New MSW sampling and characterization methodologies

Transcription

1 RELIABLE DATA FOR WASTE MANAGEMENT September 25-26, 2008, Vienna, Austria New MSW sampling and characterization methodologies The dry product method Ph. WAVRER - BRGM BRGM, Orléans - FRANCE

2 MSW characterization in France A little page of history 1993: MODECOM (French MSW characterization methodology). Characterization made on the collection vehicle. Mass of sample: 500 kg. 1993: First national campaign of MSW characterization in France, based upon MODECOM : Development of selective collection schedules. 1997: Adaptation of the MODECOM methodology for the selective collection. > 2

3 MODECOM sampling & sorting operations Collection vehicle Lot 10 x 50 kg 500 kg MSW Sample to be sorted SCRE EENING SO ORTING Coarse (> 100 mm) Sorted in integrality Categories Medium ( mm) Quartering 1/8 sorted Sub-categories Fines (8-20 mm) Unsorted (< 8 mm) Optional ANALYSES (Moisture content, LOI, heavy metals, Heating Value, organic matter, etc ) > 3

4 2 French AFNOR standards Derived from the MODECOM methodology NF X30-413: Constitution of a sample of household waste contained in a waste collection vehicle Rules for sampling MSW from a collection vehicle. Sampling of 500 kg formed by ten 50 kg increments. Random sampling. NF X30-408: Characterization of a sample of household related waste Rules for characterization of MSW. Characterization made on wet (raw) material. Screening with a double-screen (20 & 100 mm) sorting table. Quartering of the mm fraction. Etc. > 4

5 Screening with a screen sorting table > Screening on wet material carried out within 24 hours after sample constitution > 100 & 20 mm round mesh > In option, 8 mm round mesh > 5

6 Some problems with MODECOM > Moisture contents measurement For each category (after sorting): take «approximately» 2/3 of the mass to be dried from the «coarse» fraction and 1/3 from the «medium» fraction. Low precision for the moisture contents value > Working on raw (wet) material Sanitary and security hazards for the operators: Unsatisfactory working conditions for optimal sorting results > 6

7 Some problems with MODECOM > Screening Biased results for the screening operation: fine particles remain stuck on coarse ones. Screening depends strongly on the operator care or skill: poor reproducibility of the screening operation. > Categories/Sub-categories distribution Biased results: fines particles sticking problem. Possibility of sorting errors (poor working conditions). > Difficulty to use characterization results for some studies For example: expert evaluation of a plant, in relation with fines particles (composting plant, MBT, ) Problem with material mass balance > 7

8 Dry product method characterization > Based upon previous Cemagref studies (B. Morvan) > Take into account previous MODECOM experience and data In order to minimize mass to be sorted without decrease the accuracy of fthe results. > Main objectives: To reduce bias observed at different stages. To improve reproducibility. To minimize i i sanitary hazards for the operator. To obtain usable data for expert evaluation of a plant. > 8

9 Dry product method characterization Constitution of the 500 kg MSW sample AFNOR X C Drying Opening all garbage bags Quartering 1/4 1/4 1/4 1/4 Sample to be sorted ~130kg Oddbits ~ 30kg Sorted in integrality Total moisture content Screening Coarse >100 mm Medium mm Fines 8-20 mm <8 mm Sortin ng Sorted in integrality 5kg sorted 500 g (in option) Unsorted Categories Sub-categories Analyses > 9

10 «Oddbits» > Element carrying heterogeneity because of its: Size, Weight, Composition, Etc. For example: a big cardboard box, a big shoe, a microwave oven, paint cans, > «Oddbits» are fully sorted from the 500 kg MSW sample. Their composition will be re-integrated into the final MSW sample composition > The remainder of the MSW sample (without «oddbits») is quartered in order to be scaled down to about ¼ of its initial mass > 10

11 Quartering of the sample > Objective: to divide the initial MSW sample (without «oddbits») while maintaining proper representativity > Based upon the «alternate shovel» method ¼ of the initial sample is kept for the next characterization ti steps ¾ are discarded after weighing > 11

12 Drying of the entire sample > «Oddbits» and quartered sample are dried during five days at 70 C Result: Total moisture content of the MSW sample But: Moisture contents data about categories (or sub-categories) are lost > 12

