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INTEGRATED PROCESSES FOR TREATMENT OF BERKELEY PIT WATER

INTEGRATED PROCESSES FOR TREATMENT OF BERKELEY PIT WATER. ACTIVITY III, PROJECT 21. BACKGROUND. The Berkeley Pit (Butte, Montana) - is currently filling at a rate of 3 million gallons per day of acidic, metals laden water

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INTEGRATED PROCESSES FOR TREATMENT OF BERKELEY PIT WATER

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  1. INTEGRATED PROCESSES FOR TREATMENT OF BERKELEY PIT WATER ACTIVITY III, PROJECT 21

  2. BACKGROUND • The Berkeley Pit (Butte, Montana) - is currently filling at a rate of 3 million gallons per day of acidic, metals laden water • EPA issued a Record of Decision in 1994; the Berkeley Pit will be allowed to fill until approximately 2021, at which time the water level will approach the Critical Water Level

  3. BACKGROUND (cont.) • Treatment technologies will be revisited approximately 2009; treatment required essentially forever • ROD designated hydroxide precipitation with aeration (followed by reverse osmosis if necessary) as preferred treatment technology • Over 1000 tons per day of dewatered sludge will be produced

  4. PROJECT CONCEPT • Value of contained metals presents opportunity for offsetting treatment costs via product recovery/resale • Acid mine drainage a worldwide problem • Project will evaluate both proven and new technologies for optimizing overall economics of producing compliant water

  5. PROJECT CONCEPT (Cont’d.) • All aspects of problem will be included • Challenges • Distance of Butte, Montana from markets • Dilute feed stream (though extremely contaminated) • Low-value base metals present

  6. CURRENT PROJECT SCOPE • Develop two optimized flowsheets • Water Treatment-Only • Water Treatment-Plus-Product Recovery • If results economically attractive, pursue pilot testing of optimized product recovery process at Berkeley Pit

  7. CURRENT PROJECT SCOPE (Cont’d.) • Major Tasks • Prepare standardized cost-estimating methodology • Develop optimization strategy (identify/prioritize potential process improvements)

  8. PROJECT STATUS AS OF APRIL 2000 • Work plan complete • Conceptual design of sludge repository complete • Cost estimating methodology document complete • Document verifying technical and cost aspects of reference flowsheets complete • Optimization strategy in development • Preliminary optimization efforts underway (gathering cost/technical data applicable to both flowsheets)

  9. PROJECT SCHEDULE • Final report describing optimized flowsheets due for publication in November 2000

  10. IMPROVEMENTS IN ENGINEERED BIOREMEDIATION OF ACID MINE DRAINAGEActivity III, Project 24

  11. Project Objectives Objectives for improvements of engineered features of a passive SRB-bioreactor include: • Selection of media • Design of a permeability and contact time enhancing system (PACTES), • Design of an organic carbon replaceable cartridge system (RCS), • Development of computer software to model SRB bioremedial processes in the bioreactor.

  12. Scope of Work The scope of work of the project includes seven tasks: Task I Selection of organic carbon media that: • is permeable when saturated with water, • contains sufficient mass of organic carbon to minimize treatment rates, and • Could be economically used for passive SRB bioreactors.

  13. Scope of Work, cont. Task II PACTES design, evaluation through a bench test study, and implementing it in the field. Task III Designing of an organic carbon RCS that would be easy to install and replace in a bioreactor at a remote location.

  14. Scope of Work, cont. TaskIV Development or adaptation of computer software to model SRB bioremedial processes in the bioreactor. This task includes efforts on: • Software development and validation • Lab experiments for bioreaction kinetics

  15. Scope of Work, cont. Task V Implementation of the results of the four previous tasks in a bioreactor constructed for this purpose. Task VI Project management activities. Task VII Site selection and characterization

  16. Status of Work(as of 03/31/00) Task I was initiated in February, 2000. • Data base structure is 60% developed. • Search of information is advanced approximately 30%.

  17. SLUDGE STABILIZATION Activity IV ; Project 2

  18. OBJECTIVE • Formation, properties and stability of sludge generated during treatment of acid mine waste water • Physically and chemically characterize sludges • Study the stability of sludges created by treatment techniques • Apply to acid mine water • Point source • Non-point source

  19. Stabilization Techniques will be Developed for Hazardous Sludge • Commonly used additives for metallurgical waste solids • Thermal Processing • Effective for arsenic bearing waste • Recovery of metal values or removal of hazardous constituent/recycling to metallurgical processes • In particular, sulfide sludge

  20. DEMONSTRATION OF ARSENIC REMOVAL TECHNOLOGY Activity IV ; Project 5

  21. OBJECTIVES • Remove Arsenic from Solution • Characterize Solid Products • Determine Stability During Storage

  22. CONCEPT • Produce an apatite mineral-like structure with the substitution of arsenate for phosphate in the structure

  23. REMOVAL OF ARSENIC FROM WASTE SOLUTIONS WHAT IS WRONG WITH SIMPLE LIME PRECIPITATION??

  24. EPA’s BDAT FOR As BEARING WASTEWATERS • Ferrihydrite precipitation is an adsorption phenomena • Potential Problem • Long-term storage

  25. ASARCO DEMO RESULTS • Scrubber Blowdown Water • >3,000,000 ppb As to <10ppb As • Thickener Overflow • 6,000 ppb As to <15 ppb • Long-term Aging Presently Being Conducted (ASARCO and Mineral Hill Products)

  26. MINE WASTE BERKLEY PIT LAKE CHARACTERIZATION PROJECT Activity IV ; Project 8

  27. CHARACTERIZATION PROJECTS • DEPTH PROFILES • ORGANIC CARBON • SRB ACTIVITY IN SEDIMENTS • SURFACE WATER REACTION KINETICS

  28. SUMMARY • Berkley Pit Lake system is complex and requires much more research to fully understand • Knowledge gained through work on Berkley Pit may be used on other pit lakes through out the world

  29. Artificial Neural Networks As An Analysis Tool for Geochemical Data Activity IV ; Project 14

  30. WHY USE NEURAL NETWORK? TO SORT THROUGH OR ANALYZE VERY LARGE DATA VOLUMES NN’s basically think like the human brain

  31. ALGAL REMEDIATION DATA OF BERKELEY PIT • 4 Classes of Data with 15 Samples • Within each class, 5 subclasses exist with 3 samples each

  32. Self- Organizing Map • Groups Data According to Trends Within the Data • For Algae, the SOM Output Compared to Known Data Classes NOTE: Neural Networks can also be used to predict data

  33. Future Possibilities for NN Analysis of Algae • Look for behavior trend within Algae species • Compare similarities and differences • Train network to recognize different Algae species and concentrations • Develop network to predict Algae types and concentrations from pit-water metal concentrations

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