Process Monitoring of Tubular Microreactors using Particle Filter

Abstract

Microreactors consist of tens to hundreds of micrometer-scale channels. The residence time of a fluid can be set exactly and backmixing can be minimized. Particularly, very short residence time can be achieved. In addition, it is possible to precisely control the reaction temperature due to large surface to volume ratio of channels. These features of microreactors make it possible to realize the production of specialty chemicals, which cannot be handled in conventional reactors. The most recognized problem in microreactors is channel blockage. The catalyst deterioration is also an inevitable problem for catalyst reactions in microreactors. To realize stable long-term operation of microreactors, it is necessary to detect such problems as early as possible. Since miniaturized sensors are expensive and their direct installation inside channels may disturb the flow, it is indispensable to develop a process monitoring system using a few indirect measurements. In this research, a state and parameter estimation system for tubular microreactors (TMRs) is developed to detect process faults. In the developed system, a process model is derived from the first-principle model of TMRs. Particle Filter (PF) or Extended Kalman Filter (EKF) or Ensemble Kalman Filter (EnKF) is designed to obtain the unknown parameters such as catalyst efficiency from a single wall temperature sensor. To achieve high estimation performance, the optimal sensor location is determined on the basis of the observability. The numerical examples illustrate that the blockage and the catalyst deterioration of TMRs can be detected more rapidly and accurately by using PF, as compared with EnKF and EKF.