INSUS connects declared EfW emissions to waste receipts, composition sampling and operating data, quantifies the biogenic and fossil split, and surfaces evidence gaps before verification. It does not create carbon data or replace physical sampling.
01
Evidence trail from waste receipt to declared emissions.
02
Uncertainty shown. Assumptions are not treated as measurements.
03
Advisory only. Verification stays with the accredited verifier.
The problem
EfW operators already keep emissions records, waste receipts and sampling information. The hard part is connecting those records into a defensible explanation of the biogenic and fossil share of emissions. When fossil carbon carries a cost, a default composition assumption is no longer the same thing as measured evidence. Assembling that trail retrospectively for verification is a different job from keeping it continuously.
The shift
Nothing here creates carbon data. What changes is whether the plant's own receipts, samples and emissions sit on one evidence trail, with uncertainty visible before verification.
Today
With INSUS
How it works
Delivered through ExpertFlow AI as document intelligence over waste-reception, sampling, plant and MRV records. It organizes the evidence trail. It does not replace physical sampling or the accredited verifier.
Connect
Connect weighbridge and waste-receipt records, waste codes, supplier information, composition sampling, plant throughput, energy output, continuous emissions data, support fuel records and prior MRV submissions.
Understand
Trace the relationship between waste received, composition evidence, plant operating data and declared emissions, identifying which parts of the calculation are measured and which depend on assumptions.
Identify
Estimate the expected biogenic and fossil fraction for the observed input mix while explicitly representing uncertainty, rather than presenting an assumption as if it were a measurement.
Advise
Identify assumption dependencies, quantify sensitivity of the carbon split, assess where additional sampling could reduce uncertainty most effectively, and assemble supporting MRV documentation for human review.
Verify
Compare modelled results with sampling outcomes and verifier findings. Where measured sampling contradicts the model, the measurement takes precedence and the model is corrected.
Getting started
For energy-from-waste plants preparing monitoring, reporting and verification of the biogenic and fossil CO₂ split. Delivered through ExpertFlow AI as document intelligence. Expert Services can help with discovery and validation. Physical sampling and laboratory analysis remain outside the software scope.
Related for waste
Biogenic and fossil carbon MRV sits beside WtE combustion stability on the same plant and anaerobic digestion for the organic fraction.
Declared EfW emissions need a defensible biogenic and fossil split. This use case connects waste receipts, sampling and plant data into a traceable MRV evidence trail, with uncertainty and gaps made visible for human review.
Weighbridge and waste-receipt records, composition sampling, plant throughput and emissions data, prior MRV submissions and the monitoring plan. The software does not require new plant sensors. Physical sampling and laboratory analysis remain outside INSUS scope.
No. Software cannot create physical composition evidence that was never collected. It makes better use of sampling already performed and identifies where additional sampling could materially reduce uncertainty.
No to both. The system supports data reconciliation, evidence organisation and documentation. Regulatory interpretation and final compliance decisions remain with qualified personnel. Verification remains with the accredited verifier.
Through ExpertFlow AI as document intelligence. Expert Services can support discovery, evidence structuring and validation with your MRV team.
No. It is intended to augment existing MRV, reporting and documentation workflows by connecting data sources and strengthening traceability.
The system cannot characterise waste that was never sampled, and confidence depends on the quality and representativeness of composition evidence. Regulatory implementation timelines may change and should be verified against the applicable rules.
Waste · EfW carbon accounting and MRV
A retrospective pass on your own waste tonnage, sampling history and emissions records will show the uncertainty, the evidence gaps and the sensitivity of the split before verification makes the question more consequential.
