ABSTRACT

The Increase use of fossil fuels has resulted in higher greenhouse gas emissions and the rise in global warming. This study applied the post-combustion process to capture CO₂ from engines powered on diesel, petrol and blended biodiesel. Exhaust emissions were measured using a gas analyzer model called IMR 1000-4 for the various engines. The engines include: diesel trucks, petrol light commercial buses, passenger cars, and petrol-biodiesel car. An amine-based adsorbent (MonoethanolAmine) through Temperature Swing Adsorption (TSA) was used to capture CO₂ from the exhaust gases. The CO₂ capture process was modeled using Aspen HYSYS V8.8. A source code was developed using Engineering Equation Solver (EES) to estimate the mass balance, energy, and exergy analysis of the system. The results shows that the average CO₂ emission was highest for heavy-duty trucks, accounting for 17.53 volume percent followed by 15.1 volume percent for light commercial vehicle models from 1980 to 1999. In contrast, heavy commercial vehicle models from 2000 to 2014 exhibited the lowest CO₂ emissions, ranging from 9.8 volume percent to 9.6 volume percent. The system achieved a CO₂ capture rate of 1.9 kg per liter of fuel consumed by an internal combustion engine, with CO₂ capture rates of 92%, 85%, and 75% observed for diesel trucks, petrol engines, and the biodiesel, respectively. The energetic efficiency (EE) and exergetic efficiency (ExE) ranged between 58.30% and 64.14%, and 47% and 53%, respectively. The exergy destruction (ED) gap between biodiesel and diesel was approximately 45%, while petrol exhibited a gap of about 32.22%. A sensitivity analysis using an Artificial Neural network (ANN) was used to determine the response and the factors influencing the level of CO₂ captured from the exhaust fuels. The results indicated the importance of independent variables: MEAmine (0.96), temperature (0.84), mass flow (0.77), and pressure (0.38). The performance of the model was evaluated using the Relative Error (RE); the model achieved a minimal RE of 0.047 (4.7%). Furthermore, the predictability coefficient (R²) yielded a value of 0.958, indicating excellent model performance. In conclusion, CO₂ can be captured from greenhouse gas emissions and as such could help to reduce the effect of climatic change in developing countries such as Nigeria.