Industrial intelligence applied where performance matters.
From energy efficiency to predictive asset performance, Arvane applies operational intelligence to the decisions that shape industrial cost, reliability and carbon intensity.
PRODUCTION / ENERGY / EQUIPMENT / COST / CARBONTHE ARVANE PERSPECTIVE
Arvane connects operating data and industrial context to the decisions that affect energy, equipment, production and carbon. The appropriate scope depends on the facility, available data and validated engineering requirements.
The question is not simply where energy is consumed. It is whether that consumption is justified by the work being done.
Compare demand by process and asset, then relate it to output, runtime and operating state. Separate useful production demand from unnecessary baseload, inefficient equipment operation and peak loads that may be avoidable.
PRODUCING
Energy per unit of output
Compare products, load and process conditions.
WAITING
Demand during idle time
Distinguish required utilities from avoidable consumption.
STARTING / STOPPING
Transient demand
Examine start sequences, warm-up and shutdown behavior.
Operators can use this operating picture to prioritize a baseload investigation, adjust equipment duty or review process settings. The objective is less energy required per unit of production, with quality and throughput protected.
Company-wide emissions totals serve a different purpose from operational intensity. Plant teams need to see how fuel choice, electricity sources, equipment performance and product mix affect emissions per tonne, unit, batch or process output.
DEFINE THE PRODUCTION BOUNDARYPRODUCTION-LINKED INTENSITY
Attributed emissionsCO₂e within the agreed boundary
÷
Production by tonneMatched period and product context
=
Carbon intensityCO₂e / tonne
Compare equivalent output and keep the emissions boundary consistent. A lower total can reflect less production rather than a more efficient operation.
Use the result to investigate which operating conditions are associated with higher intensity. Keep emission factors, allocation methods and process-emissions assumptions traceable so that an apparent improvement can be explained.
Degradation can alter the energy needed to deliver the same service. That additional demand can increase cost and carbon intensity, while a developing mechanical or process problem may increase failure risk.
01Degradation
02More energy
03Higher cost
04Higher carbon
05Failure risk
ASSET EXAMPLE / COMPRESSOR C-14
400 → 468 kWh / cycle
Sample asset data: energy use is 17% above a 400 kWh-per-cycle baseline. Compare load and operating conditions before interpreting the deviation.
Review pressure, flow, loading patterns, air leaks and the maintenance history together. The next action might be an inspection, a sensor check or a revised baseline—not an automatic maintenance instruction. Verification follows the actual intervention.
Unusual behavior can appear across temperature, pressure, flow, runtime, power, fuel use and output. A value within its individual range may still be inconsistent with the other signals around it.
MULTI-SIGNAL COMPARISONNORMALIZED SIGNALS / SAMPLE DATA
Additional power at comparable pressure and output is a signal to investigate. Confirm sensor quality, operating state and production conditions first.
Time alignment and operating-state context are essential. Start-up, a product change or a faulty instrument can look like a process anomaly. Group related deviations and provide the surrounding evidence so an engineer can determine whether investigation is warranted.
Where a process has scheduling flexibility, production can be evaluated against electricity cost, energy demand, carbon intensity, available equipment and renewable availability. Delivery commitments and process dependencies define what can actually move.
PRODUCTION SCHEDULING WINDOWDEADLINES / CAPACITY / ENERGY
EARLY SHIFTMID SHIFTLATE SHIFT
Production deadline
Equipment available
Flexible process
Candidate operating window
Compare a current schedule with feasible alternatives. Account for ramping, minimum run durations, staffing, storage and maintenance. A recommended window should state the objective it improves and any tradeoff it introduces.
Different interventions can address the same operating challenge through different mechanisms. Compare them against a common production baseline and explicitly model implementation, energy, carbon and financial assumptions.
INTERVENTION COMPARISON / EVALUATION CRITERIA
Evaluation
Scenario AEquipment upgrade
Scenario BElectrification
Scenario CProcess optimization
Investment
Equipment, installation and commissioning
Electrical infrastructure and process conversion
Engineering, instrumentation and operating changes
Energy impact
Assess efficiency at actual duty points
Compare electricity demand with displaced fuel
Model consumption under revised settings
Carbon impact
Apply relevant energy emission factors
Test grid intensity and fuel assumptions
Separate efficiency effects from output changes
Production impact
Account for shutdown and ramp-up
Validate heat, quality and throughput requirements
Maintain process and quality limits
Payback
Depends on utilization and avoided operating cost
Sensitive to tariffs and infrastructure scope
Depends on repeatability and implementation cost
Sensitivity analysis matters as much as the headline case. Test how the decision changes with utilization, tariffs, grid carbon intensity or implementation downtime. A preferred option should remain understandable when those assumptions change.
A project is implemented. Did performance improve?
Lower consumption after an intervention does not, on its own, establish an efficiency gain. Production volume, product mix, weather, operating hours and equipment availability may also have changed.
01Baseline
02Intervention
03Observed performance
04Normalized comparison
05Verified impact
Define a relevant pre-intervention baseline, record the change and compare subsequent operation under equivalent conditions. Document exclusions, data gaps and uncertainty. Distinguish energy reduction from changes in carbon factors or reporting boundaries.
THE EVIDENCE STANDARD
Verification means a documented comparison against agreed operating conditions. Use agreed boundaries, normalized operating conditions and traceable assumptions to substantiate the impact.
The same compressor signal can inform an energy investigation, a maintenance discussion and a carbon-intensity calculation.
These applications share source data, operating context and model assumptions. Connecting them helps teams examine the consequences of a decision across production, reliability, energy and carbon.
