journal article·2026-01-26

Full-scale operation of a membrane-based black liquor concentrator

Conrad Goffinet, Bryce Mason, Morris Lee, Nathan Kraft, Cole Harris, Lucy Kitch-Peck, Caitlin O'Malley, Ella Richards, Sintia Pulido Sardinas, Lymaris Ortiz Rivera, Nashin Pillay, Maxwell Barton, Sarah Ellis, Richard Caron, Ali Edwards, Mark Zaloudek, Brandon Macdonald, Brent Keller

TAPPI Journal· Volume 25· Issue 1
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Abstract

For years, black liquor concentration by reverse osmosis has been an aspiration for reducing mill energy consumption and costs. Building on a 2023 report of an 81% reduction in the energy intensity for concentrating black liquor using its membrane platform at the pilot scale, this paper reports on the fabrication, installation, startup, and operation of the world’s first full-scale membrane-based black liquor concentration system at the International Paper Grande Prairie mill in Canada. The majority of the membrane modules in this system have reliably exceeded permeability expectations by more than 50%. In addition to strong membrane performance, the system has been a significant source of learnings for material specifications and system design. Incorporating these learnings and comparing to typical multiple effect evaporators, the system demonstrates a 43% reduction in capital cost for the same capacity, a 30% reduction in the lifetime cost of removing water from black liquor, and an 86% reduction in energy use. The impact on washer optimization was also considered, and net energy and chemical costs were reduced by US$3.8 and US$6.8/a.d. metric ton for a typical brown and bleach mill, respectively, when incorporating this technology. Completed in less than 12 months, the facility has demonstrated successful black liquor concentration at the 500 gpm scale with monthly uptime as high as 96%.
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Research context

OpenAlex topics: Membrane Separation Technologies · Membrane-based Ion Separation Techniques · Adsorption and biosorption for pollutant removal

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (10)

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