Parametric and nonparametric identification of shell and tube heat exchanger mathematical model - Mulyana, Tatang (2014)
Parametric and nonparametric models of a shell and tube heat exchanger are studied. Such models are very important because they provide information about controlling a system operation. Without the model, the control task would be difficult for tuning of controller. For many years, researchers have studied these models; however, their models are still less satisfactory since they are not in general form. This problem is caused by two key issues, namely, multiple unknown parameters and highly nonlinear structures. Energy balances have been set-up for condition of unknown parameters which involved, among others, temperature, flow rate, density and heat capacity. The identification process produces a dynamic model of the heat exchanger which is developed based on a lumped parameter system. The model developed is single input single output whereas input signal is hot water flow rate and the output is cold water temperature. The general form of the model obtained could have parametric model structures such as auto regressive with external input, average auto regressive moves with external input, output error or box-jenkins. The study in this thesis aims to solve the general form through parametric and nonparametric models which has been proposed as candidate models. Both candidate models have been implemented and tested by applying several data sets constructed in lab experiments. The first finding is the derivation of the dynamic model in the general form of the transfer function in s domain, and it has been proven that it has parametric model structure. The second finding is the first order without delay time transfer function of the nonparametric model where they have gain is 35.20C and time constant 7200s. These have proven to fulfill that the measured experimental data contains calculated error that is no than more 2%. The third finding is the parametric model obtained has proven that the measured experimental data contains calculated error level that is very satisfactory, i.e. less than 1%. This error has been determined based on the final prediction error for each model structure used. The best model has been chosen, i.e. bj31131. It has the smallest values of the loss function and final prediction error of 0.0023, and it has high values of the best fits, i.e. 96.84%. Parameter optimization has been calculated to determine minimization or maximization of functions which involved the parameter studied. It is used to find a set of design parameters that can in some way be defined as optimal. The first until the third findings are minor contribution while the parameter optimization has been a major contribution.