Generalised and instance-specific modelling for biological systems
Introduction
Biological, ecological and environmental (BEE) processes are becoming more and more important in policy- and decision-making. The recent Kyoto summit has defined strategic targets to be realised by countries world-wide. On a smaller scale, regional officials have to predict, monitor and control environmental phenomena such as smog, manure, eutrophication and acid rain on local ecosystems, while on a micro-scale, such as waste- and surface water systems, operators have to maintain operational set-points by controlling complex processes. Moreover, biological processes are more and more applied on an industrial scale.
Decision and policy quality strongly depend on model validity. The complexity of BEE systems complicates the development of reliable models for use in the real world. This often leads to the deployment of models that are simplified and stripped versions of the models that would be required. Artificial intelligence techniques can be used to improve the modelling of BEE processes.
In the next section we explore problems in traditional modelling approaches for the complex systems that are found in natural and artificial biological systems. Then, in Section 3, we discuss differences between generalised and instance-specific models, and present two artificial intelligence approaches for these kinds of problems. Section 4presents for both types of solutions an example, as implemented in an activated sludge waste water system. Section 5concludes by discussing these approaches, and generalising the concepts to biological, ecological and environmental systems in general.
Section snippets
Context
Modelling BEE systems introduces a number of specific problems. A model is a representation of a (real-world) entity that mimics some aspects of the behaviour of the referent entity. Realistic systems consist of many individual entities of different kinds, each with their own behaviour. Growth, decline and extinction behaviour of organisms, as well as the behaviour of large numbers of biological processes, depend on many variables that show autonomous dynamics. Even for describing simple
AI modelling techniques
In policy and decision making models help to optimise the decision quality. The models are typically used for simulation and optimisation. Models may play different roles, depending of the way of modelling. For our paper the difference between a generalised model and an instance-specific model is important. The former employs techniques known as compositional modelling (Section 3.1), the latter are realised through case based reasoning (Section 3.2).
Case studies
This section presents two case studies in the domain of waste water treatment. In Section 4.1we give a short introduction of this domain. Section 4.2presents an overview of the WaterCIME project. In this project, the modelling concepts presented in this paper have been developed and field tested in two applications. These applications are presented in Section 4.3Section 4.4.
Conclusions
Ecological systems require flexible approaches for modelling and control to cope with typical aspects of biological systems:
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complexity
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process dynamics
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process evolution
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the possibility to have different views on one process.
Acknowledgements
The authors thank the WaterCIME partners for their close co-operation during the project, and the EU for supporting this research. Special thanks to Thorsten Abels and Peter Dannenmann (both from DASA-RI) and Christoph Bernatzky (of BEB).
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