Case Study

Improving efficiency of solid liquid separation through AI

In this case study, we present and discuss HABER’s approach towards optimizing the separation process to achieve the required turbidity of water.


Solid-liquid separation is the process that is performed to recover either solids, liquids, or both. In some cases, it may recover neither, and be used just to separate the solids and liquids before discharging it from the system.

Dissolved Air Floatation (DAF) is one of the methods through which solid-liquid separation takes place. It is used to recover residue fibers post the retention aid process. DAF separates liquid and solids by dissolving air and releasing it into a floatation tank.

A leading Kraft paper manufacturer was facing issues in Sedicell (DAF). Some of the major issues faced were as follows:

  • Low efficiency in Total Suspended Solids (TSS) reduction;
  • High usage of Polymer;
  • High usage of Inorganic coagulants. 

The customer approached HABER and had a detailed technical discussion to understand the issues experienced. This was followed by a thorough system study and lab screening of suitable products, to determine how to improve upon the process.

Our Approach:

HABER’s eLIXA® platform provided real-time artificial intelligence and machine learning-based closed-loop control for solid-liquid separation programs.

Key process variables like pH, temperature, conductivity, TSS, and others were measured and uploaded to the cloud in real-time, on which big data analysis was carried out. The solution was based on prioritizing the elimination of inorganic chemistries (with alternative measures), reducing the overall cost of the SLS, and improving the TSS efficiency. 

HABER introduced its fully automated eLIXA® controlled component polymer program and eliminated inorganic chemistries to ensure TSS is maintained within the targeted range. The scientific approach was adopted by the team to select suitable chemistry that would overcome the problem.


HABER’s solution resulted in the better functioning of the system, with:

  • A 93% reduction in Total Suspended Solids (TSS)
  • Absolute elimination of Inorganic chemistries
  • 14% reduction in overall cost incurred

The implementation of AI/ML optimized the process as it resulted in the efficient separation of the solids & liquids. It not only ensured that more fibers were extracted, thereby reducing the raw material intake, but also eliminated the need for inorganic coagulants, thereby reducing the overall cost of raw materials.

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