A software platform for process monitoring: Applications to water treatment

Effective online monitoring of the performance of water treatment is critical. Industrial wastewater treatment, for example, has to confront important challenges concerning both cost management of treatment plants and fulfillment of regulations. Moreover, when drinking water is produced, water safety is an important consideration, and possible changes in water quality should be detected as soon as possible. On the other hand, the large amount of measurement data available creates its own challenges for water treatment processes, which means that advanced tools for monitoring, analysis, and control are often required. Therefore, it would be useful to have a monitoring system which is able to handle all available measurements and present the available information in a simple, user-friendly and flexible manner. In this paper, we introduce a piece of software which can be utilized in the monitoring of water treatment processes. The structure of the software is designed so that it can be easily modified according to the user's needs. The system is demonstrated using process measurements from an activated sludge treatment plant, which is part of a pulp and paper plant, and from a pilot treatment plant for producing drinking water.

[1]  Mika Liukkonen,et al.  Dynamic soft sensors for NOx emissions in a circulating fluidized bed boiler , 2012 .

[2]  Mika Liukkonen,et al.  Dynamic soft sensors for detecting factors affecting turbidity in drinking water , 2013 .

[3]  B. Holenda,et al.  Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control , 2008, Comput. Chem. Eng..

[4]  Mika Liukkonen,et al.  Modeling of the fluidized bed combustion process and NOx emissions using self-organizing maps: An application to the diagnosis of process states , 2011, Environ. Model. Softw..

[5]  M. Lehtola,et al.  Characterization of Alum Floc by Image Analysis in Water Treatment Processes , 2012 .

[6]  M. Heikkinen,et al.  PROCESS STATES AND THEIR SUBMODELS USING SELF-ORGANIZING MAPS IN AN ACTIVATED SLUDGE TREATMENT PLANT , 2008 .

[7]  Holger R. Maier,et al.  Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters , 2004, Environ. Model. Softw..

[8]  Thierry Denoeux,et al.  A neural network-based software sensor for coagulation control in a water treatment plant , 2001, Intell. Data Anal..

[9]  Mika Liukkonen,et al.  Expert system for analysis of quality in production of electronics , 2011, Expert Syst. Appl..

[10]  Mika Liukkonen,et al.  Modelling of Water Quality: An Application to a Water Treatment Process , 2012, Appl. Comput. Intell. Soft Comput..

[11]  Mika Liukkonen,et al.  A modelling and optimization system for fluidized bed power plants , 2009, Expert Syst. Appl..

[12]  Mika Liukkonen,et al.  Subtraction analysis based on self-organizing maps for an industrial wastewater treatment process , 2011, Math. Comput. Simul..

[13]  Mika Liukkonen,et al.  Artificial neural networks for analysis of process states in fluidized bed combustion , 2011 .

[14]  Fourth Edition.  Guidelines for Drinking-water Quality, Fourth Edition , 2011 .

[15]  Kauko Leiviskä,et al.  Calibration and validation of a modified ASM1 using long‐term simulation of a full‐scale pulp mill wastewater treatment plant , 2010, Environmental technology.

[16]  L Rieger,et al.  Computer-aided monitoring and operation of continuous measuring devices. , 2004, Water science and technology : a journal of the International Association on Water Pollution Research.

[17]  Larry W. Mays.  Water Supply Systems Security , 2004 .

[18]  Esko Juuso.  Intelligent Trend Indices in Detecting Changes of Operating Conditions , 2011, 2011 UkSim 13th International Conference on Computer Modelling and Simulation.

[19]  Kesheng Wang,et al.  Applying data mining to manufacturing: the nature and implications , 2007, J. Intell. Manuf..

[20]  Paolo Giudici,et al.  Applied Data Mining for Business and Industry , 2009 .

[21]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[22]  T. Viraraghavan,et al.  Treatment of pulp and paper mill wastewater--a review. , 2004, The Science of the total environment.

[23]  Barry Lennox,et al.  Model predictive control of an activated sludge process: A case study , 2011 .

[24]  Kauko Leiviskä,et al.  Application of evolutionary optimisers in data-based calibration of Activated Sludge Models , 2012, Expert Syst. Appl..

[25]  Raymond D. Letterman,et al.  Water quality and treatment : a handbook of community water supplies , 1999 .