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Higher accuracy than required by the regulator, ensuring more precise monitoring
Deployment time - PEMS deployment time with seamless integration and minimizing disruptions
Initial deployment cost reduction compared with CEMS
Reduction in fuel consumption and carbon dioxide emissions
Reduction in carbon monoxide emissions
Reduction in NOx emissions
Detecting malfunctions in CEMS with Physical Sensors begins with the sensors measuring various parameters. The data collected then flows into an Enterprise Data System (DCS), which aggregates and potentially processes it. Subsequently, the processed data is fed into a machine learning or deep learning model for analysis. This model compares the outputs of the traditional CEMS with those of ES-PEMS, evaluating their performance. If the variance exceeds a predefined threshold, maintenance alarms are triggered, alerting personnel to potential issues.
Detecting and correcting sensor drift and failure in ES-PEMS involves continuous monitoring of sensor performance. Data collected by physical sensors is analyzed using machine learning models to identify anomalies. Discrepancies between traditional CEMS and ES-PEMS outputs trigger maintenance alarms, prompting swift action to rectify issues and ensure accurate emissions monitoring.
Cross-validating carbon tax is a feature integrated into our SaaS subscription service. With this function, ES-PEMS utilize monitored and predicted levels of CH4, CO2, and N2O to determine the equivalent CO2 emissions (CO2e). Subsequently, the CO2e calculated by ES-PEMS is compared against CO2e derived from emission factors and fuel consumption data. This validation process ensures the accuracy and reliability of the calculated CO2e values, enabling users to confidently assess and implement carbon taxation measures based on verified emissions data.
Optimize device operational processes and settings is a key function within our SaaS subscription service. Our ES-PEMS incorporates an optimization module designed to concurrently reduce emissions and enhance productivity. Through sophisticated algorithms, this module achieves emission reductions ranging from 30% to 50%. By leveraging inputs from Portable Gas Analyzer Measured Parameters and CEMS, our proprietary ES-PEMS model dynamically optimizes operational processes and settings.
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