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

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 CO2 and O2. 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 ε) 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.