Wykorzystanie techniki sieci neuronowych w zagadnieniach spalania paliw stałych

• Autorzy: Kakietek S.

Warianty tytułu

EN

Utilizing the neural networks for the solid fuel combustion

• Konferencja: ENERGETYKA 2004 / International Scientific and Technical Conference [3; 2004; Wrocław, Poland]
• Języki publikacji: PL

Abstrakty

PL

W pracy przedstawiono możliwości wykorzystania techniki sieci neuronowej w zagadnieniach spalania paliw stałych. Główną uwagę poświecono kontroli i optymalizacji procesu spalania, żużlowaniu oraz popieleniu węgli, a także szybkości wypalenia koksu. Przedstawiono także w skrócie własne doświadczenia autora w zastosowaniu sieci neuronowych do charakteryzowania paliw stałych.

EN

In this work, a short review of the possibilities of using neural networks in combustion of solid fuels is presented 'The consideration was mainly focused on few exemplars of control and optimisation of combustion, slagging and ash deposition, and char combustion rate as well. There are shown results of using neural networks to reduce NO.S emissions up to 60% with improving heat rate up to 2% overall and reducing the un-burncd carbon in ash up to 30% in utility boilers. Also neural model for identification and control the NOx emission for given set of work parameters of the boiler was presented. The monitoring of near coal pulverized burner slag deposition with a hybrid neural network system, attempts of utilizing the neural networks for prediction coal ash fusion temperature and chars combustion rate were finished with succeed as well. There is also shown the author's experience in utilizing the neural networks for solid fuel characterisation. A combined SOFM and MLP neural network was created to classify and predict solid fuel's behaviour during its combustion based on experiments in 10-20kW test stand (Plug Flow Reactor). Five different coals and coal blends were used to train the neural network. After training the neural model was tested using sixth fuel. The model with good accuracy predicted the combustion behaviour of tested fuel and classified it into proper group.

Słowa kluczowe

PL

spalanie paliw stałych; sieć neuronowa

EN

solid fuel combustion; neural network

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems : journal of transdisciplinary systems science
• Rocznik: 2004
• Tom: Vol. 9, Sp. 2/1
• Strony: 461--468
• Opis fizyczny: Bibliogr. 29 poz.

Twórcy

autor

Kakietek S.

• Instytut Energetyki, Zakład Procesów Cieplnych, Warszawa

Bibliografia

• [1] SOTERIS A.KALOGIROU, Artificial intelligence for the modeling and control of combustion processes: a review, Progress in Energy and Combustion Science 2003, 29, 515.
• [2] REIFMAN J., Feldman E.E., Identification and control of NOx emissions using neural networks, J. Air & Waste Manage. Assoc., 1998, 48, 408-417,
• [3] WANG F. i inni, The research on the estimation for the NOx emissive concentration of the pulverized coal boiler by the flame image processing technique, Fuel 2002, 81, 2113-2120.
• [4] ZHOU H. i inni, Modeling and optimisation of the NOx emission characteristics of the tangentially fired boiler with artificial neural networks, Energy 2004, 29, 167-183.
• [5] WARD J. i inni, Simulation of a range of thermal systems by artificial neural networks, Proceedings of: 2001 ASME Intern. Mech. Engineer. Congress & Exposition 2001, 369 (5), 3-10
• [6] CHONG A.Z.S. i inni, Prediction of gaseous emissions from a chain grate stoker boiler using neural networks of ARX structure, IEE Proc.-Sci. Meas. Technol. 2001,3, 148.
• [7] DONG CH. i inni, Tests on co-firing of municipal solid waste and coal in a ciculating fluidized bed, Energy Conversion & Management 2002,43, 2189-2199.
• [8] YIN CH. i inni, Predicting coal ash fusion temperature with a back-propagation neural network model, Fuel 1998, 77 (15), 1777-1782.
• [9] SALEFHAR H., Benson S. A., Neural network based power plant coal quality analysis, IEEE North American Power Symposium, 1997
• [10] SALEFHAR H., Benson S.A., Electric utility coal quality analysis using artificial neural network techniques, Neurocomputingl998, 23, 195-206.
• [11] QUAZZANE A.K., Design of an opt. instr. to measure the carbon content of fly ash, Fuel 2002, 81.
• [12] BOCCALETTI C. i inni, An application of neural network in combustion processes evaluations, EPMESC-7th Intern. Conf. 1999, 7 (2), 787-796.
• [13] Li N., Thompson S., Power plant air/fuel ratio optimisation, Trans Inst. MC 1998, 20 (4).
• [14] LEPPAKOSKI K., i inni Integrated optimisation and control system to reduce flue gas emissions, IFAC Power Plants and Power Systems Control 2000,113-118.
• [15] BOOTH R.C., RONALD W.B., Neural network-based combustion reduces NOx emissions while improving performance, Proceedings of the American Power Conference 1998, 60 (2), 667-672.
• [16] JI-ZHENG CHU i inni, Constrained optimization of combustion in a simulated coal-fired boiler using ANN model and information analysis, Fuel 2003, 85, 693-703.
• [17] ZHU Q. i inni, The predictions of coal/char combustion rate using ANN approach, Fuel 1999, 78
• [18] BALIJEPALLI S.N. i inni, Estimation of coal conversion by supercritical solvents using the method of neural nets, Fuel 1997, 76 (2), 165-168.
• [19] HEPING P. I GUOQIANG L., Applying Back Propagation Artificial Neural Networks to predict coal quality parameters and coalbed gas content. Intern. Mining and Minerals 1998, 1 (2), 45-49
• [20] ABBAS T. i inni, An AI treatment of devolatilization for pulverized coal and biomass in co-fired flames, Combustion and Flames 2003, 132, 305-318.
• [21] YIN CH. i inni, Methods to improve prediction performance of ANN models, Simulation Modeling Practice and Theoiy 2003, 11,211 -222.
• [22] DONG CH. i inni, Predicting the heating value of MSW with a feed forward neural network, Waste Management 2003, 23, 103-106.
• [23] TAN C.K. i inni, Monitoring near burner slag deposition with a hybrid neural network system, Meas. Sci. Technol. 2003, 14, 1-9
• [24] CHANG N.B., Chen W.C., Prediction of PCDDs/PCDFs emissions from municipal incinerators by genetic programming and neural network modelling, Waste Menag. Res. 2000, 18 (4), 341-351.
• [25] ZHOU HAO i inni, Combining neural network and genetic algorithms to optimise low NOx pulverized coal combustion, Fuel 2001, 80,2163-2169.
• [26] ZHOU HAO i inni, Optimizing pulverized coal combustion performance based on ANN and GA, Fuel Processing Technology 2003, 85, 113-124.
• [27] CHEN W.M., Jiang N., Clean Coal Technology, 1996,2 (2), 34.
• [28] POLIFKE W. i inni, Optimization of rate coefficients for simplified reaction mechanism with genetic algorithms, Combustion and Flame 1998, 113, 119-135.
• [29] BLASCO J.A. i Inni, Modelling the temporal evolution of a reduced combustion chemical system with ANN, Combustion and Flame 1998, 113,35-52.