Papers by Keyword: Thermal Conditioning

Abstract: Thermal conditioning (TC) can improve sludge mechanical dewaterability and drying performance, and produce solid fuel from sewage sludge with low-cost and harmlessness. This study investigates the influence of the TC on sludge combustion with thermogravemetric analysis of sewage sludge with/without TC. The experiments were carried out at a 150 mL/min air ambient with a heating rate of 10, 20, 30, 40, 50 °C/min. Tow integration approximation methods (Coats-Redfern and Flynn,-Wall-Ozawa) and a differential method (Kissinger method) were applied to study the kinetics. The Coats-Redfern method requires assuming the reaction’s kinetic mechanism. The calculated activation energy was lower than that from the Flynn-Wall-Ozawa and Kissinger, which were very close to each other. Among these methods, the Flynn-Wall-Ozawa can predict the energy required at every combustion stage, which therefore would be the best one to explore the combustion mechanism. The results show that the organic matters within sewage sludge are much more homogeneous after TC. The combustion of the thermal conditioning sludge (TCS) is much more stable with a 10% reduction in burnout time and a 9.94% reduction in combustion temperature range. The ignition temperature of the raw sludge keeps increasing from 188 to 224 °C with the heating rate, while that of the TCS is almost constant at 222-240 °C. The TC can improve the activation energy, which is about 144.52 and 123.00 kJ/mol for the raw sludge (RS) and TCS. Considering the gaseous pollutant emissions, the TC can dramatically reduce NO emissions, which is decreased from 14.22 to 6.59 mg/g by the TC, representing a reduction of 50.7%. Therefore, the TC can promote the hydrolysis of macromolecular organics, which would improve the sludge combustion performance, and reduce the gaseous pollutant emissions from combustion.

Abstract: This study aimed at developing an artificial neural network (ANN)-based temperature control method for the double skin envelope buildings. For this, control logic for opening conditions of the inner and outer surfaces’ openings as well as for cooling system’s operation was developed based on the predictive and adaptive ANN model. The parametrical optimization process for the structure and learning methods of the ANN model was conducted in terms of the number of hidden layers, the number of neurons in the hidden layers, learning rate, and moment. Then, the performance of this optimized model was tested using the similarity analysis between the predicted values from the ANN model and the measured values from the actual double skin envelope building. Analysis revealed that the developed ANN model proved its prediction accuracy and adaptability in terms of stable Root Mean Square (RMS) and Mean Square Error (MSE) values. Based on this finding, it can be concluded that the developed ANN model showed potentials to be successfully applied to the temperature controls for the double skin envelope buildings.

Abstract: The heat convection was the main heat exchange in the autoclave by which composite pressure vessel was cured. To determine the heat convection coefficient, the combination of theoretical calculation and temperature test is absolutely necessary. In the theoretical calculation, the determination of the heat convection coefficient is considered as an inversion problem of thermal conduction. By adjusting convection coefficient value in the finite element calculation, optimization method was employed to obtain a good agreement between calculated temperature and measured temperature. In the temperature test, the metallic liner of pressure vessel was used as test component to record temperature data which was compared with the calculated temperature. The results present the equivalent thermal boundary condition for the simulation of curing process of pressure vessel.