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Liquiline System CA80COD - COD analyzer for environmental monitoring

COD analyzer
Liquiline System CA80COD

Analyzer for chemical oxygen demand in environmental monitoring, industrial and municipal wastewater

CA80COD
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Specs at a glance

  • Measuring range

    0 to 500 mg/l O2 COD chromate method
    0 to 5000 mg/l O2 COD chromate method
    0 to 5000 mg/l O2 COD chromate method + dilution module (1:4)

  • Process temperature

    4 to 40 °C (39 to 104 °F)

  • Process pressure

    Atmospheric

Field of application

Liquiline System CA80COD offers constant online measurement of the chemical oxygen demand (COD). It supports you in monitoring the cleaning capacity of wastewater treatment plants and enables load-based billing for industrial dischargers. Automatic calibration and cleaning save operating costs while advanced diagnostics with remote access help to provide process documentation to authorities. The COD analyzer's software-controlled safety functions ensure the highest level of occupational safety.

The Liquiline System COD analyzer monitors:

  • Inlet and outlet of wastewater treatment plants for documentation purposes and calculation of the cleaning capacity

  • Discharges of industrial wastewater treatment plants to determine discharge fees and to support the polluter-pays-principle

  • Process water

Benefits

  • Established COD dichromate method directly comparable to laboratory results.

  • Meeting industry needs: The analyzer is perfectly suited for environmental monitoring, industrial and municipal wastewater.

  • Fast and easy process integration: Direct installation of self-priming version or y-strainer for bypass applications.

  • Safety on the highest level: Software-controlled safety cover for the reactor and all heated parts.

  • Combination of reliable sample delivery and high-precision dosing: Peristaltic pumps are able to cope with particles in the sample. Light barriers enable precise, reproducible dosing.

  • Easy upgrade of functionality to a complete measuring station - simply by adding modules and connecting Memosens sensors.

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