Identification of methods for diagnosis, prognosis and correction of non-nominal operating conditions in biomass-based combustion systems

Project Objectives

The abridged project IdDiaPro searches for ways to diagnose and prognose plant-related or combustion-related, non-nominal operating states  by

(i) model-based analysis methods,

(ii) signal-based analysis methods,

(iii) machine learning methods

and possible combinations of them. The result will be a feasibility study, which includes a review of the methods and their evaluation regarding the implementation in commercial systems during a potential follow-up project.

EIFER’s Contribution

EIFER contributes to the project with its expertise in wood combustion plants for heat production and in signal-based, system-independent diagnosis/prognosis algorithms. The core contributions are:

  • Identification of methods for plant-related problems
  • Application of signal-based methods for detection of non-nominal states* and diagnosis
  • Exploitation of the results

*e.g. more than the triple standard deviation (based on the signal noise) from the system  behavior defined by the system producer

This project receives funding from Federal Ministry for Economic Affairs and Energy under the ID 03EI5425.