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Abstract
Power Plant Heat exchanger is widely used in chemical and petroleum plants because it can sustain wide range of temperature and pressure. Heat exchanger is a high nonlinearity and poor dynamics plant; therefore it is complex to model and difficult to control its dynamics. In this paper two types of heat exchanger model and controller are applied for selecting suitable model and controller. First model is called (Physical model) and derived using real parameter of heat exchanger plant. Second, a Second Order Plus Dead Time (SOPDT model) that is derived from the response of heat exchanger. While the controllers are consisted of fuzzy proportional derivative (FPD) controller and proportional integral derivative (PID) controller and applied to the model and their responses are compared with the existing PID controller. The PID controller response based on Physical model gives similar response of existing PID controller based real heat exchanger plant in comparison with SOPDT model. That means the Physical model is able to represent the heat exchanger plant dynamics more accurately than SOPDT model. For the controller, the FPD control gives a slight enhancement based on SOPDT model. Therefore, FPD controller is more suitable than PID controller.
Highlights
Advanced control of heat exchanger processes are important tasks for control engineers, as these devices belong to the key of equipment in petrochemical, food processing and pharmaceutical industries and they are energy intensive processes [1]
The model parameters for a FOPDT or SOPDT models are commonly gained from experiment transient response
These techniques had been used for a wide range of process control studies to its simplicity to use and been most effective way to get faster results through real-time processes
Summary
Advanced control of heat exchanger processes are important tasks for control engineers, as these devices belong to the key of equipment in petrochemical, food processing and pharmaceutical industries and they are energy intensive processes [1]. CDM controller performance is more consistent in term of the peak magnitudes of the disturbance error [9] Another technique, multiple model based Proportional integral derivative control (MM-PID) and multiplemodel based model reference adaptive control (MM-MRAC) applied for a nonlinear heat exchanger process. The advantage of the combined NNMPC with fuzzy control is that it is not a linear-model-based strategy and the control input constraints are directly included into the controller synthesis The disadvantage for this method is the complexity of design and time consuming to create their scheme [14]. The simulation results confirm that fuzzy control is one of the possibilities for successful control of heat exchangers This strategy is designed based on nonlinear model [17].
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