(689b) A Systematic Safety-Oriented Process Design and Explicit Model Predictive Control Optimization Approach

The recent initiatives towards process design and operational innovation, such as modular process intensification and industry 4.0, have posed new challenges and opportunities to process safety [1-2]. These novel process solutions typically feature a higher degree of task integration and network interaction, which lead to more stringent requirement to ensure operational safety. To this purpose, advanced process control strategies have been leveraged towards proactive safety monitoring, management, and alarm systems with considerations of external disturbances and abnormality occurrences [3-6]. Despite these advances, key open research questions remain on: (i) How to define a metric which can quantitatively relate (multiple) process variables with the resulting process risk as a function of time? (ii) How to develop a multi-time-scale safety-oriented operational strategy which can address the temporal discrepancy for control and risk propagation? and (iii) How to develop an integrated approach for design, control, and safety optimization?

To address these challenges, in this work we propose a systematic framework for risk-aware process design and control optimization based on the PAROC (PARametric Optimization and Control) framework [7]. The PAROC framework follows a step-wise procedure to generate the optimal control actions of explicit/multi-parametric model predictive control (mp-MPC), which are expressed as affine functions of the defined parametric set (e.g., disturbance, state variables, design variables). It has been extensively applied for the simultaneous design and control of chemical process systems and for the explicit fault-tolerant multi-parametric control [8-9]. In this work, the dynamic risk assessment strategy introduced by Bao et al. [10] is incorporated to quantify the process risks resulted by fault probability and severity. Thus, the upper bounds on process risk can be explicitly formulated as mp-MPC path constraints to identify the maximum set of disturbances and optimal set point selection to theoretically prevent any constraint violation through operation. The risk value can also be controlled as the output variable and/or optimized leveraging the mp-MPC dynamic optimization formulation. We investigate two classes of dynamic process systems for optimal design, control with predictive safety management: (i) fast dynamic systems – in which the MPC moving horizon estimation can be utilized to forecast the potential fault in the successive time steps, and (ii) slow dynamic systems – in which the propagation time of process risk is much longer than that of the control time scale. In this control, a two-level control structure is developed in analogy to integrated control and scheduling optimization to predict the risk evolution in longer time horizon [11]. The framework will be demonstrated on a continuous stirred tank reactor case study for the processing of methylcyclopentadienyl manganese tricarbonyl at T2 Laboratories.

References

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