In summary, we have developed and demonstrated a holistic "dual modeling" approach to reaction optimization. This approach is capable of developing both an analytical model and reaction model within one experimental day. First, standard addition is utilized to collect calibration data for the FTIR and a python program automatically generates a PLS model to determine species concentrations. Dynamic flow experiments are then used to quickly cover a broad chemical space and collect high density data for reaction model development. The obtained data is fed into a software program that fits kinetic parameters for different reaction networks within minutes. Additionally, the software can use the fitted kinetic parameters and the reactor geometries to generate a digital twin of the synthesis process. This process model is utilized for in-silico optimization of multiple objectives, such as conversion, space-time yield or measures of environmental impact. This drastically reduces optimization timescales and saves resources during process development.