Climate change has been a critical problem that requires rapid action in today’s Digital world to save our planet. Its impacts are already being felt globally and the negative effects are expected to grow exponentially, with disproportionate consequences for the world’s most marginalized communities. Tackling climate change requires action across society, spanning many communities, approaches, and tools. After a detailed analysis and research, Ai labs has done has developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky to highlight the effects of Climate change.
Ai Labs has developed several green projects such as Renewable Energy, Water treatment, Greenhouse gas emission from power generators. The objective of this case study is to predict hazardous gas emissions (Carbon Monoxide CO, N2O and NOx) from power generation plants.
After comprehensively evaluating the client ‘s challenges/data, we used Minsky to accurately model the historical data which comprised of gas-turbine attributes such as Ambient Temperature, Ambient Pressure, Air filter difference pressure, Gas turbine exhaust pressure compressor discharge pressure, compressor discharge pressure, Carbon monoxide, Nitrogen oxides etc. which provides detailed visibility to factors contributing to Climate Change. Once the detailed modelling was performed by Minsky for the selected Algorithms, historical power plant’s gas turbine data along with other equipment data was used by Minsky to predict emission of hazardous gases like carbon monoxide and nitrogen oxide. Prediction data from Minsky was also integrated with 3rd Party data visualization application like Tableau.
Based on the data from Fig 1 above, our analysis shows that K Neighbours Regression (74.1%) gave the highest model accuracy based on our limited Train and Test data. In terms of the GHG attributes data, the Turbine Inlet Temperature showed the highest dependency for the CO emissions from the Power Plant followed by Turbine Inlet Temperature & Compressor Discharge Pressure.
Based on the data from Fig 2 above, our analysis shows that K Neighbors Regression (85.5%) gave the highest model accuracy based on our limited Train and Test data. In terms of the GHG attributes data, the Ambient Temperature showed the highest dependency for the Nitrous Oxide emissions from the Power Plant followed by Turbine Inlet Temperature & Turbine Outlet Temperature.