Intelligent automation and IT for the optimization of renewable energy and wastewater treatment processes

Abstract

Background: Environmental systems often have a very complex structure. Methods from computational intelligence (CI) that are often inspired by nature can help to improve these systems. On the one hand, CI methods can be used for optimization; on the other hand, they can be used to extract information out of time series recorded from environmental systems. Methods: Methods from different fields of computational intelligence are investigated. Among them are supervised and unsupervised machine learning methods used for classification and cluster analysis, respectively. Furthermore, methods from evolutionary computation and multi-agent systems are used to develop control and optimization solutions for environmental processes. Results: In this paper, five applications in the fields of anaerobic digestion, pellet-heating, and wastewater management are studied. Using CI methods, e.g., biogas plant operation or a pellet-heating process can be optimized. Furthermore, important process variables can be obtained from huge measurement datasets that otherwise would be unanalyzed and therefore data cemeteries. Conclusions: The results reveal that using CI methods environmental processes can be improved in a favorable cost-benefit fashion.