In the experimental case study, an esterification reaction was selected with four continuous variables: equivalent, temperature, flow rate and catalyst loading. Two objective functions were set as space-time-yield and E-factor. MO-E-EQI was able to find the Pareto front efficiently and identified a clear trade-off between the two objectives. A set of Pareto front solutions generated with STY ranging from 2.66 g/(L•min) to 195.04 g/(L•min) while the E-factor ranged from 2.89 to 8.47, which left researchers to decide which condition to choose for further development. Notably, MO-E-EQI shows efficient performances under noise, however, this may be at a cost of increasing the number of experimental measurements by repetitions. It is therefore more suitable for automated experimental platforms or large-scale manufacturing where cheap sensor data is available.