A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

被引:21
作者
Rossi, Francesco [1 ]
Velazquez, David [1 ]
机构
[1] Univ Seville, Dept Energy Engn, Seville 41092, Spain
关键词
Energy savings; Industry; Cogeneration; Baseline; Modelling; Verification; ARTIFICIAL NEURAL-NETWORKS; GAS-TURBINE; EMISSION CHARACTERISTICS; NOX EMISSIONS; FIRED BOILER; NATURAL-GAS; PERFORMANCE; PREDICTION; OPTIMIZATION; MODEL;
D O I
10.1016/j.energy.2015.06.016
中图分类号
O414.1 [热力学];
学科分类号
摘要
A methodology is proposed to assess the savings achieved from the implementation of ECMs (energy conservation measures) in industrial plants with application for a combined cycle cogeneration plant. The analysis begins with the study of the operation of the system and a qualitative assessment of the changes resulting from the ECMs in the plant. The control volume is subsequently selected with the objective of minimising and focusing the efforts exclusively on the areas of the system actually influenced by the ECMs. An ANN (artificial neural networks) approach is proposed for the modelling stage and it is used to mimic the production and consumption of the system during the post-retrofit period in its configuration prior to the changes. Special attention is given to the selection of the variables used as predictors in the developed models, as well as to the determination of the relative influence of the inputs on the final models. Finally, the savings are calculated, and a critical analysis of the results is presented based on the comparison with the predictions obtained from the preliminary qualitative assessment. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:528 / 544
页数:17
相关论文
共 92 条
[1]   Application of neural network for the modeling and control of evaporative condenser cooling load [J].
Abbassi, A ;
Bahar, L .
APPLIED THERMAL ENGINEERING, 2005, 25 (17-18) :3176-3186
[2]  
[Anonymous], 2005, 16 IFAC WORLD C PRAG
[3]  
[Anonymous], 2001, FUEL, DOI DOI 10.1016/S0016-2361(01)00104-1
[4]  
Arriagada J, 2003, P INT GAS TURB C IGT
[5]   ANN based optimization of supercritical ORC-Binary geothermal power plant: Simav case study [J].
Arslan, Oguz ;
Yetik, Ozge .
APPLIED THERMAL ENGINEERING, 2011, 31 (17-18) :3922-3928
[6]   Artificial Neural Network-Based System Identification for a Single-Shaft Gas Turbine [J].
Asgari, Hamid ;
Chen, XiaoQi ;
Menhaj, Mohammad B. ;
Sainudiin, Raazesh .
JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME, 2013, 135 (09)
[7]  
Azid IA, 2000, TENCON IEEE REGION, pB512
[8]   Power system voltage stability monitoring using artificial neural networks with a reduced set of inputs [J].
Bahmanyar, A. R. ;
Karami, A. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 58 :246-256
[9]   Neural network approach for a combined performance and mechanical health monitoring of a gas turbine engine [J].
Barad, Sanjay G. ;
Ramaiah, P. V. ;
Giridhar, R. K. ;
Krishnaiah, G. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2012, 27 :729-742
[10]   Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks [J].
Bekat, Tugce ;
Erdogan, Muharrem ;
Inal, Fikret ;
Genc, Ayten .
ENERGY, 2012, 45 (01) :882-887