The second objective in sampling is concerned with the improvement of data quality. The design space for sampling is initially based on limited prior experience or even random guesses, and the infeasible design space is commonly unavoidable. Consequently, some inputs, which happen to be sampled from the infeasible design space, can lead to unexpected outputs, such as non-converged simulation outputs or experimental failures. Such outputs will introduce significant errors to the surrogate construction, and thus they are ineffective data, which should be screened out. To increase data effectiveness, a classifier can be constructed to distinguish between infeasible and feasible design spaces. Such application of a classifier has been successfully demonstrated in prior research works. Ibrahim et al. reported that a support vector machine (SVM) can be used to set a feasibility constraint to filter infeasible design space for non-converged simulations. Cao et al. adopted a Bayes classifier to improve the design space for the experimental conditions of formulations. Kim et al. applied a combined classification system to increase the quality of design space for computation-based material discovery, which can significantly reduce the number of further samplings. Houben et al. included a classifier into a Bayesian optimisation algorithm to avoid infeasible experiments in emulsion polymerisation. To further enhance data effectiveness, exploitation-based methods can be considered to identify the promising sample placement. A simple example can be used to explain the importance of sample placement. Suppose a data-driven model fits a model with a simple form ! = sin(') at the design space [0, 5]. Then the sampling places at 0, ! " , 5 are more important than other places. Although the real-world problems can be more complex with high dimensionality, such placement can be identified by selecting the data points which can minimize the surrogate uncertainty. Cozad et al. developed a workflow called ALAMO for algebraic model building in a sequential sampling way. For each new data to sample, they apply a derivative-free optimization technique to maximize the error between the surrogate and the original model. To identify one optimal sample placement, many new data points are required to be generated for evaluation during the optimization. Consequently, this method, actually, generates far more data points than the number of optimal data points, as presented in their results. An alternative approach is to employ GP-based surrogates, which contain the uncertainty-related information, and the new points can be sampled to reduce the predicted uncertainty. However, the model-based methods can result in over-sampling in local spaces and lose generality. For example, Gonzalez-Garay et al. reported a surrogate-based optimization method for a chemical flowsheet, and over-sampling was executed to refine the surrogate near the optimal solution. However, the obtained surrogate may not be applicable for other case studies if the objective changes, because only the prior local space near optima is highly exploited.