On-line diagnosis and uncertainty management using evidence theory––experimental illustration to anaerobic digestion processes

The on-line diagnosis is a key requirement in biological processes. This is particularly true in the case of wastewater treatment processes due to the composition of media, the requirements of operating conditions and the wide variety of possible disturbances that necessitate careful and constant monitoring of the processes. Moreover, because only partial information is available in an online context and because of the technical and biological complexities of the involved processes, specific characteristics are required for diagnosis purposes. Several approaches like quantitative model based, qualitative model based and process history based methods were applied over the years. This paper present a methodological framework based on evidence theory to manage the fault signals generated by conventional approaches (i.e., residuals from hardware and software redundancies, fuzzy logic based modules for process state assessment) and to account for uncertainty. The advantages of using evidence theory like modularity, detection of conflict and doubt in the information sources are illustrated with experimental results from a 1 m3 fixed bed anaerobic digestion process used for the treatment of industrial distillery wastewater.