Fuzzy estimation for temperature distribution of furnace inner surface

A fuzzy inference method (FIM) for predicting the temperature distribution of the furnace inner surface is proposed in this work. The deviations between the computed and measured temperature at measurement points of furnace wall are taken as input parameters of fuzzy inference units (FIUs), and the corresponding fuzzy inference components of measured information are obtained by the FIUs. According to the importance of the various measured information, the fuzzy inference components then are weighted and synthesized to gain the compensations of the guessed temperature distribution. Finally, the temperature distribution estimation of the furnace inner surface is accomplished. Numerical experiments are performed to study the effects of measurement points number, measurement errors and other factors on the predicted results. Comparisons with the conjugate gradient method (CGM) and the genetic algorithm (GA) are also conducted, and they all show the validity and superiority of the proposed method in the current paper.

► We propose the fuzzy inference method (FIM).
► Temperature distribution of furnace inner surface was estimated availably by FIM.
► The FIM possesses better anti-ill-posed character than the CGM and GA.
► The FIM reduces the dependence of estimated results on measurement points number.
► The FIM can weaken the effects of measurement errors.