Investigation on Industrial Heat Transfer Fluids Degradation and Prediction of Their Physical Properties Using a Smart Technique

Heat Transfer Fluids (HTFs) are extensively utilized in industry where direct heating by a naked flame is not practical. Historically, steam has been favoured method because its main advantages are the absence of environmental issues and the low cost of water. However, the steam utilization demands the complex system such as the use of chemical additions, demineralised water, safety valves, drains, traps and blow downs. If the HTF systems are designed properly, it is safer and less problematic. Moreover, HTFs are far better than steam as they are able to operate at high temperature and do not require high pressure system. Since the majority of the industrial processes are operated under high temperature, it signifies the importance of the utilization of HTFs in the industrial applications. In this project, the main objectives are to investigate the parameters affecting the degradation of the HTFs, which are commonly used in industry, to predict the physical properties of the fluids using a smart technique as artificial neural network (ANN), to conclude the degradation rate of one type of HTF, and to give some suggestions to lengthen the lifetime of the commonly used HTFs in industry based on the analysis of the results and the investigation fulfilled. ANN is an efficient tool which is widely utilized to analyse the process systems. The results imply that the causes of HTFs degradation are thermal cracking, oxidation and contamination. The results also reveal that ANN has been successfully able to predict HTFs properties with relative percent error as much as 0.262%. Moreover, the degradation rate derived can be utilized to analyse the performance of the HTF system for the next period of operation, and the suggestions provided can be effectively applied for the industrial systems to improve their efficiency and to increase the HTFs lifetime.