TY - GEN
T1 - AI techniques for waste water treatment plant control case study
T2 - 11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007
AU - Sottara, Davide
AU - Luccarini, Luca
AU - Mello, Paola
PY - 2007
Y1 - 2007
N2 - We propose to show how different AI techniques might be used in the development of a modular expert system, acting as a manager and advisor for the operation of a pilot-scale SBR urban wastewater treatment plant, fed with real sewage. The plant's depurative effectiveness and global biomass' health depend on the reactions of nitrification and denitrification, with the former taking place as soon as the latter is complete. Since the duration of the reaction cannot be predicted, we have trained an intelligent software to recognize the event analyzing the profiles of some available signals, namely pH, orp and dissolved oxygen, thus allowing us to optimize the process' yield and detect possible failures. Using a SOM neural network, the system has been trained to remember an adequate set of reference signals, which have been given meaning using Bayesian belief techniques. Eventually, using the formalism provided by logical languages, reasoning capabilities have been imparted to the system, allowing the real-time, online deduction of new pieces of needed information. Thanks to the integration of these techniques the system is able to assess the status of the plant and act according to the adequate known policies.
AB - We propose to show how different AI techniques might be used in the development of a modular expert system, acting as a manager and advisor for the operation of a pilot-scale SBR urban wastewater treatment plant, fed with real sewage. The plant's depurative effectiveness and global biomass' health depend on the reactions of nitrification and denitrification, with the former taking place as soon as the latter is complete. Since the duration of the reaction cannot be predicted, we have trained an intelligent software to recognize the event analyzing the profiles of some available signals, namely pH, orp and dissolved oxygen, thus allowing us to optimize the process' yield and detect possible failures. Using a SOM neural network, the system has been trained to remember an adequate set of reference signals, which have been given meaning using Bayesian belief techniques. Eventually, using the formalism provided by logical languages, reasoning capabilities have been imparted to the system, allowing the real-time, online deduction of new pieces of needed information. Thanks to the integration of these techniques the system is able to assess the status of the plant and act according to the adequate known policies.
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U2 - 10.1007/978-3-540-74819-9_79
DO - 10.1007/978-3-540-74819-9_79
M3 - Conference contribution
AN - SCOPUS:38049117843
SN - 9783540748175
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 639
EP - 646
BT - Knowledge-Based Intelligent Information and Engineering Systems
PB - Springer Verlag
Y2 - 12 September 2007 through 14 September 2007
ER -