Using our trained surrogate models, we can derive optimal extraction conditions for arbitrary design criteria associated with lignin-based products. In this context, the design criteria are a set of requirements placed on the extracted lignin by a potential application. For instance, the lignin yield should typically exceed some minimum threshold and we might require high amounts of specific moieties. To solve this problem, we can employ a Pareto front analysis to our surrogate models to find optimal trade-offs between the design criteria. The practical implication of having multiple optimal trade-offs is that changing the extraction conditions to bring one target closer to its design criteria will result in at least one other target having a less optimal value. As a concrete example of how to apply a Pareto front analysis we consider, as a first case, optimizing the AqSO biorefinery for extracting lignin suitable as a feedstock in the production of aromatic platform chemicals. For this purpose, most processes require a maximal number of β-O-4 linkages to maximize the yield of the targeted monomers. Hence, the maximum revenue per original biomass correlates with both high lignin yield, and high β-O-4 content. Using our surrogate models, we can determine all feasible combinations of yield and β-O-4 content, i.e. those obtainable by varying the processing conditions in their allowed range. These feasible points forms a twodimensional space, as indicated in blue in (Fig. ). By subsequently calculating the Pareto front, we see that it lies on the boundary of the space of feasible points: these are the optimal trade-offs. We observe from the shape of the Pareto front that high values of the lignin yield are correlated with low β-O-4 content, in agreement with Fig. ). The corresponding Pareto optimal solutions, i.e., the processing conditions (P f , T ) that produce opti-mal trade-offs, are then found by projecting the Pareto front onto (P f , T )-space (Fig. ).