Capability of Optical Sensors for Waste Water Quality Analysis in Food-Manufacturing

Transcription

1 Capability of Optical Sensors for Waste Water Quality Analysis in Food-Manufacturing Dr G. Sp. Skouteris Research Associate, Centre for SMART, Loughborough University

2 Contents Research Aim Current Status of Use of Water in Food Manufacturing Overview of Optical Instrumentation for Measurements of Water Content Description of Experimental Rig Analysis of Experimental Data Discussion and Conclusions

3 Research Aim Objective To prepare and characterise the capability of optical instrumentation for analysis of individual process effluents in-plant Questions What is the range of turbidities the instrument can handle? Can the instrument distinguish between turbid (apparent colour) samples and turbidity-free (true colour) samples?

4 Current Status of Water Use in Manufacturing Case Problems Manufacturers have little information on water use inside the factory Exceeding water discharge concerns Over/under use of fresh water, e.g. cleaning procedures Over/under use of cleaning chemicals Laboratory analysis of samples is too slow to be an effective management measure Action Investigation of the use of optical instrumentation to give in-line rapid analysis to address the problem

5 Measurements of Water Content Prototype optical instrumentation must involve the use of 1) multi-purpose 2) low-maintenance 3) minimally-invasive 4) broad-spectrum Production of the Digital Water Fingerprinting (DWF) of industrial water-using processes 5) low-cost measurements

6 Optical Instrumentation Commercially available optical sensor (Mettler Toledo Inpro 83 RAMS unit) that measures back-scattered light intensity (18 ) and light absorption optical sensor from a water sample at four different coloured LED sources (red, green, blue and infra-red).

7 Methodology Measurements were performed at a range of optical path lengths Question 1 Question 2 Test Fluid 1: Milk + Water A range of diluted samples were used for characterisation of the dynamic range and sensitivity of the equipment with respect to turbidity. Test Fluid 2: Industrial Process Water Filtered and unfiltered samples were used to study the capability of the instrumentation to distinguish between true (dissolved matter) and apparent colour (suspended + dissolved matter). Dilution Level: 32, 62.5, 125, 25, 5, 1 ml STOCK SOLUTION* L -1 * 16 ml of full fat milk L -1 Filtration: GE Healthcare Life Sciences, Whatman, CAT. No , Pore Size: 1.2 μm

8 Analysis Absorption: Beer-Lambert Law where: ln(i/i ) = -αl I : Intensity of light entering the sample I: Intensity of light leaving the sample l: Path length (mm) α: Absorption (Attenuation) coefficient What to determine: The effect of light absorption on a range of water samples What to assess: The α-value for the same range of water samples

9 ln(i/i ) ln(i/i ) Results: Milk + Water (1) Near Infra-Red (NIR) Red (R) ml y = -.4x +.11 R² = ml y = -.8x +.15 R² = ml y = -.17x +.74 R² = ml y = -.73x +.96 R² = ml y = -.279x R² = ml y = -.565x R² = ml y = -.7x -.8 R² = ml y = -.14x +.1 R² = ml y = -.53x R² = ml y = -.227x R² = ml y = -.516x R² = ml y = -.631x R² = ml MILK SOLUTION 62.5 ml MILK SOLUTION 125 ml MILK SOLUTION 25 ml MILK SOLUTION 5 ml MILK SOLUTION 1 ml MILK SOLUTION NIR: l vs ln(i/i ) 32 ml MILK SOLUTION 62.5 ml MILK SOLUTION 125 ml MILK SOLUTION 25 ml MILK SOLUTION 5 ml MILK SOLUTION 1 ml MILK SOLUTION R: l vs ln(i/i )

10 ln(i/i ) ln(i/i ) Results: Milk + Water (2) Green (G) Blue (B) ml y = -.63x R² = ml y = -.166x R² = ml y = -.35x +.17 R² = ml y = -.576x -.74 R² = ml y = -.826x R² = ml y = x R² = ml y = -.66x R² = ml y = -.177x R² = ml y = -.41x R² = ml y = -.633x R² = ml y = -.8x R² = ml y = x -.24 R² = ml MILK SOLUTION 62.5 ml MILK SOLUTION 125 ml MILK SOLUTION 25 ml MILK SOLUTION 5 ml MILK SOLUTION 1 ml MILK SOLUTION 32 ml MILK SOLUTION 62.5 ml MILK SOLUTION 125 ml MILK SOLUTION 25 ml MILK SOLUTION 5 ml MILK SOLUTION 1 ml MILK SOLUTION G: l vs ln(i/i ) B: l vs ln(i/i )

11 α-values Discussion and Conclusions: Milk + Water Concentration (L STOCK L -1 ) Summary of Data Turbidity (NTU) l vs ln(i/i ) α-values NIR R G B α-values vs Turbidity 1 2 Turbidity (NTU) NIR Red Green Blue α-values vs Turbidity Discussion Variation in absorption for turbidity values up to 17 NTU was successfully resolved by the instrument. For turbidity values >2 NTU, the absorption data fits the Beer-Lambert model well (R 2 >.9). The values of the attenuation coefficient were calculated from the fits for each dilution and colour. The variation in α-value seen with turbidity and colour of light will be subject to further investigation.

12 ln(i/i ) ln(i/i ) Results: Process Water (1) Green (G) - Before Filtration Green (G) - After Filtration A: y = -.1x -.13 R² =.7774 B: y = -.18x +.37 R² =.939 C: y = -.12x + 4E-5 R² =.8128 D: y = -.15x +.17 R² =.9189 E: y = -.5x +.9 R² = A: y = -.2x +.3 R² =.88 B: y = -.2x +.5 R² =.5681 C: y = -.3x +.29 R² =.676 D: y = -.2x +.5 R² =.6932 E: y = -.2x +.14 R² = A B C D E G (unfiltered): l vs ln(i/i ) A B C D E G (filtered): l vs ln(i/i )

13 Results: (Brickyard Process Water) (2) ln(i/i ) ln(i/i ) Blue (B) - Before Filtration Blue (B) - After Filtration A: y = -.12x +.7 R² =.9911 B: y = -.2x +.38 R² =.9833 C: y = -.15x +.18 R² =.9792 D: y = -.17x +.23 R² =.9799 E: y = -.5x -.6 R² = A: y = -.3x +.8 R² =.9559 B: y = -.2x +.5 R² =.8518 C: y = -.4x +.18 R² =.7589 D: y = -.3x +.8 R² =.7937 E: y = -.3x +.24 R² = A B C D E A B C D E B (unfiltered): l vs ln(i/i ) B (filtered): l vs ln(i/i )

14 Discussion and Conclusions (Process Water) Summary of Data Unfiltered Samples Filtered Samples Turbidity Colour α-values Turbidity Colour α-values (NTU) (Hz) G B (NTU) (Hz) G B Discussion The effect of filtering out suspended solids could be clearly identified in the absorption measurements at all colours, with the turbid unfiltered samples (apparent colour/total matter) exhibiting significantly higher α-values than the turbidity-free filtered (true colour/dissolved matter) samples. It was concluded that for these samples, true colour is mainly due to coloured dissolved organic matter from clay, which strongly absorbs short wavelengths (i.e. visible-g and -B). Consequently for very low turbidity (<15 NTU), α-values can be better resolved at visible-g and -B because of the reduced effect of noise in the measurements.

15 Thank you for your attention!