Within complex chemical engineering applications, subsystems or technologies have to be pre-selected and evaluated, without being an expert in each technology. In such cases, a surrogate model can help, if it can be set up with easily available reliable tools and data from publications. Here, a pressure swing adsorption (PSA) surrogate model for hydrogen separation was developed. Therefore, 90 published data sets were chosen for training and five different machine learning algorithms were tested. For these data sets a random forest regression yielded the best results when 80 % of the data was used for training and 20 % for testing. The predicted hydrogen recovery deviated from the true value by 6.6 %. The procedure of the surrogate development and analysis is described in this work as a transferable methodology to other scientific questions, not limited to PSA applications.