Introduction of a new alien marine species somewhere in the world every 9 weeks

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1 Modelling, simulation and optimisation of an onboard ballast water treatment system Kayvan Pazouki, Ehsan Mesbahi, S. R. Moloney, K. J. Carney, J.E. Delany F. Yonsel, C. Bilgin

2 Content Background Ballast Water Management Modelling Approach for Ballast water Treatment System Experiment Setup Results of Model Proposed biological model Discussion

3 Ballast water Ecological Problem species carried out per day in the World Movement of 12 billion tonnes of ballast water Introduction of a new alien marine species somewhere in the world every 9 weeks

4 Some of the proposed techniques Ballast Water Management Onboard Treatment Primary Treatment Secondary Treatment Filtration Cyclonic separation Chemical Biocide Chlorine H 2 O 2 Ozone Organic agents Others Physical UV Irradiation Deoxygenation Heat treatment Ultasounds Cavitation Magnetic/ Electric field

5 BaWaPla Treatment System Chlorine, at early stage, has shown potential as ballast water treatment system Chlorine can be generated by electrolysing saline/sea water Generated chlorine is subsequently added to ballast water intake flow No onboard storage of chlorine

6 System s Approval The IMO Guidelines for Approval of Ballast Water Management System (G8), address both land-based and shipboard testing procedures and requirements. The Administration decides the sequence of landbased and shipboard testing. A successful laboratory scale ballast water treatment system requires up-scaling to meet requirements of G8 and achieve Administration s approval.

7 System Modelling The gap between laboratory scale to land- based/shipboard size treatment system could be bridged by: Series of costly and time consuming setup, or Developing an intelligent model to mimic the system s behavior and identify key parameters. Modelling can additionally provide: Experiment Shipboard & Lab Inherent correlation Intelligent between input and output parameters Setup land-based scale Model Influencing parameters used for both operational scale and physical optimisation IMO s G8 Requirements

8 Modelling Techniques Different modelling techniques are available Inherent features and complexity of a system are deciding factors Chemical/ Physical Models Data-Driven Models A Process Thermodynamic Models Physical understanding of system / process is required to develop a model Hydraulic Models

9 Modelling Difficulties in modelling a ballast water treatment system: Many input & output parameters Non linear relationships, Inter-coupled parameters Lack of sufficient data Output of one subsystem becomes the input to another

10 Data-Driven Models Since there is no clear relationship between system parameters which could be physically explained, Data- Driven modelling techniques provide the only way forward Data-Driven models: Least Mean Square Regression Analysis Intelligent Algorithm Artificial Neural Networks Fuzzy Logic

11 Artificial Neural Networks An Artificial Neural Networks (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as brain, process information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.

12 Artificial Neural Networks Inputs Outputs y m sig h sig = n i= 1 j= 1 x 1 sig( x) = 1 + e j W x 1 i,j + c i W 2 m,i + b m

13 Electrolyzing Generator Current Current De-Ionized Salinity Water Model of Electrolyzing Generator Sea salt Total Chlorine AnofluidFree Hypochloric Chlorine acid rich Chloride The functional relationship to define the quantities of output will be: De-ionized (TC, FC, Water CH) + = Sea f (Salinity, salt = Current) Saline solution

14 Experiment Setup Independent Variables: Salinity Current Dependent Variables: Total Chlorine Free Chlorine Chloride Data generation: Four different salinities (8.5 to 39.0 ) Three different current (8, 12 & 16 Amp) Each produced Anofluid tested for constituents

15 Experiment Results Saline pump Speed % Amp. Chlorine (total) mg/l Chlorine (free) mg/l Chloride mg/l

16 Modelling Procedure 10 sets of data selected for training A three layer feed-forward forward ANN model with 4 neurons in hidden layer was chosen for training 2 other sets of data used for testing the ANN model The errors of test data are acceptable Saline pump Speed % INPUT Experimental results ANN Prediction Chlorine (total) mg/l Chlorine (free) mg/l Chlorine (total) mg/l Output Chlorine (free) mg/l Output Chloride mg/l Output Chloride Amp. mg/l

17 Anofluid Constituents Performance Map Total Free Chloride Chlorine versus versus Current Current and and Saline Saline Pump Pump Speed Speed % %

18 Anofluid Biological Effectiveness Model Experiment setup: Anofluid added at different dosage (0.1, 0.2, 1%) Allocated residence time (zero as far as feasible) Biological Result: Mortality rate was not significant especially at low dosage rate Subsequent tests with increased residence time were carried out and higher mortality rate were observed.

19 Proposed biological model % of Chlorine Residence Time Number of Species Biological Model Zooplankton mortality rate Phytoplankton mortality rate

20 Discussion & Future Work Mathematical modelling of BW management systems can provide invaluable insight into systems performance leading to successful optimisation and up-scaling Intelligent algorithm has proved to be a powerful modelling tool for performance prediction of highly non-linear and non-physical BW Management systems A mathematically based biological model to predict system s biological effectiveness, will be developed in the near future. It is imperative that biological data is mathematically defined, when using proposed modelling technique.

21 Acknowledgment Authors would like to thank all partners of European Project BaWaPla for their valuable discussions and support. Special thank to Mr. Michael Laverty and Mr. Frank Voigtlaender from LVPG International This research has been funded by the European Union under contract number , which commenced 15/11/2006, within the project BaWaPla Sustainable Ballast Water Management Plant.

22 Thank you! Contact: Kayvan Pazouki Ehsan Mesbahi