Neural Network Based Model of an Industrial Oil-Fired Boiler System

Abstract

ABSTRACT ABSTRACTIn this study, an oil In this study, an oil In this study, an oil In this study, an oil----fired boiler system is modeled as a multivariable plant with fired boiler system is modeled as a multivariable plant with fired boiler system is modeled as a multivariable plant with fired boiler system is modeled as a multivariable plant with fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are mod fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are mod fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are mod fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are mod artificial neural network, based on experimental data col artificial neural network, based on experimental data col artificial neural network, based on experimental data col artificial neural network, based on experimental data col forward neural network with Hyperbolic tangent sigmoid transfer function (Tansigmoid) and line forward neural network with Hyperbolic tangent sigmoid transfer function (Tansigmoid) and line forward neural network with Hyperbolic tangent sigmoid transfer function (Tansigmoid) and line forward neural network with Hyperbolic tangent sigmoid transfer function (Tansigmoid) and line neurons (Purelin) are neurons (Purelin) are neurons (Purelin) are neurons (Purelin) are used at the hidden used at the hidden used at the hidden used at the hidden network model is network model is network model is network model is then trained off then trained off then trained off then trained off----l l l line with Levenberg ine with Levenberg ine with Levenberg ine with Levenberg neural network model when neural network model when neural network model when neural network model when subjected to test, u subjected to test, u subjected to test, u subjected to test, u outputs for both temperature and pressure agree closely with the actual plan outputs for both temperature and pressure agree closely with the actual plan outputs for both temperature and pressure agree closely with the actual plan outputs for both temperature and pressure agree closely with the actual plan 0.

Modeling of Reservoir Inflow for Hydropower Dams Using Artificial Neural Network

“…Artificial neural network (ANN) has been widely applied in modeling of many nonlinear hydrologic processes such as numerical weather and global climate model [4], rainfall-runoff model [5] and [6], stream flow model [7], [8] and [9], precipitation prediction [10] and [11], rainfall modeling [12], [13] and [14] and simulation of daily temperature [15]. ANN have been trained to perform complex functions in various fields, including pattern recognition, identification, traffic prediction, classification, speech, vision and control systems [16]. According to Khaing andThinn among forty eight (48) studies conducted using ANN between 1988 and 1994, it was found that neural network models produced superior predictions [11].…”

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

“…Te failure rate in the power equipment prediction for a number of input variables afects its performance [36]. Another application of the ANNs is found in the modelling of an industrial oil-fred boiler plant with reliable results [37].…”

Exergetic Modelling of Oil-Fired Steam Boilers

“…The performance of steam boilers could be assessed based on operational and output parameters which are of primary interest to a researcher or other stakeholders. In a study of an oilfired boiler system, Orosun and Adamu [16] modelled steam boiler as a multivariable plant with two inputs, namely, feed water rate and oil-fired flow rate; and two outputs, which were steam temperature and pressure. The authors modelled the plant parameters using artificial neural network.…”