MARC details
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02407nam a2200277Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250625140640.0 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
B-10884 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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250602s9999 xx |||||s2 |||| ||und|d |
| 245 10 - TITLE STATEMENT |
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| Title |
Operational optimization and real-time control of fuel-cell systems |
| 490 0# - SERIES STATEMENT |
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| Volume/sequential designation |
Journal of Power Sources, 193(1), p.258-268, 2009 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Fuel cells is a rapidly evolving technology with applications in many industries including transportation, and both portable and stationary power generation. The viability, efficiency and robustness of fuel-cell systems depend strongly on optimization and control of their operation. This paper presents the development of an integrated optimization and control tool for Proton Exchange Membrane Fuel-Cell (PEMFC)systems. Using a detailed simulation model, a database is generated first, which contains steady-state values of the manipulated and controlled variables over the full operational range of the fuel-cell system. In a second step, the database is utilized for producing Radial Basis Function (RBF)neural network "meta-models". In the third step, a Non-Linear Programming Problem (NLP)is formulated, that takes into account the constraints and limitations of the system and minimizes the consumption of hydrogen, for a given value of power demand. Based on the formulation and solution of the NLP problem, a look-up table is developed, containing the optimal values of the system variables for any possible value of power demand. In the last step, a Model Predictive Control (MPC)methodology is designed, for the optimal control of the system response to successive sep-point changes of power demand. The efficiency of the produced MPC system is illustrated through a number of simulations, which showthat a successful dynamic closed-loop behaviour can be achieved, while at the same time the consumption of hydrogen is minimized. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
FUEL CELLS |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
HYDROGEN |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
MODEL PREDICTIVE CONTROL |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
OPTIMIZATION |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
META-MODELING |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
NEURAL NETWORKS |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Hasikos, J. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Sarimveis, H. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Zervas, P.L. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Markatos, N.C. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://drive.google.com/file/d/1Ja4Y-jp3mt_vnX95E1oywpAXuF8bQE_T/view?usp=drivesdk">https://drive.google.com/file/d/1Ja4Y-jp3mt_vnX95E1oywpAXuF8bQE_T/view?usp=drivesdk</a> |
| Public note |
Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Clasificación local |
| Koha item type |
Documentos solicitados |
