One of the main concerns in the goods production industries (process industry) is to ensure quality of finished products. The main approach is to identify a correlation between the process settings and the quality of the final product. In this work, a two steps approach is presented to modelling an industrial process. The first step uses a fault detection method, a Principal Components Analysis (PCA) based method, to retain only individuals from a normal production process. The second step consists in using a Support Vector machines Regression (SVR) method to build a model of the considered process. It is based on the historic process data defined by an output (a criterion on the product quality) and multiple inputs (various production line settings). The proposed framework is tested on a fluidized bed combustion boiler in the context of paper industry. The experiments confirm the efficiency of our approach.