e-SEEMA™
Regulatory requirements regarding SOx, NOx & CO2 emissions are getting stricter day by day. Process industries are increasingly forced to think of ways & means to monitor & record their stack emissions, ideally in real time through on-line data/analysis. From a plant owner’s perspective, it is important that efficient and reliable tools for acquiring emission data are available.
The above needs are addressed by e-SEEMA™ (e-Simulation Enabled Emission Monitoring & Analysis), a first principle-based simulation enabled application to calculate, record, analyze, and monitor the SOx, NOx, and CO2 emission. Also, it can help to determine analyzer deviations/errors and validate measurements by third party inspection agencies.
e-SEEMA™ provides an online predictive model for real-time emission monitoring & an offline predictive model for analysis & reporting.
Due to huge emissions, it is essential for process industries to adopt a holistic approach of tracking, analyzing & reporting SOx, NOx & CO2 emissions through a model based business process leveraging first principle simulation, data based inferential, online analyzers, third party inspection data to comply with regulatory norms.
The problem
Challenge
Monitor, validate, analyze and report SOx, NOx & CO2 emissions for:
Regulatory compliance
Furnace performance analysis
Online analyzer performance management
How we solve it
Solution
Implement e-SEEMA™, to monitor and report SOx, NOx & CO2 emission which allows user to analyze furnace and stack performance. It also calculates other important parameters like:
Flue gas oxygen content
Stack volumetric flowrate
Stack velocity
Dewpoint calculations
Flame temperature
What you gain
Benefits
- Perform data reconciliation to know any consistent analyzer deviation
- Validated sound basis for reporting & analysis of SOx, NOx & CO2 emissions
- Perform analysis for the all the stacks in the plant on daily basis
- Validate the measurements of third party inspection agencies
- Perform quick calculations using inferential techniques based on plant operating data