applied machine learning method to aid in engineering risk assessment.  ...  The findings show that the automotive industry is leading the adoption of machine learning algorithms for risk assessment.  ...  The second author is funded by the project Online risk management and risk control for autonomous ships (ORCAS).  ... 
doi:10.1016/j.ssci.2019.09.015 fatcat:d23ilhztlbfapahbwybk34ev3e

Trace: Tennessee Research and Creative Exchange Inverse Dynamics and Control for Nuclear Power Plants

Riza Berkan, John Bailey, Robert Uhrig, Lefteri Tsoukalas
1991 unpublished
Figure 5 .3 shows a functionally symmetric, coupled neural networks as a model for motor-control.  ...  The plant is driven by three different control paradigms (RID, Fuzzy Logic, and Neural Network (42] ) in three separate simulations.  ...  Parameters FLA.Gxx control the simulation (1 for discrepancy, 0 fo r perfect match) corresponding to the 8 parameters of the model.  ... 
fatcat:jujasny4pndsnegnwpj35cbjqu

Mechanical and electrical characterization of CVD-grown graphene transferred on chalcogenide Ge 2 Sb 2 Te 5 layers

G. D'Arrigo, M. Christian, V. Morandi, G. Favaro, C. Bongiorno, A.M. Mio, M. Russo, A. Sitta, M. Calabretta, A. Sciuto, E. Piccinini, L. D'Urso (+1 others)
2018 Carbon  
At excitation densities relevant for lasing we observe breakdown of the rate-equation model indicating a build-up of a highly correlated regime of the photo-carrier population that suppresses the non-radiative  ...  To increase fluid mixing, complex three-dimensional networks inducing chaotic advection have to be designed.  ...  Acknowledgments The authors acknowledge the financial support of MIUR for the project "High Efficiency Technologies for On-board Environmental and Sustainable Energy use (TESEO)", in the framework of National  ... 
doi:10.1016/j.carbon.2018.02.046 fatcat:wiqrs5krczgrrahoa7hl2rgtcm

Thermo-economic evaluation, optimization and synthesis of large-scale coal-fired power plants [article]

Ligang Wang, Technische Universität Berlin, Technische Universität Berlin, George Tsatsaronis, André Bardow, Yongping Yang
2016
Future coal-fired power plants will be with high temperature and pressure levels, multiple heat sources, multiple products, and many available technologies integrated to efficiently utilize different-grade  ...  Only nearly 10% of the costs of the whole system could be avoided for modern industrial designs at present. In addition, movin [...]  ...  Suresh et al. (2011) and Hajabdollahi et al. (2012) coupled genetic algorithm and artificial neural network to determine the maximum design or operation efficiency.  ... 
doi:10.14279/depositonce-5451 fatcat:d2hktqf6snhohem3pi2vwilvju