Neural management for heat and power cogeneration plants

Engineering Applications of Artificial Intelligence

Volumn 19, Issue 7, 2006, Pages 721-730

  Cerri, Giovanni ^a   Borghetti, Sandra ^a   Salvini, Coriolano ^a  

^a ROMA TRE UNIVERSITY   (Italy)

Author keywords

CHP; Diagnosis; Neural networks; Optimisation; Plant models

Indexed keywords

COMPUTER AIDED DIAGNOSIS; DATABASE SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS; NEURAL NETWORKS;

COGENERATION HEAT AND POWER (CHP) PLANTS; IDENTIFICATION NEURAL MODEL (INM); OPTIMUM LOAD ALLOCATION NEURAL MODEL (OLANM); PLANT MODELS;

POWER PLANTS;

EID: 33749234932     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2006.05.013     Document Type: Article

References (17)

1. Al-Shayji K.A., Al-Wadyei S., and Elkamel A. Modeling and optimization of a multistage flash desalination process. Engineering Optimization 37 6 (2005) 591-607
2. Boccaletti, C., Cerri, G., Khatri, D.S., Seyedan, B., 1999. An application of neural network in combustion processes evaluations. Proceedings of International Conference on Enhancement and Promotion of Computational Methods in Engineering and Science (EPMESC VII), Macao.
3. Boccaletti, C., Cerri, G., Salvini, C., 2000. Algoritmi deterministici ed evolutivi naturali nell'ottimizzazione della gestione di impianti cogenerativi. 55° Congresso ATI, Matera.
4. Boccaletti C., Cerri G., and Seyedan B. A neural network simulation of a gas turbine with a waste heat recovery section. Journal of Engineering for Gas Turbines and Power 123 (2001) 371-376
5. Cerri G. Un metodo generale per la ottimizzazione di sistemi termodinamici. L'ENERGIA ELETTRICA, LXVIII 12 (1991) 431-438
6. Cerri, G., 1996. A simultaneous solution method based on a modular approach for power plant analyses and optimized designs and operations. ASME paper 96-GT-302, International Gas Turbine and Aeroengine Congress and Exhibition, Birmingham, UK.
7. Cerri, G., Monacchia, S., Seyedan, B., 1999. Optimum load allocation in cogeneration gas-steam combined plants. ASME paper 99-GT-6, ASME TURBO EXPO land, sea and air. The 44th ASME Gas Turbine and Aeroengine Technical Congress, Exposition and User Symposium, Indianapolis, Indiana, USA.
8. Cerri, G., Evans, R., Frangopoulos, C.A., Parrella, M., Pitt, R.U., Psychogios, J., Salvador, S., Sepielli, M., Seyedan, B., 2000. Optimum management system with environmental monitoring. ASME paper 2000-GT-310, International Gas Turbine and Aeroengine Congress and Exhibition, Munich, D.
9. Cerri G., Michelassi V., Monacchia S., and Pica S. Kinetic combustion neural modelling integrated into CFD. Journal of Power and Energy I. Mechanical Engineers A2 217 (2003) 185-192
10. Cerri, G., Borghetti, S., Salvini, C., 2005. Inverse methodologies for actual status recognition of gas turbine components. ASME Paper PWR2005-50033, ASME Power, Chicago, IL, USA.
11. Cerri, G., Borghetti, S., Salvini, C., 2006. Models for simulation and diagnosis of energy plant components. ASME Paper PWR2006-88146, ASME Power, Atlanta, GA, USA.
12. DePold H., and Glass F.D. The application of expert system and neural networks to gas turbine prognostics and diagnostics. Journal of Engineering for Gas Turbine and Power 121 (1999) 607-612
13. Diakunchak I.S. Performance deterioration in industrial gas turbines. Journal of Engineering for Gas Turbines and Power 114 (1992) 161-168
14. Elkamel A. An artificial neural network for predicting and optimizing immiscible flood performance in heterogeneous reservoirs. Computers and Chemical Engineering 22 11 (1998) 1699-1709
15. Kobayashi, T., Simon, D.L., 2001. A hybrid neural network genetic algorithm technique for aircraft engine performance diagnostics. Thirty seventh AIAA/ASME/SAE/ASEE Joint Propulsion Conference.
16. Mathioudakis, K., Stamatis, A., Bonataki, E., 1999. Diagnosing the sources of overall performance deterioration in CCGT. 99-GT-364, 44th ASME International Gas Turbine and Aeroengine Congress and Exposition, Indianapolis, Indiana, USA.
17. Pham D.T., and Oh S.J. Identification of plant inverse dynamics using neural networks. Artificial Intelligence in Engineering 13 (1999) 309-320