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

https://doi.org/10.1016/j.jprocont.2003.12.007Get rights and content

Abstract

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 on-line 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.

Introduction

More than their control, the diagnosis (i.e., detection, isolation and analysis) of faults occurring in biological processes has become a challenging research area. Indeed, several types of disturbances like influence of the inoculate, contamination of the media, presence of toxic in the feeding line, fouling of sensors, can be present even in normal operational conditions. These disturbances can largely affect the process operation and damage the quality of the end products. Moreover, these disturbances can be either sudden or slow and they can be related to normal or faulty process operation provoking real or apparent deviations from the normal operation.

Hence, there is a clear need for advanced supervisory control (i.e., gathering on-line control and diagnosis) in order to keep the system performance as close as possible to optimal. This is particularly true for biological processes with environmental purposes like WasteWater Treatment Plants (WWTPs) where the state of the “living” part of the system is to be closely monitored together with large possible disturbances occurring on any part of the systems.

In the present study, anaerobic digestion has been chosen as an illustrative example of biological WWTPs. Anaerobic digestion (AD) is a serie of biological processes that take place in the absence of oxygen and by which organic matter is decomposed and converted into biogas, a mixture of mainly carbon dioxide and methane, microbial biomass and residual organic matter.

Several advantages are recognised to AD processes when used in WWTPs: high capacity to treat slowly degradable substrates at high concentrations, very low sludge production, potentiality for production of valuable intermediate metabolites, low energy requirements and possibility for energy recovery through methane combustion. AD is indeed one of the most promising options for delivery of alternative renewable energy carriers, such as hydrogen, through conversion of methane, direct production of hydrogen, or conversion of by-product streams.

However, despite of these large interests and more than 1400 commercial installations refereed world-wide in 1999 [1], many industries are still reluctant to use AD processes, probably because of the counterpart of their efficiency: they can become unstable under some circumstances. Hence, actual research aims not only to extend the potentialities of anaerobic digestion [2], but also to optimise AD processes and increase their robustness towards disturbances [3].

Throughout our studies, it has been experimentally demonstrated that AD processes could be very efficiently controlled despite large changes in the influent concentrations. Important organic loading rates (more than 100 kgCOD/m3 d) were indeed achieved while keeping the carbon removal above 75% [4]. Nearly perfect control of intermediate products (e.g., volatile fatty acids and alkalinity) or end products (e.g., residual pollution, total biogas produced or methane flowrate) was also possible over a long period of time while minimising the rejected pollution (expressed in terms of chemical oxygen demand at the output of the process). Several control approaches were used, each of them demonstrating several interesting characteristics for the control of AD processes [5]:

  • PID and fuzzy logic [6], [7], [8],

  • artificial neural networks [9],

  • non-parametric adaptive control [10],

  • adaptive control [11],

  • disturbance accommodating control [12],

  • interval based approaches [13],

  • robust output feedback [14], [15].


However, these studies were only devoted to lab or pilot scale digesters. In addition, it is important to note that all these control laws were to meet the specific objective they have been designed for. As a consequence, it is not to be expected that, for example, a control law will manage a technical problem in the feeding circuit (e.g., a clogging of a pipe) if its goal is to control volatile fatty acids in the output of the reactor. In addition––and since there does not exist an “universal” control law that could manage all the disturbances occurring on a process––it is mandatory to couple control laws with advanced diagnosis scheme. It is felt that it is the only possible way for achieving successful optimisation of AD processes at industrial scale.

In the past, we tackled these objectives using quantitative [16], [17] and qualitative [18], [19] model based diagnosis approaches, sometimes combining them together [20] or with process history based methods [21]. Nevertheless, it is our strong belief that a unified approach––based on evidence theory––could be of great help for overall optimisation of AD processes.

This paper will present this approach and is organised as follows. Next section details the diagnosis problem statement with specific emphasis on WWTPs in general and AD processes in particular. Then, our motivations are presented and the scientific basis of evidence theory is briefly underlined. Before concluding, experimental results obtained on a pilot scale fixed bed AD process are described and discussed.

Section snippets

Problem statement

From a very general prospect, timely detection, diagnosis and correction of abnormal conditions of faults in a process are the central components of abnormal event management.

There is an abundant literature on process fault diagnosis ranging from analytical methods to artificial intelligence and statistical approaches. From a modelling perspective, accurate quantitative, semi-quantitative or qualitative models can be required. At the other end of the spectrum, there are methods that do not

Key requirements for design of AD fault detection and diagnosis systems

To summarise, two key requirements are of prime importance for the diagnosis of AD processes: uncertainty management and modularity in the design of the diagnosis system. This allows one (i) to handle the poor knowledge available on-line about the internal functioning of the process (due to the lack of on-line sensors, to the simplicity of the models usable in a real-time context, to the large unmeasured disturbances occurring at the input, etc.) and (ii) to account for adaptativity, novelty

The evidence theory

Evidence theory, first introduced by Dempster and later formalised by Shafer, allows one to manipulate non-necessarily exclusive events and thus to represent explicitly process uncertainty [69]. This theory assumes the definition of (i) a frame of discernment Ω consisting of the exhaustive and exclusive hypothesis and (ii) the reference set 2Ω of all the disjunctions of the elements of Ω.

When used for diagnosis purposes, the frame of discernment includes singletons of all the possible faults

Results and discussion

The following illustrates the management of residual generation from fuzzy fault detection modules using the evidence theory. This is applied to the monitoring of a pilot scale AD process under small (i.e., hydraulic overload, low pH in the influent, low toxicant added) and large disturbances (i.e., inhibition of the biomass activity due to volatile fatty acids accumulation) occurring separately and sometimes simultaneously.

The next section presents the wastewater used and details the process

Conclusion

This paper presented the development of a modular diagnosis system using evidence theory. This approach gathers many advantages for the advanced supervision of biological wastewater treatment processes: robustness, novelty identifiability, adaptability, low modelling requirements, multiple fault identifiability.

Application has been made to a pilot scale fixed bed AD reactor and illustrated experimentally how different faults could be managed in a simple but efficient way. Moreover, several

Acknowledgements

This work has been carried out with the support of the European commission, Information Society Technologies programme (contract TELEMAC number IST-2000-28256). This information is provided under the sole responsibility of the authors and does not necessarily represent the opinion of the European Commission, which is not responsible for any use that might be made of it.

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