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

Abstract

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.

Comparative analysis on the application of neuro‐fuzzy models for complex engineered systems: Case study from a landfill and a boiler

“…The detailed description on the type of boiler and data collection is given elsewhere (Behera et al . , ).…”

“…The transient‐state data were removed from the available plant data during initial data screening, and the data were filtered. More details on the plant characteristics and boiler operation can be found elsewhere (Behera et al, ).…”

“…The data used for this study was collected over a period of 5 months to predict steam temperature, pressure, and mass flow rate using feed water pressure, feed water temperature, conveyor speed, and incinerator exit temperature as the input parameters. The detailed description on the type of boiler and data collection is given elsewhere (Behera et al, 2013).…”

“…The boiler under discussion is of water-tube type, fired with RPF More details on the plant characteristics and boiler operation can be found elsewhere (Behera et al, 2013).…”

“…Analysing the performance of engineered systems with complex functionalities, for example, biological systems such as landfills, and mechanical systems such as boilers, has been a challenge for the scientific community for years. This can be addressed to certain extent by simple predictive models, namely, back propagation neural network models due to their inherent ability to solve many nonlinear systems (Behera, Meher, & Park, ; Behera, Rene, Kim, & Park, ; Behera, Rene, & Park, ).…”