Optimal process operation is crucial for maintaining system resilience, playing a key role in ensuring operations to continue safely and without interruption even during system failures. This process involves identifying, diagnosing, and fixing causes within a system to restore its function and prevent further issues. However, many current methods rely heavily on machines and computers, which can encounter errors or become trapped in less-than-optimal conditions. They often overlook the valuable insights gained from operators’ years of experience. To address this gap, this chapter presents a novel approach using operator-in-the-loop Bayesian optimization, which combines Bayesian optimization techniques with operator expertise. The proposed method is demonstrated through a case study of a polyvinyl chloride (PVC) production plant, modeled in Aspen HYSYS, and further validated for its practical use with an experimental continuous stirred tank reactor (CSTR) setup.