13 Screening of the dried sample > Use of a trommel with successively 100, 20 and (in option) 8 mm round mesh Reproducibility of the screening method The fine particles problem is solved when screening dried material Screening no longer depends on the operator: improved reproducibility No bias for the particle size distribution > 13

14 Sorting on dry material > According to AFNOR X standard categories and sub-categories (revised in 2007) : The integrality of «oddbits» and coarse elements (>100 mm) are sorted. After sub-sampling, about 5 kg of medium elements ( mm) are sorted. In option, sub-sample and sort about 500 g of fine elements (8-20 mm). Elements <8 mm are not sorted. > From screening/sorting results and sampling ratios, MSW composition on dry matter > 14

15 Relation between wet and dry composition > MODECOM : Moisture contents per category MSW composition on wet matter > Dry product method: Global moisture contents MSW composition on dry matter To compare previous and new data > Mean category humidity data base > Correspondence table (ADEME-Cemagref study) > 15

16 Dry method benefits >Clean The operator does not touch raw (wet) waste before drying. Drying allows to reduce bad smells. Working conditions are safer and more comfortable. >Accurate Sorting errors are less frequent while working on dry material. Systematic bias due to the fine particle sticking problem strongly decreases (for instance, 20% fine particles are stuck on wet plastic films against only 1% on dry ones). The fine particles (<20 mm) content becomes representative when using a trommel for screening. >Faster thus less expensive The mass of sample to sort is lower, saving labour costs. Results are independent of the operators > 16

17 MODECOM /dry method comparison results % sur sec % on dry mass Niort Nantes Rennes Sables O X X X X X X X X Putrescible Putrescibles 8,2 3,5 13,0 4,2 11,2 3,7 12,9 5,4 Paper Papiers 15,5 15,0 19,2 19,1 12,1 11,4 13,9 14,1 Cardboard Carton 8,4 8,6 6,8 7,1 6,7 7,1 6,6 6,6 Composite Complexes 4,8 3,9 1,8 1,2 2,4 1,7 3,8 2,9 Textile Textiles 3,7 3,6 0,3 0,3 0,9 1,0 4,8 5,0 Sanitary Text textile sanitaires 5,2 7,1 7,0 6,9 4,8 4,9 5,6 5,7 Plastic Plastiques 18,2 15,9 18,3 16,3 15,2 12,2 19,9 18,1 Combustibles 8,5 5,9 3,2 2,9 6,2 6,3 6,8 6,4 Glass Verre 3,8 3,3 7,3 7,0 8,3 7,9 5,1 4,7 Metal Métaux 10,4 9,6 3,8 3,7 3,1 2,7 2,7 2,6 Un-combustible Incombustibles 1,3 1,4 4,5 4,9 12,6 9,1 7,6 5,7 Special DMSwaste 0,8 1,1 0,8 0,8 1,0 0,7 0,4 0,4 Fine <20mm 11,2 20,9 13,9 25,8 15,6 31,3 9,9 22,6 Sources: An accurate comparison of results between methods is difficult: MODECOM strongly depends on the operators Dry method is independent of the operators > 17

18 Outcome > A new French AFNOR standard: X Household and related refuse Characterization method Dry product analysis > Method used for the new French national campaign of MSW characterization acte at Ademe study - in progress. > Used for plant expert evaluations Composting plants. MBT plants. Etc. No more problem with material balance > 18

19 Outside France > Review study Methods for household waste composition studies (Dahlén & Lagerkvist, 2008) Over 20 methods for waste sampling and analysis all on manual sorting of wet waste > Validated standards: ASTM D (2003) Standard Test Method for Determination of the Composition of Unprocessed Municipal Solid Waste Sorting on wet waste > European approach: the SWA tool No practical application yet? > 19

20 Thank you! > 20

21 Sampling errors, from theory > Theory of sampling and sources of error (Gy, 2003) 1. Long-range heterogeneity fluctuation error 2. Periodic heterogeneity fluctuation error 3. Fundamental error 4. Grouping and segregation g error 5. Increment delimitation error 6. Increment extraction error 7. Preparation error > 21