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Intelligent Soot-Blowing
Pulp & Paper Company, Australasia

Energy

Intelligent Soot-Blowing

Intelligent Soot-Blowing
Pulp & Paper Company, Australasia
Intelligent Soot-Blowing

Intelligent Soot-Blowing

Intelligent Soot-Blowing
Executive Summary

Rapidly Turning $133 kUSD into $396 kUSD

We implemented Intelligent Soot-Blowing(ISB) and adaptive excess-air control on the recovery boiler at a long-servingkraft mill. The optimisation project increased net-steam by 6 t/h,raised boiler efficiency by 3.6 %, and eliminated six unplannedwater-wash shutdowns per year. Finance verified recurring savings of ≈ USD396 000 per year, with a project pay-back of ≈ 4 months.

Client Background

400 kTon Bleached Softwood Pulp Mill

For more than forty years, this coastal Southern-Hemisphere kraft complex converts plantation softwood into ≈400 000 t of bleached pulp per year, supplying packaging markets across theAsia–Pacific. Its single recovery boiler drives a 25 MW back-pressure turbine, exporting surplus electricity to the grid.

Problem

Inefficient Cleaning, High Downtime and Lost Steam

When the client reached out to us they had multiple challenges

  • Wholesale power prices have climbed > 80 % since 2020, magnifying every lost megawatt.
  • Ageing evaporators raise liquor-solids variability, increasing fouling risk.
  • Corporate ESG targets mandate a 15 % CO₂-intensity cut by 2030 with minimal capital outlay.

Fixed soot-blowing intervals wasted steam and failed to prevent fouling on the most critical surfaces. Unplanned water-wash shutdowns wiped out turbine revenue gains and increased operator workload.

Some of their key pain points were

  • Six unplanned water-wash shutdowns per year.
  • ≈ 4 % steam lost to over-cleaning.
  • No live fouling index — operators flying blind until efficiency dropped.
Solution

Anssum's Three Step Solution Framework

To tackle the client’s mill challenges, we applied Anssum’s three-step framework:

  • Data-Driven Discovery: Pinpointing issues with clear data.
  • Collaborative Design: Building solutions together with the client.
  • Straightforward Integration: Delivering results with ease.

John Doe

Implementation Process

Step What We Did Operator Role
1 Data-Driven Discovery Six weeks of 5-min historian data modelled fouling vs. net steam and exhaust O₂. Flagged abnormal batches and validated model bias.
2 Collaborative Design Embedded an adaptive soot-blow optimiser and excess-air controller into the existing recovery boiler DCS. Piloted logic in shadow mode; set fouling index and safety limits.
3 Straightforward Integration ISB and excess-air control embedded in the existing DCS; no lance or PLC upgrades required. Dashboards show live fouling index and net-steam KPI. Approved new HMI views; completed one-shift hand-over session.
Results

$396 kUSD Annual Gain

Initiative Challenge Addressed Outcomes
Net-Steam Increase Over-cleaning + fouling Net-steam +6 t/h
Boiler Efficiency Fixed 30-min soot-blow cycles +3.6% boiler efficiency
Availability Boost Six unplanned water-wash shuts 0 water-wash shuts in 12 months
Power Export Potential Fouling variability limited turbine export Up to +1 MW additional export capacity

FinancialSummary – ≈ USD 396 000/year verified savings; project pay-back ≈ 4 months; value-to-cost ratio ≈ 3 : 1.

Takeaways

Key Takeaways

Adaptive cleaning beats fixed intervals — fouling is dynamic; cleaning must be too.

Operator co-design secures adoption — front-line input sets risk limits everyone trusts.

Controls over cap-ex — optimisation of existing equipment delivered sustained steam gains and CO₂ cuts.

Strategic roadmap unlocked — freed steam and stable boiler operation enable future turbine uprate and ESG progress.

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