MO-E-EQI is first compared with MOBO algorithms under noisy conditions: qNPar-EGO, qNEHVI, TSEMO. The sequential Latin hypercube sampling strategy (named LHS Space Filling) was also included for comparison as a baseline. For the following comparison, the algorithms started with 20 initial sampling points with two objectives to maximize STY and to minimize E-factor. The GPs work best when covariates are normalized to the unit cube and outcomes are standardised (i.e. zero mean, unit variance). To achieve that, prior to fitting a GP, all inputs were scaled to [0, 1] and all the outputs from the training data were standardised, using the sample mean and sample variance. Those samples' means and variances were subsequently used to standardise all model outputs in the sequential part of the algorithm.