Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden

De, S. K.; Kaiadi, Mehrzad; Fast, Magnus; Assadi, Mohsen

doi:10.1016/j.energy.2007.04.008

Cited by 83 publications

(36 citation statements)

References 13 publications

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“…It was known from previous experience [29] that mass flow rate of fuel, pressure and temperature of feed water were expected to influence the prediction of main steam properties. So, they were included as input parameters in boiler model.…”

Section: Selection Of Output and Input Parametersmentioning

confidence: 99%

“…In order to select suitable input parameters, all available parameters, for which data were available from the plant, were examined. It was decided based on system knowledge of the authors and their previous experiences with ANN modeling [29] that parameters of the main steam (m s , p s , t s ), condenser pressure (p CN ) and those related to bleeds from the turbine would be included in the initial set of input parameters. The final set of input parameters for turbine model emerged from this set through two-stage sensitivity analysis.…”

Section: Selection Of Output and Input Parametersmentioning

confidence: 99%

“…ANN is one of these AI-based tools that could be used for the modeling of energy systems for which operational data (either real plant or simulated) are available. The development of ANN models for the steam process of a biomass and coal co-fired combined heat and power plant using real plant data has already been reported by the same group [29,30]. However, there are a few reported works [31][32][33][34][35] on the modeling of conventional coal-fired boilers using ANN, and only one of them [35] used real plant data.…”

mentioning

confidence: 92%

“…This work is one of the series of studies [29,30,[35][36][37]] made on ANN modeling of coal-based power and CHP plants. These studies explored the aspects of generalization of ANN modeling of such plants, if any, and established a road map for such studies in future.…”

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confidence: 99%

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Prediction of power output of a coal-fired power plant by artificial neural network

Smrekar

Pandit

Fast

et al. 2009

Neural Comput & Applic

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Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network (ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers of parameters compared to mathematical ones.Step-by-step method of the ANN model development of a coal-fired power plant for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant. The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and observed results are reported.

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Section: Selection Of Output and Input Parametersmentioning

confidence: 99%

Section: Selection Of Output and Input Parametersmentioning

confidence: 99%

mentioning

confidence: 92%

mentioning

confidence: 99%

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Prediction of power output of a coal-fired power plant by artificial neural network

Smrekar

Pandit

Fast

et al. 2009

Neural Comput & Applic

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“…Teruel et al [8] have investigated ash deposits in coal-fired boilers, and developed an ANN model for the fouling and the cleaning in the furnace. Other than environmental emissions, there are also applications of ANN modeling in the literature, for estimation of combustion rate of coal [9], optimization of the operating conditions of pulverized coal combustion [10], monitoring of combined heat and power plants [11,12], prediction of hardgrove grindability index [13], gross calorific value [14], and coal rank parameters [15].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks

Bekat

Erdogan²,

İnal³

et al. 2012

Energy

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a b s t r a c tThe amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R 2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned.

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Performance prediction of a RPF-fired boiler using artificial neural networks

Behera

Rene

Kim³

et al. 2013

Int. J. Energy Res.

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In order to provide adequate engineering assistance and to improve the energy efficiency in process industries, it is crucial to evaluate the operational performance of a boiler in terms of its practical requirements, viz. temperature, pressure, and mass flow rate of steam. This study was aimed at assessing and optimizing the performance of a refuse plastic fuel-fired boiler using artificial neural networks. A feed-forward back propagation neural network model was developed and trained using existing plant data (5 months), to predict temperature, pressure, and mass flow rate of steam, using the following input parameters: feed water pressure, feed water temperature, conveyor speed, and incinerator exit temperature. The predictive capability of the model was evaluated in terms of mean absolute percentage error between the model fitted and actual plant data, while sensitivity analysis was performed on the input parameters by determining the absolute average sensitivity values. The higher absolute average sensitivity value of the incinerator exit temperature in comparison to that of feed water pressure, feed water temperature and conveyor speed suggested that the change of incineration exit temperature has a significant influence on the selected outputs (steam properties). Overall, the good results observed from this work demonstrate the fact that artificial neural networks can efficiently predict the data on steam properties and could serve as a good tool to monitor boiler behavior under real-time conditions.

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Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden

Cited by 83 publications

References 13 publications

Prediction of power output of a coal-fired power plant by artificial neural network

Prediction of power output of a coal-fired power plant by artificial neural network

Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks

Performance prediction of a RPF-fired boiler using artificial neural networks

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