Analysis of CO2 emissions from natural gas-fired power plants and an emission control strategy

The quantification, prediction, and reduction strategies of CO 2 emissions are essential for their negative impacts on health, the environment, and the global climate. This study analyzed CO 2 emissions from Engine-based Natural gas-fired power plants and forecasted CO 2 emissions. To quantify, analyze, and predict CO 2 emissions using Error, Trend, and Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), Auto-Regressive Integrated Moving Average (ARIMA), Holt, Holt-Winters and Extreme Learning Machine (ELM) modeling approaches have been adapted and selected the best model among them based on CO 2 emissions data from natural gas-fired power plants. This paper introduced a new idea to reduce emissions by using exhaust to generate electricity. This study investigated the contribution of Exhaust Gas Boilers (EGB) to CO 2 emission reductions and found in engine based Natural Gas fired power plants, it reduces on average 0.8–10 % of CO 2 emissions using Waste Heat Recovery (WHR), and this reduction is dependent on engine load, but waste air or exhaust is still released into the atmosphere. This paper introduces a conceptual framework for Waste Air Recovery (WAR), a forward-looking approach that proposes utilizing post-EGB exhaust air in turbine systems for further efficiency gains and emissions reduction. While not evaluated empirically in this study, WAR is presented as a potential direction for future research. • Quantification and prediction of CO 2 emissions from engine-based natural gas-fired power plants using AI models. • Evaluation of forecasting accuracy across ARIMA, ETS, TBATS, Holt, Holt-Winters, and ELM models. • Empirical analysis of CO₂ reduction achieved via Exhaust Gas Boilers (EGB) using waste heat recovery. • Proposal of a conceptual framework for Waste Air Recovery (WAR) as a potential future emission reduction strategy. • Fuel consumption reduction by EGB and WAR.