Compliance Monitoring Simplified with

Compliance Monitoring Simplified with

A cost effective software alternative to your traditional hardware-based Continuous Emissions Monitoring System(CEMS).

Zuno hero image
Zuno hero image

Faster, Better, Affordable uses Deep Learning to model your facilities' emissions and industrial processes with over 95% accuracy so you no longer have to deal with old, cumbersome hardware or its downtime and maintenance costs. As organizations are held increasingly accountable for their influence in the climate crisis, ensures you adhere to the most stringent compliance monitoring and emissions reduction regulations anywhere.
Lower Cost
No hardware analyzers needed to measure direct emissions from exhaust stacks
Zero downtime, Zero maintenance, zero dedicated required
Generate regulatory reports across any format instantly

Emissions monitoring built
with you in mind

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Replacing Hardware Analyzers with as the Primary Monitoring System

  • Zuno Carbon helped a regional ethanol plant replace hardware analysers with as the primary monitoring system.
  • Using historical data from the existing hardware analyser ,we developed an AI based prediction model for emissions that garnered the same results as the hardware.
  • was able to pass an audit with 98.5% accuracy and this decisions helped the company avoid costs of over USD 150, 000 a year.
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Tracking Emissions and Performance with for Health Monitoring

  • Zuno Carbon helped a local vessel chartering company track emissions and performance using
  • We modelled CO2 and CO emissions with only 2 parameters, subsequently creating a tailored solution to obtain data from physical gauges.
  • enabled the company to detect anomalous operations through emissions and temperature data 20x faster than before.
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Tracking and extending the battery life of wireless sensors

  • A large petrochemical plant leveraged the built-in Data Acquisition and Handling System(DAHS) of to monitor the battery life of multiple wireless sensors with different communication protocols.
  • Zuno Carbon created a tailored solution where the user can toggle the polling rate of the wireless sensors from’s dashboard, prolonging the battery life according to the activity of the sensors and optimizing the battery replacement schedule.
  • This drastically reduced the number of runs required from routine maintenance, saving the company in both resources and time.
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Replacing Statistical PEMS with for Compliance Monitoring

  • A large methanol plant was using a PEMS solution that frequently produced incorrect measurements.
  • Zuno Carbon replaced this with, and developed an AI based prediction model for emissions based on historical audit data.
  • We use high-quality data direct from the source rather than proxy measures- this included operational data about fuel flow, pressure, temperature and valve position, as well as pollutant passed the audit with 99.1% accuracy, with zero downtime during operations. It also helped reduce support tickets raised by 98% a year
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Frequently Asked Questions

What is a PEMS?

Predictive Emissions Monitoring Systems (PEMS) are a software-based solution for emissions monitoring. PEMS is generally regarded as a cost-effective alternative to traditional hardware based Continuous Emissions Monitoring Systems (CEMS). There are three main model types used in PEMS - neural networks. first principles, and statistical hybrid models

What is a CEMS?

CEMS, or Continuous Emissions Monitoring Systems are hardware analyzer based solution for emissions monitoring. CEMS collect air samples from the emission source using probes, condensers, heated sample lines, and analyzers. The different types of CEMS can be catogorized into Dilution, Extractive and FTIR CEMS.

How does work? is a neural network based PEMS. Here is a brief explanation on how it works:

1. Data Collection
First, Historical process and emissions data are collected.

2. Model Training
Next, Using deep learning techniques, the neural network learns correlations between the input process data and the emissions produced, building a predictive model. The model can be retrained easily as more data is collected, improving prediction accuracy.

3. Deployment
When deployed at a site, real-time process data is fed into the model and predicted emissions are generated. After that, an initial certification audit is performed where the predicted emissions are verified using reference methods (i.e: Mobile CEMS).