Computational intelligent strategies to predict energy conservation benefits in excess air controlled gas-fired systems

Abstract Gas-fired systems and boilers are the noteworthy energy consumers in industries for energy production. The mostly applied factor in systems which contain boilers is the combustion efficiency. This parameter has widely applied to determine important information about exhaust gas such as CO 2 and O 2 . This study plays importance on applying the predictive techniques based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron Artificial Neural Network (MLPANN), Support Vector Machine (SVM), and simple correlation for predicting the combustion efficiency of natural gas at extensive range of excess air fraction and stack temperature rise. The developed tools can be of great assessment for engineers dealing with combustion to have a rapid check on combustion efficiency of natural gas at broad range of applications without the requirement of any unit as pilot plant. The Levenberg–Marquardt algorithm is employed to optimize the bias and weight values of the ANN model. In addition, Hybrid algorithm (coupling of back propagation and least square methods) is used to determine parameters of membership function in the ANFIS approach. Hyper variables of the SVM technique (i.e., C , K and e ) are determined using tried and error procedures. To that end, a database including 72 data points has been collected from the energy management handbook. Approximations are recognized to be in high approbation with reported data points. Furthermore, potential of aforementioned models has been evaluated through statistical analyses. The developed models were examined using several data, and a reasonable match was attained showing a good potential for the proposed predictive tools in estimation of the combustion efficiency of natural gas at extensive range of excess air fraction and stack temperature rise.

[1]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[2]  G. B. Sahoo,et al.  Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms , 2006 .

[3]  Amin Shokrollahi,et al.  Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir , 2013, Appl. Soft Comput..

[4]  F. Gharagheizi,et al.  Determination of the speed of sound in ionic liquids using a least squares support vector machine group contribution method , 2014 .

[5]  Mohammad Ali Ahmadi,et al.  Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach , 2014 .

[6]  Abdulkadir Sengür,et al.  Modelling of a new solar air heater through least-squares support vector machines , 2009, Expert Syst. Appl..

[7]  Mohammad Ali Ahmadi,et al.  Evolving simple-to-apply models for estimating thermal conductivity of supercritical CO2 , 2017 .

[8]  Alireza Bahadori,et al.  Estimation of the water content of natural gas dried by solid calcium chloride dehydrator units , 2014 .

[9]  Dilip Kumar Pratihar,et al.  Tuning of neural networks using particle swarm optimization to model MIG welding process , 2011, Swarm Evol. Comput..

[10]  Mohammad Ali Ahmadi,et al.  Connectionist model for predicting minimum gas miscibility pressure: Application to gas injection process , 2015 .

[11]  Chien-Li Lee,et al.  Reduction of Energy Cost and CO2 Emission for the Boilers in a Full-Scale Refinery Plant by Adding Waste Hydrogen-Rich Fuel Gas , 2008 .

[12]  Hikmet Esen,et al.  Modelling and experimental performance analysis of solar-assisted ground source heat pump system , 2017, J. Exp. Theor. Artif. Intell..

[13]  Mehmet Esen,et al.  Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing , 2008 .

[14]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[15]  Wlodzimierz Blasiak,et al.  Mathematical modelling of NO emissions from high-temperature air combustion with nitrous oxide mechanism , 2005 .

[16]  Alireza Baghban,et al.  Estimating hydrogen sulfide solubility in ionic liquids using a machine learning approach , 2014 .

[17]  Alireza Baghban,et al.  Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches , 2015 .

[18]  Giuseppe Modoni,et al.  Prediction of the diameter of jet grouting columns with artificial neural networks , 2015 .

[19]  Alireza Bahadori,et al.  Predicting emissivities of combustion gases , 2009 .

[20]  David J. Hand,et al.  Intelligent Data Analysis: An Introduction , 2005 .

[21]  Amin Gholami,et al.  Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling , 2014, Korean Journal of Chemical Engineering.

[22]  Mustafa Inalli,et al.  Modeling a ground-coupled heat pump system by a support vector machine , 2008 .

[23]  M. Ahmadi,et al.  Phase Equilibrium Modeling of Clathrate Hydrates of Carbon Dioxide + 1,4-Dioxine Using Intelligent Approaches , 2015 .

