Data-based and model-based blockage diagnosis for stacked microchemical processes

One of the critical problems in the operation of microchemical processes is blockage in microchannels. Therefore, a process monitoring system which can detect and diagnose blockage is indispensable for effective and stable operation of microchemical processes. In the present research, two types of diagnosis systems, a data-based blockage diagnosis system (DB-BDS) and a model-based blockage diagnosis system (MB-BDS), are proposed. To realize efficient diagnosis for various degrees of blockage, the proposed DB-BDS uses the ratios of temperature differences between normal and abnormal operating conditions at one sensor to those at the other sensor. On the other hand, MB-BDS, which uses simple physical models of the process, diagnoses blockage by calculating the correlation coefficients between the prepared temperature distribution vectors and the actual temperature distribution vector when blockage is detected. The performance of the proposed systems is evaluated with their applications to a stacked microreactor. DB-BDS and MB-BDS are applied to various types of blockage, i.e., various locations and degrees. The results show that both DB-BDS and MB-BDS can diagnose the blockage location successfully even when blockage degree is less than 10%.