Monoclonal antibodies (mAbs) constitute one of the leading products of the biopharmaceutical market with significant therapeutic and diagnostic applications. This has drawn increased attention to the intensification of their production processes, where model-based approaches can be utilized for successful optimization and control purposes. In this manuscript, dynamic optimization of mAb production in mammalian cell cultures in semibatch operation is performed. To develop a model suitable for optimization, reformulation steps consisting of function smoothening, reducing model size, and scaling are applied to a predictive energy-based model for mAb production presented in Quiroga et al. (2016). Optimization of the reformulated model leads to the derivation of an optimal feeding strategy, accounting for indirect quality measures, dilution effects, and the energy requirements of the cells. The results highlight the increased production outcome by using the reformulated model, and thus indicate the strong dependence of model-based optimization results on model structure.