The accumulation of impurities in the recycling of steel impacts the quality of secondary steel. Understanding impurity levels is crucial in the context of the proliferation of circular economy policies, expected high recycling rates, and growth of scrap consumption. By assuming the accumulation of impurities to be equal worldwide, the understanding of the extent and variation of the mixing and accumulation was limited in previous studies, and the factors influencing those variations were not considered. This is a first cross-national comparison of impurity accumulation in recycled steel. In this study, the copper, tin, nickel, chromium, and molybdenum content was analyzed in over 500 samples of electric arc furnace rebars from China, Japan, Vietnam, Ukraine, and the Netherlands (representing northwestern Europe) with an optical emission spectrometer. The impurity content in rebars represents the content of impurities accumulated in steel scrap in the countries studied. The measured content of impurities was then used to determine the factors influencing the accumulation of those impurities. It was revealed that the recycling technology, the presence of a market for recovered metals, the quality of the material input, steelmaking practices, and the management of byproducts derived from a legislative or economic context played a role in the impurities content. By communicating on scrap chemical content, the collaboration between the recycling and steel industries could be enhanced in terms of matching the demand and supply and facilitating an increase in the scrap share in steelmaking.

Manually laying out the floor plan for buildings with highly-dense adjacency constraints at the early design stage is a labour-intensive problem. In recent decades, computer-based conventional search algorithms and evolutionary methods have been successfully developed to automatically generate various types of floor plans. However, there is relatively limited work focusing on problems with highly-dense adjacency constraints common in large scale floor plans such as hospitals and schools. This paper proposes an algorithm to generate the early-stage design of floor plans with highly-dense adjacency and non-adjacency constraints using reinforcement learning based on off-policy Monte-Carlo Tree Search. The results show the advantages of the proposed algorithm for the targeted problem of highly-dense adjacency constrained floor plan generation, which is more time-efficient, more lightweight to implement, and having a larger capacity than other approaches such as Evolution strategy and traditional on-policy search.