Equation-based modeling that utilizes reusable components to represent real-world systems can result in excessively large models. This, in turn, significantly increases compilation time and code size, even when employing state-of-the-art scalarization and causalization techniques. This paper presents an algorithm that leverages repeating patterns and uniform causalization to enable array-size-independent constant time processing. Allowing structural parameters that govern array sizes to remain resizable during and after the causalization process enables the formulation of an integer-valued nonlinear optimization problem. This approach identifies the minimal model configuration that preserves the required structural integrity, which can subsequently be resized as needed for simulation. The proposed method has been implemented in OpenModelica and builds upon preliminary work aimed at preserving array structures during causalization, while still resolving the underlying problem in a scalarized manner.