Flow-based control of temperature in long ducts

Model predictive temperature control in long ducts by means of a neural network approximation tool

“…Under these assumptions, the temperature distribution depends only on the fluid velocity. Applying the energy conservation law in this system, the following first-order hyperbolic partial differential equation arises [5]:…”

“…After appropriate nondimensionalization [5], a governing equation of the following form is finally obtained:…”

“…Christofides and Daoutidis [2] introduced the concept of characteristic index for the synthesis of states and output feedback controllers, as well as for the development of robust schemes that can overcome unmodeled dynamics [3,4]. Alotaibi et al [5] used the numerical solution of the Lagrangian form of the PDE in a long duct to analyze the stability of a proportionalintegral (PI) controller for the flow-based control of the outlet temperature.…”

“…The great advantage of the proposed methodology is that no discetization is performed online, since the model can estimate temperature at any location, and consequently the computational time required to solve the optimization problem is significantly reduced compared to conventional MPC approaches. The proposed methodology is tested in the system that was presented in Alotaibi et al [5].…”

Analysis of Thermal Transient Processes by Means of Neural Network Technique

“…The implementation of ANN technique in heat transfer science literature is limited. For the identification or analysis of heat transfer problems a neural network approach has been attempted by authors (Thibault & Grandjean, 1991;Christtofindes, 2001;Alotaibi et al, 2004;Zdaniuk, 2006;Ashforth-Frost et al,1995 andYilmaz &Atik,2007). In the relevant thermal scientific literature is most concerned with the performance prediction and control of heat exchangers (Islamoglu, 2003;Diaz et al, 2001;Pacheco-Vega et al 2001).…”