The results from this study show that it is possible to obtain data series on the steel scrap collection based on mass balance model on the crude steel production figures by steelmaking reactor type and additional knowledge on process metallurgy as well as information on inputs and outputs into the reactors with an area correlation coefficient of 0,91 compared to data obtained from trade statistics. Furthermore, the study shows that based on a new method it is possible to calculate the time duration of mass flows on a continuous basis. Furthermore, two complementary statistical dynamic material flow models that can be used to calculate the societal recycling rates of steel was constructed. These statistical models contribute to a standardized way of obtaining consistent results. The new models are able to segregate the non-recirculated amounts of steel into the hibernating steel stock available for future collection from the amounts of losses based on statistics. The results show that it is possible to calculate the amounts of steel scrap available for steelmaking at a given point in time. In addition, based on the new models it is possible to calculate recycling trends in society. Also, the models are able to calculate robust forecasts on the long-term availability of steel scrap, and test if forecast demand of steel scrap exceeds a full recovery. This due to that the steel scrap generation is a function of the collection rate of steel scrap. Also, a method for obtaining representative samplings on the alloy content in steel scrap called random sampling analysis (RSA) was developed. The results from the RSA show that it is possible to optimize the recovery of valuable elements in the production process of steelmaking based on the information on the composition of steel scrap. / <p>QC 20151020</p>