Nonlinear interactions can strongly affect Earth observations collected by hyperspectral sensors, especially in multiple layer and multiple scattering scenarios. In this paper, we introduce a new nonlinear mixing model that aims to efficiently characterize the physical-chemical composition of scenes where super-linear interactions among endmembers occur. Experimental results show how the proposed method can actually outperform nonlinear HSU algorithms that have been already introduced in literature.