The Oregonator model can be used to analyze nonlinear changes in systems and can predict the stability of the system. It is an autocatalytic reaction model and a Multiple Input Multiple Output case study that provides a reduced mathematical description of the Belousov–Zhabotinsky (BZ) reaction, a non-equilibrium oscillating chemical reaction. It captures the nonlinear chemical oscillations that arise from far-from-equilibrium kinetics. Bifurcation Analysis (BA) offers a mathematical approach to understanding how systems behave under changing conditions by examining nonlinear behaviors and transitional states, known as bifurcations. This poster demonstrates a novel framework that utilizes BA to assess stability, demonstrated through the Oregonator autocatalytic reaction model. The relationship between parameter variation, of stoichiometric coefficient f, for this reaction and the onset or loss of dynamic stability remains only partially characterized across the full range of realistic operating conditions. This research will analyze this relationship in more depth using Python. The purpose of this poster is to present a proposed experimental framework and receive feedback from the community. Future work will include more case studies and integration with other frameworks to assess stability.