Constant advancements in biomanufacturing require operational frameworks that enable accurate comparisons between similar production processes for performance and cost optimization purposes. However, the extensive and heterogeneous nature of available data which is often presented in different formats, characterized in different ways, and reported in disparate units - poses a significant challenge to an efficient and effective comparative analysis. In this paper, the potential benefits of applying the biomanufacturing production process ontology to provide a common ground for addressing data heterogeneity issues are investigated in the context of process design optimization with respect to key performance indicators (KPIs). Using acetic acid fermentation as a case study, three semi-continuous processes involving different bacterial species and feedstock sources were analyzed. Existing industrial ontologies were utilized, and additional ontologies were developed to facilitate the normalization of notions and link data from multiple domains, including life cycle analysis, process, production, and materials. Following ontology development best practices, competency questions and respective queries were formulated to validate the ontology, with the aim to evaluate KPIs, including the material intensity and the number of fermentation cycles each microorganism can sustain. Results demonstrate that the ontology effectively resolved data inconsistencies, enabling more accurate comparisons and providing deeper insights into the differences in processes across various strains and conditions.