[24]  Engin Özdemir.  Energy conservation opportunities with a variable speed controller in a boiler house , 2004 .

[25]  Ali Elkamel,et al.  Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization , 2013 .

[26]  Wayne Turner,et al.  Energy Management Handbook , 2020 .

[27]  Alireza Bahadori,et al.  Estimation of energy conservation benefits in excess air controlled gas-fired systems , 2010 .

[28]  Petr Stehlik,et al.  Low NOx burners--prediction of emissions concentration based on design, measurements and modelling. , 2002, Waste management.

[29]  R. Srivastava,et al.  The combustion of Orimulsion and its generation of air pollutants , 2000 .

[30]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[31]  Mustafa Inalli,et al.  Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems , 2008 .

[32]  Prasant Kumar Pattnaik,et al.  Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization , 2014, Expert Syst. Appl..

[33]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[34]  Bashar Tarawneh,et al.  Pipe pile setup: Database and prediction model using artificial neural network , 2013 .

[35]  Abdulkadir Sengür,et al.  Artificial neural network and wavelet neural network approaches for modelling of a solar air heater , 2009, Expert Syst. Appl..

[36]  Zhongya Zhang,et al.  Artificial neural networks applied to polymer composites: a review , 2003 .

[37]  Mingjun Wang,et al.  Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil , 2009 .

[38]  Alireza Baghban,et al.  Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique , 2015 .

[39]  Jiejin Cai,et al.  Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .

[40]  Fu-Hsiang Chen,et al.  Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree , 2015 .

[41]  Harry Taplin.  Combustion efficiency tables , 1991 .

[42]  M. Ahmadi.  Neural network based unified particle swarm optimization for prediction of asphaltene precipitation , 2012 .

[43]  Bruce G. Miller,et al.  Adsorption separation of carbon dioxide from flue gas of natural gas-fired boiler by a novel nanoporous “molecular basket” adsorbent , 2005 .

[44]  V. I. Kuprianov,et al.  Minimizing fuel and environmental costs for a variable-load power plant (co-)firing fuel oil and natural gas. Part 1. Modeling of gaseous emissions from boiler units , 2006 .

[45]  Betul Bektas Ekici,et al.  A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems , 2014 .

[46]  Mustafa Inalli,et al.  Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system , 2008 .

[47]  Mohammad Ali Ahmadi,et al.  Evolving smart approach for determination dew point pressure through condensate gas reservoirs , 2014 .

[48]  F. Barreras,et al.  Behavior of a high-capacity steam boiler using heavy fuel oil Part II: Cold-end corrosion , 2004 .

[49]  Danial Jahed Armaghani,et al.  Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks , 2015 .

[50]  Qingyan Fang,et al.  Numerical simulations of the slagging characteristics in a down-fired, pulverized-coal boiler furnace , 2010 .

[51]  Christos Schizas,et al.  Artificial Neural Network Learning: A Comparative Review , 2002, SETN.

[52]  Davide Anguita,et al.  Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[53]  Ali Abbas,et al.  Estimation of air dew point temperature using computational intelligence schemes , 2016 .

[54]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[55]  Mustafa Inalli,et al.  Predicting performance of a ground-source heat pump system using fuzzy weighted pre-processing-based ANFIS , 2008 .

[56]  Tian Haijun,et al.  Modeling of Power Plant Superheated Steam Temperature Based on Least Squares Support Vector Machines , 2012 .

[57]  Behzad Pouladi,et al.  Connectionist technique estimates H2S solubility in ionic liquids through a low parameter approach , 2015 .

[58]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[59]  Mustafa Inalli,et al.  Performance prediction of a ground-coupled heat pump system using artificial neural networks , 2008, Expert Syst. Appl..

[60]  Siegfried Gottwald,et al.  Fuzzy Sets and Fuzzy Logic , 1993 .

[61]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[62]  Dimitar P. Filev,et al.  Fuzzy SETS AND FUZZY LOGIC , 1996 .

[63]  Alireza Bahadori,et al.  Estimation of performance of steam turbines using a simple predictive tool , 2010 .