Efficient and flexible operation is essential for competitiveness in the energy market. However, the CO₂ emissions of conventional power plants have become an increasingly significant environmental dilemma. In this study, the optimization of a steam power process of an IGCC was carried out, which improved the overall performance of the plant. CCPP with a subcritical HRSG was modelled using EBSILON Professional. The numerical results of the model were validated by measurements for three different load cases (100, 80, and 60%). The results are in agreement with the measured data, with deviations of less than 5% for each case. Based on the model validation, the model was modified for the use of syngas as feed and the integration of heat into an IGCC process. The integration was optimized with respect to the performance of the CCPP by varying the extraction points, adjusting the steam parameters of the extractions and modifying the steam cycle. For the 100% load case, a steam turbine power achieved increase of +34.2%. Finally, the optimized model was subjected to a sensitivity analysis to investigate the effects of varying the extraction mass flows on the output.

In this study two mathematical models, rate-based and equilibrium-stage models in Aspen Plus process simulator, were used to simulate the tar absorption processes using soybean oil as a solvent in a research lab-scale experiment. The matching between simulation results and experimental data shows a good agreement. The simulation results predicted by the rate-based model show a higher level of agreement than the equilibrium model compared with the experimental data. Analysis study of tar absorption process was carried out which revealed the effect of temperature and flow rate on the soybean oil, and height-packed bed on tar removal efficiency. The methodology of selecting the optimum (most economical) operation conditions has also been performed in this study.