An approach to mechanistic event recognition applied on monitoring organic matter depletion in SBRs

Abstract

A fundamental practice in process engineering is monitoring the state dynamics of a system. Unfortunately, observability of some states is related to high costs, time, and efforts. The mechanistic event recognition (MER) aims to detect an event (defined as a change of the system with specific significance to the operation of the process) that cannot be directly observed but has some predictable effect on the dynamics of the systems. MER attempts to apply fault diagnosis techniques using mechanistic “recognition” models to describe the process. A systematic method for building recognition models using optimal experimental design tools is presented. As proof of concept, the MER approach to detect organic matter depletion in sequencing batch reactors, measuring only ammonia, dissolved oxygen, and nitroxides is applied. The event, that is, consumption of organic matter to a level below 50 gCOD/m3, was successfully detected even though microbial activity is known to continue after organic matter depletion

Anaerobic Digestion Model (AM2) for the Description of Biogas Processes at Dynamic Feedstock Loading Rates

“…In most cases, a combination of methods and case studies is required to achieve the most suitable result. Some examples of the applicability of model reduction in biological processes are available in the literature including its applications for advanced monitoring and optimization . However, the complexity of the biogas process and the concomitant limitation in sufficient data limits practical application.…”

Critical-to-Fault-Degradation Variable Analysis and Direction Extraction for Online Fault Prognostic

“…In addition, for the normal class, the corresponding discriminant components can be calculated, revealing the normal version of T f T n = X n W. (12) It is noted that the optimization objective expressed in (4) implies that the extracted components can be controlled by adjusting the weight parameter attached to the subobjective. If β = 0, only the between-class variations are being maximized.…”