The two variables and their corresponding limits make up the design space of our optimization problem. We consider two different ways of adjusting the sampling of the design space to a setting with multiple experimental targets: the pure and the combined acquisition strategies. In the pure acquistions (PA) strategy, we carry out separate BO processes for each target function. This means that independent acquisitions are made for each target function, and used to update the corresponding surrogate model at every iteration (Fig. ) much like in conventional BO. With the combined acquistions (CA) strategy, our aim is to investigate if the optimization can be accelerated by allowing data exchange between two simultaneous BO processes. Here, data acquired for different target functions is combined and used to update all existing surrogate models at every BO iteration (Fig. ). Another important consideration is which, and how many, target functions to include in the BO acquisition process. For simplicity, we elected to focus on two important targets: lignin yield and β-O-4 content. Given the important role that β-O-4 moieties play in chemical reactions, this will help us produce high yields of chemicals to target different lignin applications. We emphasize that, in addition to the lignin yield and β-O-4 content, each experiment performed also provided data for the S/G ratio and the carbohydrate content. This enabled us to train surrogate models for all these targets as well, even if they were not actively employed to generate new acquisitions.