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In recent years, the steel industry has significantly raised its demands regarding product quality, optimization of production cost, environmental issues and lead-time. The demand for improved production performance has in turn increased the demand on information systems, in particular highlighting the need for improved factory-and company-wide collaboration and information exchange. The heterogeneity in structure, technology and architecture of the information systems deployed in manufacturing plants presents further challenges to the design and implementation of a data exchange system for process optimization.

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Methods of computational intelligence (CI), especially fuzzy control and neuronal networks, are used for controlling and optimising of wastewater treatment plants. Areas of application are the control of sludge water dosage, of phosphate elimination by optimal precipitant dosage as well as an optimal aeration in the nitrification zone. In two municipal wastewater treatment plants with 60,000 and 12,600 person equivalents the controllers have been installed and optimised and they have been in operation for several years. Results of operation of the plants are presented in comparison to previously used classical control. Performance increased significantly and the outflow values could be kept securely below the government requirements without increase of the energy consumption. Peak loads in the inflow were eliminated in the plant and did not increase outflow concentrations. Results of operation for more than three years clearly show that the CI controller is a cost-efficient method for a sustainable rise of performance in municipal wastewater treatment plants.

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