(314e) Integration of Dynamic Risk and Control for Enhanced Safety and Operational Efficiency

In contemporary industrial operations, the imperative to ensure safety while optimizing control remains a challenge. This research proposes an innovative probabilistic framework that integrates dynamic risk assessment with explicit Model Predictive Control (eMPC). By incorporating a Bayesian-based probabilistic approach within the eMPC framework via chance-constrained programming, this methodology facilitates the real-time quantification and management of operational risks while seamlessly integrating these assessments into control strategies. As a result, achieving a harmonious equilibrium between operational control and safety assurance becomes attainable.

Central to this framework is the concept of probabilistic safety violation constraints, meticulously formulated within the underlying control optimization model. These constraints exhibit adaptive characteristics, dynamically responding to real-time updates of risk probabilities associated with safety-critical variables. Consequently, the control system acquires the capability to proactively mitigate potential risks while simultaneously optimizing performance objectives. This probabilistic approach empowers anticipatory insights into system behaviors under uncertainty, augmenting the controller's capacity to make well-informed decisions that preempt safety violations and operational inefficiencies. The inherent adaptability and foresight of the probabilistic framework offers a robust solution to managing the intricacies and uncertainties inherent in modern industrial processes. To validate the efficacy of this integrated framework, a comprehensive case study focusing on water electrolyser stack temperature control is presented. This case study serves as a testament to the framework's proficiency in detecting and diagnosing potential faults (fault diagnosis), while also forecasting future system vulnerabilities (fault prognosis). By seamlessly integrating this predictive capability into the dynamically responsive control strategy, a safety-aware proactive explicit model predictive controller design is achieved. The findings underscore significant enhancements in operational control achieved through this integration, with notable improvements in system safety and reliability, all while maintaining optimal performance standards.