A petri nets‐based process planning system for wastewater treatment

Abstract

It is always challenging to simulate, debug or diagnose automated systems. The aim of this paper is to present the development of a convenient tool for reengineering the control system in a complicated industrial wastewater treatment plant. In this project, a PC-based Human-Machine Interface (HMI) in conjunction with Petri nets (PN) theory is adopted to develop and simulate the operational process for wastewater treatment. The resultant tool offers many advantages to the reality of the automated control world. It not only reduces the process reengineering time and the cost of error recovery, but also builds a panel of human interface for the process. The discrete event control sequence of wastewater treatment can be easily modeled and evaluated before its build-up. Furthermore, this PN-based system can be used as an online diagnostic tool when the wastewater treatment process is malfunctioning. That is, the presented PN tool provides an adequate means for offline process development, simulation, performance evaluation, and quick online process diagnosis.

Formal Design and Analysis of a Wastewater Treatment Control System based on Petri Net

“…A more relevant work in terms of control model is presented in [14] where the conventional Petri Net method is adopted to build a simulation tool for online diagnostic on Formosa wastewater treatment plant. However, in terms of modeling, logical model verification and design evaluation was not dealt yet.…”

Elementary‐Siphon‐Based Control Policy for Flexible Manufacturing Systems with Partial Observability and Controllability of Transitions

“…In the real world, there are a variety of DES such as flexible manufacturing systems (FMS), computer networks, traffic systems, and workflow systems. In recent years, researchers have been increasingly interested in the control of DES that are modeled with Petri nets in many areas: fault tolerant control , workflow systems , traffic systems , web services , robotics , power systems , wastewater treatment , energy recovery , artificial neural networks , automated guided vehicles , oil refinery processes (although it involves continuous process industry, it contains discrete‐event variables and belongs to a hybrid system), and control of manufacturing systems .…”