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Biological wastewater treatment is a promising technology to be applied to adequate wastewaters arising at steel production plants. However, only a small number of applications can be found throughout the European iron and steel industry, Therefore, methodologies for controlled biological treatment of wastewater arising from steel producing processes have been assessed and developed. In particular, an operational concept has been developed for a degreasing bath wastewater at a stainless steel pickling and annealing line. A second example is the controlled biological treatment of a degreasing bath wastewater of an electrical steel pickling section. A third concept was set up for the treatment of wastewater arising from two different coking plants. The results were obtained by iterative model development and accompanying experiments performed at different scales, starting from laboratory scale via technical scale up to operational scale. Development of models and control concepts was done using activated sludge models (ASM) known from municipal wastewater treatment. In the cases of stainless steel production and coking plant wastewaters, it was feasible to use modifications or simplifications of ASM model No 1 (ASM1). The developments for electrical steel production wastewater, however, required artificial neural network (ANN) modelling due to poor performance of the ASM models (ASM 1 and ASM2) concerning the occurrence of phosphate compounds. In addition, a graphical user interface (GUI) was developed to allow easy simulation of ASM models.

Additional information

Authors: SCHMERMBECK H, BFI, Düsseldorf (DE);KOZARISZCZUK M, BFI, Düsseldorf (DE);SCHMIDT B, BFI, Düsseldorf (DE);MUND P, ThyssenKrupp Nirosta, Düsseldorf (DE);MIRABILE D, CSM, Rome (IT);MARAÑÓN E, Universidad de Oviedo, Gijón (ES);MICHELIS P, IMMG, Pentili (GR)
Bibliographic Reference: EUR 21905 EN (2006), 212 pp. Euro: 35
Availability: Katalogue Number: KI-NA-21905-EN-S The paper version can be ordered online and the PDF version downloaded at:
ISBN: ISBN: 92-79-00679-7
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