AI-based support for experimentation in an environmental biotechnological process

This paper presents an AI-based system that supports experimentation and control in the domain of environmental biotechnology. The objective of the experiments is to verify hypotheses on biostimulation of an activated sludge by sustaining oscillations in its metabolism to improve degradation of a hardly removable organic waste in the wastewater treatment plants. The presented system incorporates the application of a multi agent system (MAS), which uses ontologies and rules, and also a smart image processing method. One of the main tasks of the MAS is to provide a support for analysis of the off-line microscopic measurements based on both the rules describing the trends of analytical measurements and the quantitative on-line microscopic observations. Finally, the proposed MAS may keep track of results provided by the experts with results obtained on the basis of rules. As a result, the appropriate biostimulation control may prevent or reduce the climate changes.