Chromatographic separation processes are described by nonlinear partial differential and algebraic equations, which may result in high computational cost, hindering further online applications. To decrease the computational burden, different data-driven modeling approaches can be implemented. In this work, we investigate different strategies of data-driven modeling for chromatographic processes, using artificial neural networks to predict pseudo-dynamic elution profiles, without the use of explicit temporal information. We assess the performance of the surrogates trained on different dataset sizes, achieving good predictions with a minimum of 3400 data points. Different activation functions are used and evaluated against the original high-fidelity model, using accuracy, interpolation, and simulation time as performance metrics. Based on these metrics, the best performing data-driven models are implemented in a process optimization framework. The results indicate that data-driven models can capture the nonlinear profile of the process and that can be considered as reliable surrogates used to aid process development.