The steel industry is among the strategic industries and it plays an important role in the persistent economic growth of countries. Therefore, awareness of current and future conditions of this industry and identification of factors affecting them is important to economic analysis. The most important purposes of economic analysis are cited as identification of effective factors and accurate predictions. One of the main challenges faced by managers, is to provide logical approaches in order to create balance between supply and demand and control their associated parameters. Accordingly, the main aim of this thesis is to use scientific methods to manage consumption crude steel in the country. However, the literature shows that yield accurate results in the prediction of consumption, especially in long-term horizon is relatively difficult. Researchers believe that main reason of this matter, is high level of complexity and uncertainty in financial markets. So, in this thesis in order to simultaneously model the complexities and uncertainties in the data, a hybrid of intelligent and soft computing models have been used as an effective way. In this way, the list of variables is recognized based on the literature and expert opinions. Then the linear and nonlinear relationships and also correlations between variables are evaluated and final explanatory variables specified. Finally, four models including hard ltr"
