An Application of Nonlinear Autoregressive(NARX)Model to Predict Adsorbent Bed Temperature of Solar Adsorption Refrigeration System

In any solar adsorption refrigeration system,there are three major components:a solar collector adsorbent bed,a condenser and an evaporator.All of those components operate at different temperature levels.A solar collector with a tubular adsorbent configuration is proposed and numerically investigated.In this study,a nonlinear auto-regressive model with exogenous input is applied for the prediction of adsorbent bed temperature during the heating and desorption period.The developed neuronal model uses the MATLAB Network toolbox to obtain a better configuration network,applying multilayer feed-forward,the TANSIG transfer function,and the back-propagation learning algorithm.The input parameters are ambient temperature and the uncontrolled natural factor of solar radiation.The output network contains a variable representing the adsorbent bed temperature.The values obtained from the network model were compared with the experimental data,and the prediction performance of the network model was examined using various performance parameters.The mean square error(MSE)and the statistical coefficient of determination(R2)values are excellent numerical criteria for evaluating the performance of a prediction tool.A well-trained neural network model produces small MSE and higher R2 values.In the current study,the adsorbent bed temperature results obtained from a neural network with a two neuron in hidden layer and the number of the tapped time-delays d=9 provided a reasonable degree of accuracy:MSE=1.0121 and R2=0.99864 and the index of agreement was 0.9988.This network model,based on a high-performance algorithm,provided reliable and high-precision results concerning the predictable temperature of the adsorbent bed.

Restart-up Performance of a CFB Boiler after a Sudden Power Failure Accident

Due to the massive bed materials in its typical main loop,a circulating fluidized bed(CFB)boiler may face greater trouble in operation and restart-up if an unplanned sudden power failure happens in the power plant.This study aims to explore the restart-up performance of a 170 t/h natural circulation CFB boiler after a sudden power failure and evaluate whether the hot restart-up can be realized.The heat transfer process after the restoration of the primary air is modeled.Validating the isothermal property of the bed after the restoration of the power and air supply,the correlation among the accident duration time,bed temperature after the power restores,inlet air temperature,air supply time,and air velocity is proposed.The predicted results indicate that the major influencing factor of the bed temperature during this process is primary air velocity.To provide guidelines for judgment on whether the hot restart-up can be realized,the maximum values of the air supply time and accident duration time for hot restart-up for different types of coals are given.The results show both of them have a rapid decrease as the coal ignition temperature in a CFB boiler increases from 450°C to 650°C,which also means that the coal ignition temperature plays a very important role in the restart-up process.Based on the simulation results of bed temperature during the accident,it is also estimated that the drum level drops by 77.7 mm after 8 hours but still stays in the permissible range.