The sampled points might fall in the infeasible design space due to extreme operating conditions for experiments (e.g., unexpected reactions occur at high temperature) or nonconverged recycle streams, or integration failure on stiff models during computational simulations. A classifier can be trained to pre-treat the data inputs. Only the selected data inputs can be passed into the simulation or experiment stage, thus saving the average time spent on a single data point.