1. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 Proposed set up Automatic assessment of biological treatability of textile wastewaters using a neural network Lettinga Associates Foundation

2. TOWEF0 - Paris - October 2003 About this work Extra work based on finished WP’s: –WP05.02.5 (Development of on-line wastewater characterisation techniques) –WP05.04.05 (Respirometric on-line tests)  implementation of the on-line respirometric technique: “Protocol WW Design i.t.o. treatability”

3. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 Automation Automatic decision stream is biologically treatable or not Using data from respirometric (BOD st ) and infrared measurements (COD) COD/BOD indicates treatability

4. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 Data required to take decision End-point, length and height of the respirogram Area under respirogram -> Short term BOD Shape of respirogram COD value from infrared device

5. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 Schematic Respirometer IR meter Decision for treatment option COD Respiro gram COD BOD Extra data BOD(st)

6. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 IR value Presented at meeting in Como Use of infrared spectroscopy to measure COD on-line

7. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 Respirogram analysis - Human Let’s see: 1) height 2) end  length 3) area 4) basic rate OK? normal shape? 1 2 3 4 Easy!

8. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 3 Respirogram analysis - Computer Let’s see: 1) height 2) end  length 3) area 4) basic rate OK? normal shape? 1 2 2 2 4 Quite difficult!

9. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 In practice: more possible shapes Dilemma: The end of the respirogram ? ?

10. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 In practice: more shapes Dilemma: Basic rate can change during measurement

11. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 In practice: more shapes Dilemma: Time can be a problem End?

12. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 In practice A computer model is not intelligent enough to analyse “non-perfect” respirograms Experience so far with numerical methods and computer models was not satisfying New direction: Neural network

13. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 Neural network (NNW) Basic difference with “normal” model: –It can work with similarities instead of just “identical” and “different” Based on data training sets, NNW can learn and give the most probable outcome In this case, the NNW should be trained for each different wastewater type

14. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 NNW can learn (e.g) To ignore shoulders and take the real end (for samples that always show shoulders) To decide where the endpoint is (for samples that have a long and “flat” ending)

15. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 Work involved Building of neural network (NNW) Recording of respirograms to test computer model with similar yet different effluents Training the NNW to recognise respirogram characteristics and calculate BOD(st) Measuring “infrared COD” for same samples

16. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 Respirograms Respirograms have been recorded with I09 samples from five different acid dyeing wastewaters (dyebaths and rinsing steps) In case more are needed to test the model, old respirograms may be used –Neural network needs the shapes to be similar for successful training

17. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 IR-measurements Samples are being analysed by INRA Narbonne Results will be sent back to Wageningen

18. Lettinga Associates Foundation TOWEF0 - Paris - October 2003 Neural network Software: Matlab Input: –Similar respirograms –Infrared COD data Output for any respirogram: –COD/BOD value –List of characteristics (height, length,...)