Abstract
A heat exchanger is widely used for energy management or heat recovery in sustainable energy systems. In many application cases, the outlet temperature should be strictly controlled as desired. However, it is challenging to obtain an accurate dynamic model due to the high-order dynamics, thus reducing the control performance. To this end, this paper proposes a novel identification method by considering the heating process as an approximate second-order plus time delay (SOPDT) model. A normalized analysis indicates that the time-scaled step responses of the general second-order models almost intersect at the same point, which leads to an equation describing the sum of the time constants. Critical stability analysis based on the Nyquist criterion gives another two equations in the frequency domain. Hence the time constants and time delay can be obtained by solving the equations. Illustrative examples show the identification efficiency of the proposed method in the parameter estimation, model reduction, and anti-noise performance. With an effective identification, the high-fidelity SOPDT model makes the PID controller tuning less challengeable. The simulation results based on a benchmark heat exchanger model demonstrate the feasibility of the identification and control. Finally, a real heat exchanger control facility is built and the experimental performance agrees well with the simulation expectation, depicting a promising application prospect in future sustainable applications.
Highlights
A heat exchanger is widely existing in sustainable energy systems [1,2,3,4]
We propose a simple but efficient method to describe a heat exchanger system as second‐order plus delay time (SOPDT) for the purpose of PID parameters tuning
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Summary
A heat exchanger is widely existing in sustainable energy systems [1,2,3,4]. It is generally required to control the outlet temperature, tracking a desired command for economic and safe operation purposes. Another work done by Jamal [8] presented a model of fuzzy logic control combined with neural network techniques His results showed that control of the fuzzy logic controller was capable of stabilizing the temperature of the heat exchanger. A new survey conducted in more than 100 boiler-turbine units in Guangdong Province, China, shows that the single-loop PI controller plays a dominant role in the process industry [17] Their prevalent use owes much to their simple structure and their requirement of less parameters for tuning [2,14], which exhibit an ideal control performance in practical use. Note that low-order models of FOPDT or SOPDT can efficiently describe the dynamic characteristics of high-order linear models for the purpose of PID controller tuning [20,21].
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