Decoupling control of high-purity heat integrated distillation column process via active disturbance rejection control and nonlinear wave theory

Abstract

Although the heat integrated distillation is an energy-efficient and environment-friendly separation technology, it has not been commercialized. One of the reasons is that the nonlinear dynamics and the interactions between various control loops have limited the performance of the traditional control strategy. To achieve a high-purity product concentration, a dynamic decoupling control strategy based on active disturbance rejection control (ADRC) is proposed. The effects of interactions, uncertainties and external disturbances can be estimated and rejected by using extended state observer. Considering the constraints on manipulated variables, an optimized ADRC is designed for the first-order system. Moreover, a concentration observer based on a nonlinear wave model is formulated to reduce the number of sensors. In the simulation research, the related internal model control (IMC), multi-loop ADRC and model predictive control (MPC) are compared with the proposed control scheme. The simulation results demonstrate the advantages of the proposed control scheme on tight control, decoupling performance and disturbance rejection for the high-purity heat integrated distillation column.

Reduced-Order Modeling and Control of Heat-Integrated Air Separation Column Based on Nonlinear Wave Theory

“…Moreover, several control scheme performances have been enhanced to some extent based on the wave model. Some researchers have explored the integration of control schemes and wave theory, achieving certain levels of success in this endeavor [39][40][41].…”

Experimental performances of nonlinear GPC and discrete–time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil

“…Giwa et al [16] implemented MATLAB/Simulink's MPC toolbox for renewable energy. Regarding the multivariable theoretical control, Wu [17] proposed an improved PID controller optimized with an extended non-minimal state space model based MPC, Saravanakumar et al [18] used Lagrange-based state transition algorithm to tune the decentralized PID controller, using its numerical stability and performance, Abraham et al [19] developed an optimal GPC using the first principle and linearized 16 th order and reduced fifth order models, Hadian et al [20] used an event-based neural network prediction controller using the Cuckoo Optimization Algorithm for the nonlinear process, Shin et al [21] used HYSYS and examined an MPC integrated neural network model in the nonlinear process, and Cheng et al [22] proposed a dynamic decoupling strategy based on active disturbance rejection control for a first order system with an observer based on a nonlinear wave model.…”

Adaptive-based linear active disturbance rejection attitude control for quadrotor with external disturbances

“…In Liu et al (2018), for a class of switched uncertain non-linear systems, the ADRC is proposed to solve the problem of parameter uncertainty and unknown nonlinearity. In Cheng et al (2020), a dynamic decoupling control strategy based on ADRC is proposed, which uses the ESO to estimate and compensate for uncertain external disturbances to achieve high-purity production of products in distillation technology. However, the ADRC contains nonlinear functions and many adjustable parameters, which makes parameter tuning more complex.…”