Fahad Albalawi

Process operational safety using model predictive control based on a process Safeness Index

Abstract It has been repeatedly suggested that the common cause-and-effect approach to evaluating process safety has deficiencies that could be addressed by a systems engineering perspective. A systems approach should consider safety as a system-wide property and thus would be required to integrate all aspects of the process involved with monitoring or manipulating the process dynamics, including the control, alarm, and emergency shut-down systems while operating them independently for redundancy. In this work, we propose initial steps in the first systems safety approach that coordinates the control and safety systems through a common metric (a Safeness Index) and develop a controller formulation that incorporates this index. Specifically, this work presents an economic model predictive control (EMPC) scheme that utilizes a Safeness Index function as a hard constraint to define a safe region of operation termed the safety zone. Under the proposed EMPC design, the closed-loop state of a nonlinear process is guaranteed to enter the safety zone in finite time in the presence of uncertainty while maximizing a stage cost that reflects the economics of the process. Closed-loop stability is established for a nonlinear process under the proposed implementation strategy.

Process operational safety via model predictive control: Recent results and future research directions

Abstract The concept of maintaining or enhancing chemical process safety encompasses a broad set of considerations which stem from management/company culture, operator procedures, and engineering designs, and are meant to prevent incidents at chemical plants. The features of a plant design that take action to prevent incidents on a moment-by-moment basis are the control system and the safety system (i.e., the alarm system, safety instrumented system, and safety relief system). Though the control and safety systems have a common goal in this regard, coordination between them has been minimal. One impediment to such an integrated control-safety system design is that the traditional industrial approach to safety focuses on root causes of incidents and on keeping individual measured variables within recommended ranges, rather than seeking to understand incidents from a more fundamental perspective as the result of the dynamic process state evolving to a value at which consequences to humans and the environment occur. This work reviews the state of the art in control system designs that incorporate explicit safety considerations in the sense that they have constraints designed to prevent the process state from taking values at which incidents can occur and in the sense that they are coordinated with the safety system. The intent of this tutorial is to unify recent developments in this area and to encourage further research by showcasing that the topic, though critical for safe operation of chemical processes particularly as we move to more tightly integrated and economics-focused operating strategies, is in its infancy and that many open questions remain.

Distributed economic model predictive control with Safeness-Index based constraints for nonlinear systems

Abstract In this work, sequential and iterative distributed economic model predictive control (DEMPC) architectures are developed with constraints based on a metric (termed the Safeness Index) that is indicative of the safeness of operating a process at a given state in state-space. The DEMPC’s may have lower computation times than a centralized economic model predictive control (EMPC) design with Safeness Index-based constraints, without significantly limiting closed-loop economic performance, which enhances their practicality and ability to improve process operational safety. Sufficient conditions are derived under which the implementation strategies for the DEMPC’s guarantee closed-loop stability.

Integrating production scheduling and process operation via economic model predictive control

Managing production schedules and tracking time-varying demand of certain products while optimizing process economics are subjects of central importance in industrial applications. Production schedule following is generally required for a small subset of the total process state vector. Motivated by this, the present work proposes an approach that targets achieving the desired production schedule while maximizing economics using economic model predictive control (EMPC), which is a feedback control approach that optimizes plant economics in a receding horizon fashion. Conditions for closed-loop stability are derived for a general class of nonlinear systems. The proposed EMPC scheme was applied to a chemical process example where the product concentration was requested to follow a certain production schedule. Simulation results demonstrate that the proposed EMPC was able to maintain closed-loop stability, achieve the desired production schedule, and maximize plant economics.

Simultaneous control of safety constraint sets and process economics using economic model predictive control

Maintaining safe operation of chemical processes is of paramount importance in process systems and control engineering, and is ideally achieved while maximizing profit. It has long been argued that process safety is fundamentally a process control problem, yet few research efforts have attempted to integrate process safety and control. Economic model predictive control (EMPC) has attracted significant attention recently due to its ability to optimize process operation from an economic perspective. However, there is very limited work on the problem of integrating safety considerations with EMPC. Motivated by the above considerations, this work presents an EMPC methodology that adjusts in real-time the size of the safety sets in which the process state should reside in order to ensure safe process operation and feedback control of the process state while optimizing economics via time-varying process operation. Recursive feasibility and closed-loop stability are established for a sufficiently small EMPC sampling period. The proposed method is demonstrated with a chemical process example.

Intermittent sensor fault detection for stochastic LTV systems with parameter uncertainty and limited resolution

ABSTRACT This paper considers the detection problem of intermittent sensor faults in stochastic linear time-varying systems with both parameter uncertainty and limited resolution. By introducing the soft measurement model, a state estimator is designed whose upper bound of estimation error covariance is obtained and minimised at each time step. Based on it, the residual is generated and its relationship with the fault is analysed quantitatively. Then the evaluation function and corresponding detection threshold is given. Our proposed method is recursive and therefore suitable for real-time online applications. At last, two simulation studies are carried out to illustrate the validity of our proposed method.

Process safeness index: Its definition and use in economic model predictive control to ensure process operational safety

In this work, we propose initial steps in the first systems safety approach that coordinates the control and safety systems through a common metric (a Safeness Index) and develop a controller formulation that incorporates this index. Specifically, this work presents an economic model predictive control (EMPC) scheme that utilizes a Safeness Index function as a hard constraint to define a safe region of operation termed the safety zone. Under the proposed EMPC, the closed-loop state of a nonlinear process can be guaranteed to enter the safety zone in finite time while maximizing the process economics. The proposed design is demonstrated using a chemical process example.