• Monitor process efficiency across facilities for performance anomalies and explore predictive maintenance as a source of water / energy savings or emissions / waste reduction.
  • Automate data assessment and IoT monitoring to identify outlier outcomes and potential data collection, sensor and calibration maintenance needs.
  • Maintain equipment based on performance conditions, reducing downtime.
  • Improve the lifespan of critical assets while reducing operational costs.
  • Execute facility wastewater operations and maintenance processes flawlessly.
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Sectors Plutoshift supports:

metals and machinery industry icon

Metals and
Machinery

paper production industry icon

Paper

plastics sector icon

Plastics

transportation icon

Transportation

textiles production sector icon

Textiles

Use Cases

1.
Process and equipment energy consumption optimization
2.
Water purification and treatment
3.
Waste minimization
4.
Safety improvement
5.
Sustainability planning
1.
Optimize energy demand, production and electricity costs to improver facility performance
2.
Apply predictive algorithms to water purification and treatment process to reduce maintenance costs and enhance asset life
3.
Optimize chemical dosing and related materials to reduce unnecessary waste treatment and related disposal/recycling costs
4.
Develop learning models to predict faults and prescribe actions that lead to less at-risk manual intervention
5.
Implement unified view on facility sustainability metrics with predictive algorithms that spot performance anomalies before they occur

Use Cases

Process and equipment energy consumption optimization.
Optimize energy demand, production and electricity costs to improver facility performance
Water purification and treatment.
Apply predictive algorithms to water purification and treatment process to reduce maintenance costs and enhance asset life
Waste minimization.
Optimize chemical dosing and related materials to reduce unnecessary waste treatment and related disposal/recycling costs
Safety improvement.
Develop learning models to predict faults and prescribe actions that lead to less at-risk manual intervention
Sustainability planning.
Implement unified view on facility sustainability metrics with predictive algorithms that spot performance anomalies before they occur

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