Continuous emission monitoring of Industrial emission

USA based Global Research University Initiative


✅ India based state regulator wanted a centralized platform to monitor and control industrial emissions in the state and also regulate the industries for High emission.

✅ Communication devices consolidated the data from Analysers planted at Industries for emission data retrieval.

✅ Web-application is build to track all emissions, generated meaningful dashboards & reports.

✅ Each industry was using their own commercial Analyser. No standardization of Analyzer platform by regulator.

✅ Data transmission was prone to Data loss & Data corruption.

✅ Centralized automated monitoring of Industrial emission.

✅ Analytics used to identify High polluting industries, Geographic locations and Even the processes impacting pollution.

Environmental sensors for PM, SO2, Sox, NO2, NOx etc. Communication devices and protocols like RS232, RS485 and GPRS. Cloud technology like AWS, Google. Web and mobile application development using php, python, Django, Android and iOS programming. Big data technologies using spark/HDFS.

Adoption of Industry 4.0

Asian aluminum and copper manufacturer major

iot 2

✅ An aluminium producing industry wanted to control its raw material consumption through artificial intelligence.

✅ Various kind of sensors like temperature sensor, pressure sensor, current sensor and height measurement sensors were used to gather plant operation and control data.

✅ AI/ML algorithms were used to simulate good plant behaviour and control algorithms were build to control the parameters.

✅ Data collection was manual and data quality was poor. Even data availability was very slow since there was no automated mechanism to gather data.

✅ No access to control system.

✅ Reduced Operating / Input Cost for manufacturing by 2-3 %

Sensors for temperature, pressure, moisture, electrical and length measurement . Usage of raspberry pi for data collection and transmission. Usage of google cloud for Analytics and data storage. Usage of Machine learning algorithms like linear regression, decision treefor analytics and control signal generation.

There is always time to talk..