TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

Proposed set up

Automatic assessment of biological treatability of textile wastewaters

using a neural network

Lettinga Associates Foundation

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

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”

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

Automation

• Automatic decision stream is biologically treatable or not

• Using data from respirometric (BODst) and infrared measurements (COD)

• COD/BOD indicates treatability

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

Schematic

Respirometer

IR meter

Decision fortreatment

option

COD

Respirogram

CODBOD

Extradata

BOD(st)

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

0

10

20

30

40

-10 10 30 50t (min)

R (

mg/lh

)Respirogram analysis - Human

• Let’s see:1) height2) end length3) area4) basic rate OK?normal shape?

1

23

4

• Easy!

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

0

10

20

30

40

-10 10 30 50t (min)

R (

mg/lh

)

3

Respirogram analysis - Computer

• Let’s see:1) height2) end length3) area4) basic rate OK?normal shape?

1

2 2 2

4

• Quite difficult!

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

In practice: more possible shapes

• Dilemma:

The end of the respirogram

0

10

20

30

40

50

-10 10 30 50 70 90t (min)

R

?

?

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

0

5

10

15

20

-10 10 30 50 70 90(min)

R

In practice: more shapes

• Dilemma:

Basic rate can change during measurement

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

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

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

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

TOWEF0 - Paris - October 2003 Lettinga Associates Foundation

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