1 / 18

Case study: composting Domestic Waste

Case study: composting Domestic Waste. C O M P O S T I N G. Waste. Influence of selective collections. Summary. Available data and simulation objective phase model Simulation of selective collections Influence on the composition of residual waste Simulation of Bio Reactor BRS

barney
Télécharger la présentation

Case study: composting Domestic Waste

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Case study: composting Domestic Waste C O M P O S T I N G Waste Influence of selective collections

  2. Summary • Available data and simulation objective • phase model • Simulation of selective collections • Influence on the composition of residual waste • Simulation of Bio Reactor BRS • Influence of selective collections on the composition of fermented waste

  3. Objective: Flowsheet optimization Collection of data on the operation of the existing process Data reconciliation Material Flow Analysis Set of coherent data for the complete flowsheet Simulator of actual situation Calibration of the simulation models Simulation of the operation based on scenarii (hypothesis on feed, equipment) Design of additional equipment Method

  4. Losses Overband DW > 150 mm Bio Reactor Iron < 150 mm > 18 mm < 18 mm Press Balistic separator Bales Composting plant Balistics Compost Composting plant Selective Collections Compost standard

  5. Available data and objective • > 20 mm : MODECOM categories on dry matter • 1 - Putrescibles, 2 - Papers, 3 - Cardboards, 4 - Complexes, 5 -Textiles, 6 - Sanitary textiles, 7 - Plastics, 8 - Combustibles, 9 -Glass, 10 - Metals, 11 - Incombustibles, 12 - Special waste • < 20 mm : analysis of inerts • MONS (Non Synthetic Organic Matter) + • Plastic Films + • Other plastics + • Metals + • Glass-Stones-Limestone (‘ Minerals ’) + • Wasted Mineral Matter < 2 mm (‘ Reste ’)= 100% • NB : The standard NF Compost Urbain for compost quality is based on inerts analysis

  6. Marque NF Available data Compost quality 3 3 Flowrate 3 3 Size distribution 3 > 100 mm 3 MODECOM Categories 100-20 mm < 20 mm 3 > 100 mm 3 Inerts Analysis 100-20 mm 3 < 20 mm 3 3 Heavy Metals per MODECOM categories 3 Heavy Metals per category of inerts Modelling the matter

  7. Phase model • 3 criterion • Particle size • Composition : • MODECOM + Description inerts analysis • User defined sub-populations 1 : • Heavy metals per MODECOM category • Hierarchy types • Component garde per size • Sub-pop. 1 per component • Chemical reactions • Transformation matrix MODECOM  Description inerts analysis

  8. Composition of Domestic waste

  9. Metals in categories

  10. Selective Collections

  11. BRS : Bio Reacteur Stabilisateur • Loss in organic matter: 19.95 % • No losses in heavy metals • Size reduction • Reactor Output

  12. Principal operations in BRS • A representation of unit • operations during • composting • Calibration of level 0 • models with coherent data Reactions Separator Mill

  13. Conversion

  14. Composting

  15. Mill

  16. Selective Collections

  17. Product composition

  18. Conclusion • Level 0 models allow to reach the objective of simulation • But, simulation results should be compared to reality to estimate their precision (non predictive simulation) • If validated models of level 1, 2 or 3 have been used, results should have been more reliable

More Related