Antibody therapies are important in treating life-threatening ailments such as cancer and autoimmune diseases. Purity of the antibody is essential for successful applications and Protein A selective resin extraction is the standard step for antibody recovery. Unfortunately, such resins can cost up to 30% of the total cost of antibody production. Hence, the optimal design of this purification step becomes a critical factor in downstream processing to minimize the size of the column needed. An accurate predictive model, as a digital twin representing the purification process, is necessary where changes in the flow rates and the inlet concentrations are modeled via the Method of Moments. The system uncertainties are captured by including the stochastic Ito process model of Brownian motion with drift. Pontryagin’s Maximum Principle under uncertainty is then applied to predict the flowrate control strategy for optimized resin use, column design, and efficient capturing of the antibodies. In this study, the flow rate is controlled to optimize the process efficiency via maximizing the theoretical plate number with time, the objective for efficient resin usage within a fixed-size column. This work successfully achieved optimality, which was also confirmed via experimentation, leading to higher antibody resin loading capacity. When the work was expanded to 200 cycles of Protein A usage, significant improvements in the downstream process productivity were achieved allowing for smaller footprint columns to be used.