Stabilise transformation without adding complexity to the shop floor

In plastics processing, performance depends on fine balances: melt viscosity, material temperature, shear, pressure, moisture, additives and utility stability directly determine throughput, dimensions, surface quality and mechanical properties.

Even minor deviations result in scrap, dimensional drift, gels/black specks, bubble instability, fibre breakage or unplanned downtime.

Indao transforms machine, material and quality data (extrusion, injection, compounding, film/fibre, utilities, lab) into reliable operational indicators and concrete recommendations.

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Why plastics processes are different

Processes where the material is continuously “alive”: rheology, thermal behaviour and moisture directly drive quality.

Rheology sensitive to shear and temperature

Melt viscosity evolves with temperature, shear rate and formulation even small deviations impact pressure, throughput and stability

Narrow process window and visible quality

Surface appearance, transparency, gels, burns, flow lines, haze… small material or machine variations quickly become defects

Increasing variability of materials and recyclates

MFR/MFI, moisture, contamination, filler distribution and additives vary significantly, making stable industrialisation difficult without proper contextualisation

The challenges you face

  • Melt stability & throughput: maintain consistent material viscosity despite variations in batches, recyclates and temperatures
  • Product quality & defects: gels, black specks, burns, bubbles, delamination, brittleness, dimensional drift, haze/opacity
  • Energy & utilities: optimisation of heating zones, cooling (water/air), compressed air, vacuum and thermal equipment efficiency
  • Line availability: filter clogging, screw breakage, wear, melt pump drift, bubble instability, blockages and unplanned downtime
  • Traceability & compliance: linking process conditions ↔ lab results (MFR, DSC, moisture) ↔ SPC ↔ non-conformities

The limits of your current tools

  • Machine alarms based on fixed thresholds (pressure / temperature / amperage) that ignore material, recipe and operating regime
  • Quality controls often delayed (MFR, DSC, density, moisture) → corrections come too late
  • Siloed data across machine supervision, MES, lab, maintenance and energy → difficult to link process cause ↔ defect
  • Operator-dependent settings (“by feel”), poorly capitalised and difficult to transfer across lines or sites
  • Limited continuous estimation of material indicators (apparent viscosity, melt stability, moisture) and sensor drifts

What Indao delivers in practice

We design advanced analytics solutions that truly improve control of plastics processes, with AI as a support : explainable, controlled and field-oriented.

Indao tracks critical variables (heating zones, die pressure, screw speed, torque, throughput, cooling) and places them in the context of material batch, recycled content, recipe and production rate.
Continuous estimation of non-measured indicators (apparent viscosity, melt stability, residual moisture, MFR drift) from machine and utility signals.
Robust correlation between process conditions and defects (gels, black specks, bubbles, haze, delamination, dimensional drift) to isolate dominant factors.
Identification of oversized heating zones, cooling inefficiencies and energy drifts (kWh/kg) to adjust setpoints without compromising quality.
Creation of a structured history linking material (certificates, batches), settings, actual operating conditions, SPC and lab data for audits, claims and multi-site knowledge sharing.
Connection to machine data (injection/extrusion), MES, historian, lab, energy and maintenance systems, with progressive deployment that does not disrupt shop floor operations.

What you gain

Operational results, not just more dashboards

Reduced scrap and non-conformities

Early detection of material/machine drifts
→ fewer visible defects and dimensional deviations

More stable production and consistent quality

Better control of the process window despite material and recyclate variability
→ fewer trial-and-error adjustments

Increased line availability

Identification of conditions leading to filter clogging, bubble instability or blockages
→ fewer unplanned stoppages

Improved energy efficiency

kWh/kg monitoring and optimisation of heating/cooling
→ reduced consumption without compromising quality

Empowered teams

Shared references (operators, process engineers, quality) and actionable recommendations based on real signals

Controlled integration

Progressive, secure deployment without overloading IT/OT or disrupting existing routines

See clearly. Act decisively. Optimise sustainably.

Decisions that have already proven their impact

  • Process Performance Optimization

2Valorise

Optimising the steam network to maximise cogeneration plant performance
  • Energy

CoCEPTIO

Structuring and leveraging HVAC data to improve energy efficiency
  • Process Performance Optimization

CRM Group

Identifying key parameters in hot rolling to better control process performance
  • Data-driven Decision-Making

EKO-MYŚL

Centralising waste and energy data to improve operational control

Want to make decisions differently?

Let’s explore how your data can become a concrete operational lever.

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Whatever your segment in the chemical industry

Concrete levers at the core of your processes

Acids

Optimise highly thermal and corrosive processes by stabilising critical operating parameters and improving energy efficiency

PVC

Improve control of polymerisation conditions and reduce process variability to ensure product consistency and material performance

Industrial gases

Better control compression, separation and liquefaction to reduce energy intensity and improve reliability of continuous operations

Fertilisers

Optimise energy consumption of highly thermal processes while improving production consistency

Specialty chemicals

Enhance batch reproducibility and reduce variability in complex, high-quality-demand processes

Petrochemicals

Improve operational stability of continuous units by detecting performance losses early