The objective of this paper is to present the main results of an engineering-research project dealing with model-based evaluation of waste streams treatment from a biotech company. This has been extensively done in domestic treatment systems, but is equally important, and with different challenges in industrial wastewater treatment. A new set of biological (activated sludge, anaerobic digestion), physicochemical (aqueous phase, precipitation, mass transfer) process models and model interfaces are required to describe removal of organics in an upflow anaerobic sludge blanket (UASB) reactor plus either traditional nitrification/denitrification (A<sub>1</sub>) or partial nitritation (PN)/anammox (ANX) (A<sub>2</sub>) processes. Model-based analysis shows that option A<sub>1</sub> requires a decrease in digestion energy recovery (E<sub>recovery</sub>) in order to have enough organic substrate for subsequent post NO<sub>3</sub> reduction treatment (95 kWh.kg N−1). In contrast, A<sub>2</sub> in an aerobic granular sludge reactor allows for higher UASB conversion since N removal is carried out autotrophically. The study also reveals that the addition of an aerated pre-treatment unit prior to the PN/ANX (A<sub>2</sub>) reactor promotes COD and H2S oxidation, CO<sub>2</sub> and CH<sub>4 </sub>stripping, a pH increase (up to 8.5) and a reduction of the risk of intra-granular precipitation as well as sulfide inhibition. Simulations indicate clear differences regarding the microbial distribution/abundance within the biofilm in A<sub>2</sub> when comparing the two operational modes. Final results show the effects of different loading and operational conditions; dissolved oxygen (DO), Total Suspended Solids (TSS<sub>op</sub>), energy recovery (E<sub>recovery</sub>); on the overall process performance; N removal, aeration energy (E<sub>aeration</sub>), net energy production (E<sub>recovery</sub>); using response surfaces, highlighting the need of integrated approaches to avoid sub-optimal outcomes. The study shows the benefits of virtual plant simulation and demonstrates the potential of model-based evaluation when process engineers in industry have to decide between competing options.