A Novel Approach to Manage Sewage Treatment Plants Based on Process Mining Technology

Abstract

Due to the complexity of the operation process in sewage treatment plants, there are numerous potential risks involved in the process. An appropriate business process model is necessary for effective staff management and risk detection. However, conventional modeling methods are inherently subjective in the field of sewage treatment. Designers not only have to grasp the workflow language but also need to be familiar with the whole business process. Compared to conventional data mining, process mining specializes in end-to-end processes. Consequently, process mining is better adapted to solving process problems. In this paper, a novel approach is proposed to analyze the operational risk of sewage treatment plants by using process mining technology. The ideal Petri net model and event logs are utilized for conformance checking. The results of the experiment indicate that the proposed approach can discover operational processes from event logs in the field of sewage treatment. The method can assist managers detect staff deviating from standard operating procedures. The results of the implemented process mining technology present the sewage treatment plant managers with a real analysis and understanding to make the staff’s operation easier. The methodology can be extended to similar scenarios.