Advanced process control system with MPC as a new approach for layer of protection analysis

Marques, Fernando Henrique; Alvarez, Luz Adriana

doi:10.1016/j.jlp.2023.104993

Cited by 2 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, liquid level control in high-sensitive environments is not just to control hazardous substances but also to reduce overall risk and make them more reliable and safer to operate with the strictest regulations. 3 Most commonly, liquid level control in interacting tank systems exhibits high non-linearity, strong dynamic characteristics, complexity in nature and lagging phenomenon with a multi-input/multi-output (MIMO) framework. 4,5 Refineries and petrochemical giants still use storage vessels of different shapes (spherical) and geometries to ensure the inherent safety of inflammable liquids.…”

Section: Introductionmentioning

confidence: 99%

Predictive control scheme by integrating event-triggered mechanism and disturbance observer under actuator failure and sensor fault

Panda,

Thirunavukarasu,

Panda

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

Liquid-level control in an interacting storage-tank system exhibits major challenges due to the effect of interaction and the presence of combustible/flammable liquid as it faces huge threats from the unmeasurable disturbances, irregular geometry and unmodelled dynamics of the process plant. A combination of tanks with multiple geometries may be preferred for storing liquids using common pumps for energy conservation. Continuous improvement of risk assessment, predicting hazardous scenarios and implementation of inherently-safer-control systems ensure process safety and, as a result, ease out the safe operation. From the process analysis, it was observed that the dynamics of the interacting tank are very sensitive to effective parameters like valve coefficient and pump gain. To overcome the loss of inventory or production, we investigate the synthesis of a non-linear model predictive control scheme with an outline of simultaneous state and parameter estimation strategies. This mechanism deals with a stochastic event-trigger cubature Kalman filter scheme to estimate process states while performing servo operations. To mitigate plant uncertainty, reduce the impact of large disturbances and nullify the effect of actuator failure/sensor fault, influential model parameters are estimated simultaneously with the model state(s). Predicted values of the model states/parameters/faults are used to derive an effective control effort. An inferential non-linear model predictive control paradigm is proposed, where the primary variable(s) is (are) measured with the help of measurable secondary state(s). To demonstrate the practical utility and effectiveness of the control framework for safe operation and loss prevention, a complex industrial process (quadrupled tank) is considered for control, where the system dynamics have been structured newly. Realistic simulations such as servo-regulatory compliance and elimination of measurement noise with a state-of-the-art simulator ensures the efficacy of the proposed scheme. The results are analysed further for a comparative study with traditional non-linear model predictive control law. To guarantee convergence of the estimator and stability of the closed-loop controller, the Lyapunov theorem is well blended.

show abstract

Section: Introductionmentioning

confidence: 99%