A Modeling Framework to Investigate the Influence of Fouling on the Dynamic Characteristics of PID-Controlled Heat Exchangers and Their Networks

Abstract

The present work is an extension of the authors’ previous research, where changes in the dynamic behavior of heat exchangers induced by fouling build-up were studied. In the present work, the authors used the previously elaborated and validated mathematical model of transient heat exchange with the influence of thermal resistance of fouling taken into account. The behavior of specific Heat Exchanger Networks (HENs) coupled with a Crude Distillation Unit together with their control loops is simulated using Simulink/MATLAB and the influence of fouling build-up on specific indices of quality of operation is investigated. According to the presented results, the higher the number of heat exchangers in the PID control loop and the greater the number of heat exchangers interacting in the network, the smaller the influence of fouling on the control quality indices, and in the extreme case, this influence may be negligible. This might be caused by the compensation of the negative effects of fouling build-up when the heat exchangers are interacting in the HEN. Nevertheless, potential adverse effects of fouling on HEN operation can be prevented by periodic adjustments of the optimal values of PID gains.

Iterative quality weighted interpolation for LPV‐MPC control of industrial heat exchanger under varying fouling conditions

“…The prediction model uses the present input and past output to generate future outputs. A prediction model with dynamically varying model parameters converts the conventional MPC to its adaptive variant 16,33 . In the proposed work, the LPV model is used to vary the model parameters concerning the FR as in ().…”

“…A prediction model with dynamically varying model parameters converts the conventional MPC to its adaptive variant. 16,33 In the proposed work, the LPV model is used to vary the model parameters concerning the FR as in (8). The LPV models are defined in the statespace form with A, B, C corresponding to the state, input, and output matrix, which governs the dynamics of HE.…”

“…15 PID controller with adaptive control strategies demonstrates energy-efficient operations of HE networks even in the presence of fouling variations. 16 Model predictive controller has become the most extensively used advanced process control technique for HE. It can handle system constraints, interaction, measured disturbances, and inferential control in a unified manner, which is one of the fundamental requirements for HE control.…”

“…Gain scheduled PID controller for a refinery HE network is designed, which achieves more stable control performance 15 . PID controller with adaptive control strategies demonstrates energy‐efficient operations of HE networks even in the presence of fouling variations 16 …”

Implementation of a neural network MPC for heat exchanger network temperature control

“…The dynamic characteristics of the PID controller were considered by Trafczynski et al (2019a). The authors concluded that periodic and systematic adjustment of the optimal values of PID gains can prevent the adverse effect of fouling and minimize the effect of interacting heat exchangers.…”