Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling

This paper compares the vapour ejector and electric vacuum pump power consumptions with machine learning algorithms by using real process data and presents some novelty guideline for the selection of an appropriate condenser vacuum pump system of a steam turbine power plant. The machine learning algorithms are made by using the supervised machine learning methods such as artificial neural network model and local linear neuro-fuzzy models. The proposed non-linear models are designed by using a wide range of real process operation data sets from the CHP system in the thermal power plant. The novelty guideline for the selection of an appropriate condenser vacuum pumps system is expressed in the comparative analysis of the energy consumption and use of specific energy capable of work. Furthermore, the novelty is expressed in the economic efficiency analysis of the investment taking into consideration the operating costs of the vacuum pump systems and may serve as basic guidelines for the selection of an appropriate condenser vacuum pump system of a steam turbine.

[1]  Mohamed I. Mosaad,et al.  LFC based adaptive PID controller using ANN and ANFIS techniques , 2014 .

[2]  Zuchao Zhu,et al.  Design and Experimental Analyses of Small-flow High-head centrifugal-vortex Pump for Gas-Liquid Two-phase Mixture , 2008 .

[3]  Simona Onori,et al.  Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt , 2015 .

[4]  Zita Vale,et al.  Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets , 2013 .

[5]  Sungmoon Jung,et al.  Weighted error functions in artificial neural networks for improved wind energy potential estimation , 2013 .

[6]  Seppo Junnila,et al.  Assessment of financial potential of real estate energy efficiency investments–A discounted cash flow approach , 2015 .

[7]  Weixiong Chen,et al.  Experimental and numerical analysis of supersonic air ejector , 2014 .

[8]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[9]  Jurij Avsec,et al.  Artificial neural networking and fuzzy logic exergy controlling model of combined heat and power system in thermal power plant , 2015 .

[10]  Duncan A. Mellichamp.  New discounted cash flow method: Estimating plant profitability at the conceptual design level while compensating for business risk/uncertainty , 2013, Comput. Chem. Eng..

[11]  Jurij Avsec,et al.  Artificial neural networking model of energy and exergy district heating mony flows , 2015 .

[12]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[13]  Hongbin Ma,et al.  Experimental Investigation of Steam Ejector System With an Extra Low Generating Temperature , 2016 .

[14]  Abdelghani Harrag,et al.  Variable step size modified P&O MPPT algorithm using GA-based hybrid offline/online PID controller , 2015 .

[15]  Nicolas Lachiche,et al.  Flexible propositionalization of continuous attributes in relational data mining , 2015, Expert Syst. Appl..

[16]  Mohsen Assadi,et al.  Development of artificial neural network model for a coal-fired boiler using real plant data , 2009 .

[17]  T. Giegerich,et al.  The KALPUREX-Process - A new vacuum pumping process for exhaust gases in fusion power plants , 2014 .

[18]  Jianlin Yu,et al.  Energy and exergy analysis of a new ejector enhanced auto-cascade refrigeration cycle , 2015 .

[19]  George A. Aggidis,et al.  Regenerative liquid ring pumps review and advances on design and performance , 2016 .

[20]  A. S. Hanafi,et al.  1-D Mathematical Modeling and CFD Investigation on Supersonic Steam Ejector in MED-TVC , 2015 .

[21]  Zhenjun Ma,et al.  Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm , 2011 .

[22]  Rok Lacko,et al.  Stand-alone renewable combined heat and power system with hydrogen technologies for household application , 2014 .

[23]  Yongxue Zhang,et al.  Visualization study of gas–liquid two-phase flow patterns inside a three-stage rotodynamic multiphase pump , 2016 .

[24]  Swagat Pati,et al.  Teaching-learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system , 2015, Appl. Soft Comput..

[25]  E. Entchev,et al.  Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique , 2014 .

[26]  Adriano Milazzo,et al.  Modelling of ejector chillers with steam and other working fluids , 2015 .

[27]  Helon Vicente Hultmann Ayala,et al.  Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks , 2016 .

[28]  David R. Sinclair,et al.  Discounted cash flow of anesthesia information management systems. , 2012, Journal of clinical anesthesia.

[29]  Seyed Taghi Akhavan Niaki,et al.  Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: Two calibrated meta-heuristic algorithms , 2013 .

[30]  Andrea Bianco,et al.  Differential energy saving algorithms in a distributed router architecture , 2014, Comput. Commun..

[31]  Jyh-Cheng Jeng,et al.  A model-free direct synthesis method for PI/PID controller design based on disturbance rejection , 2015 .

[32]  Santi Agatino Rizzo,et al.  ANN based MPPT method for rapidly variable shading conditions , 2015 .

[33]  Roberto Revetria,et al.  New steam generation system for lead-cooled fast reactors, based on steam re-circulation through ejector , 2015 .

[34]  Jianlin Yu,et al.  Thermodynamic analyses on an ejector enhanced CO2 transcritical heat pump cycle with vapor-injection , 2015 .

[35]  V. Krishnan,et al.  Application of data mining tools for classification of protein structural class from residue based averaged NMR chemical shifts. , 2015, Biochimica et biophysica acta.

[36]  Mohammad Nazri Mohd. Jaafar,et al.  Optimization and the effect of steam turbine outlet quality on the output power of a combined cycle power plant , 2015 .

[37]  Deshun Liu,et al.  Research on power coefficient of wind turbines based on SCADA data , 2016 .

[38]  Mohsen Assadi,et al.  Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas , 2014 .

[39]  Wenjian Cai,et al.  Shock circle model for ejector performance evaluation , 2007 .

[40]  Siju George,et al.  Design and performance of main vacuum pumping system of SST-1 Tokamak , 2014 .

[41]  Borut Zupančič,et al.  THE IMPACT OF A TEMPUS PROJECT ON ACTIVE LEARNING IN AUTOMATIC CONTROL , 1995 .

[42]  Michael Stadler,et al.  Modeling of non-linear CHP efficiency curves in distributed energy systems , 2015 .

[43]  Marjan Golob.  Decomposed fuzzy proportional-integral-derivative controllers , 2001, Appl. Soft Comput..

[44]  Jianyong Chen,et al.  Conventional and advanced exergy analysis of an ejector refrigeration system , 2015 .

[45]  Bin-Juine Huang,et al.  Investigation of an experimental ejector refrigeration machine operating with refrigerant R245fa at design and off-design working conditions. Part 1. Theoretical analysis , 2015 .

[46]  Taraneh Sowlati,et al.  A mixed integer non-linear programming model for tactical value chain optimization of a wood biomass power plant , 2013 .

[47]  Hans Vogelesang,et al.  An introduction to energy consumption in pumps , 2008 .

[48]  Adnan Sözen,et al.  Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach , 2007 .

[49]  Hongwen He,et al.  A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles , 2014 .

[50]  Michael Dennis,et al.  A prescription for primary nozzle diameters for solar driven ejectors , 2015 .

[51]  Seyed Farid Ghaderi,et al.  Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms , 2012 .

[52]  Felix Ziegler,et al.  Feasibility analysis of an exhaust gas waste heat driven jet-ejector cooling system for charge air cooling of turbocharged gasoline engines , 2015 .

[53]  Yan Li,et al.  A technology review on recovering waste heat from the condensers of large turbine units in China , 2016 .

[54]  Sumit Roy,et al.  Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network , 2014 .

[55]  Chayan Chakrabarti,et al.  Artificial conversations for customer service chatter bots: Architecture, algorithms, and evaluation metrics , 2015, Expert Syst. Appl..