Plant-wide control strategy applied to the Tennessee Eastman process at two operating points

Data-driven digital twin technology for optimized control in process systems

Due to the installation of various apparatus in process industries, both factors of complex structures and severe operating conditions could result in higher accident frequencies and maintenance challenges. Given the importance of security in process systems, this paper presents a data-driven digital twin system for automatic process applications by integrating virtual modeling, process monitoring, diagnosis, and optimized control into a cooperative architecture. For unknown model parameters, the adaptive system identification is proposed to model closed-loop virtual systems and residual signals with fault-free case data. Performance indices are improved to make the design of robust monitoring and diagnosis system to identify the apparatus status. Soft-sensor, parameterization control, and model-matching reconfiguration are ameliorated and incorporated into the optimized control configuration to guarantee stable and safe control performance under apparatus faults. The effectiveness and performance of the proposed digital twin system are evaluated by using different simulations on the Tennessee Eastman benchmark process in the presence of realistic fault scenarios.

Plant-wide control design based on steady-state combined indexes

This work proposes an alternative methodology for designing multi-loop control structures based on steady-state indexes and multi-objective combinatorial optimization problems. Indeed, the simultaneous selection of the controlled variables, manipulated variables, input-output pairing, and controller size and interaction degree is performed by using a combined index which relies on the sum of square deviations and the net load evaluation assessments in conjunction. This unified approach minimizes both the dynamic simulation burden and the heuristic knowledge requirements for deciding about the final optimal control structure. Further, this methodology allows incorporating structural modifications of the optimization problem context (degrees of freedom). The case study selected is the well-known Tennessee Eastman process and a set of simulations are given to compare this approach with early works.