Integration of ion transport membrane with conventional powerplant to enhance the plant capacity with improved power production.

Ion Transport Membrane (ITM) is an emerging technology for producing O2 by separating air in its membrane. To decrease energy loss in air separation unit and to increase the overall efficiency of a power generation unit ITM is added with the gasification unit in this model. Ceramic materials are generally used to make the ion transport membrane that produces oxygen by conducting oxygen ions at a specified temperature. Potential advantages can be gained by integrating ITM technology with power generation units as 99% pure oxygen is produced from ITM. Using ITM air separator is more beneficial compared to cryogenic air separation as ITM technology helps to improve IGCC overall efficiency and also reduces plant auxiliaries than that of power generation systems integrated with cryogenic. This paper proposed a novel and effective integration of ITM, gas turbine, HRSG system, gas clean up system and gasification unit to produce sustainable energy. Environmental impacts are considered to design this integrated power generation unit. The proposed model achieved a high gross electric efficiency of 47.58% and high net power of 296730 kW which revealed its potentiality compared to available cryogenic ASU-based combine cycle power plants.

Sustainable operations of a combined cycle power plant using artificial intelligence based power prediction.

Combined Cycle Power Plants (CCPP) are an effective method for Power generation due to their high thermal efficiency, low fuel consumption, and low greenhouse emissions. However, investing millions into building a power plant without knowledge of the power generation capacity seems unproductive. With the help of AI, we have tried to eliminate this conundrum. The present study focuses on the prediction of power produced by a 747 MW Combined Cycle Power Plant (CCPP) using a Back Propagation Neural Network (BPNN) and compares its results with the actual data from CCPP. BPNN is a regression-based prediction technique that is utilized in this study to develop a predictive model and train it using the following input features: Ambient Temperature, Ambient Pressure, Mass Flow rate of fuel in Gas Turbine 1, and Mass Flow rate of fuel in Gas Turbine 2. The Predictive Model with 10 neurons in the hidden layer was found to be most effective with Mean Squared Error (MSE) value, for the validation dataset, of 0.0063237. CCPP is also analyzed through a thermodynamic model, developed using EES. A detailed energy analysis is carried out and the results were compared with predicted and actual data. It was found that the thermal efficiency and total power generation of actual, predicted, and simulated models were 27.541% & 667.32 MW, 28.238% & 683.48 MW and 28.201% & 683.16 MW, respectively. A parametric study was further carried out to investigate the significance of operating parameters on power output and it was concluded that the temperatures across the Gas turbines have a significant impact on the performance of CCPP. Finally, Methane was replaced by 3 different fuels, one by one, and the effect of each fuel was investigated thermodynamically. It was found that the Lower Heating Value (LHV) of fuel was an important parameter in achieving a higher power output. It can be summarized from this research work that predictive models do have accuracy and such data science techniques can be used as a substitute for extensive thermodynamic calculations.

Exergy and environmental analysis of a novel turbine inlet air cooling technique for power augmentation in a CCPP based on waste energy.

Combined cycle power plant (CCPP) play a crucial role in providing electricity worldwide. Therefore, researchers and industrialists always focus on developing and improving its performance. One of the factors that affect the performance of CCPPs is weather conditions. As weather conditions change, the air density of the environment changes, which ultimately affects the production power of the gas turbine (GT) and consequently the CCPP. To mitigate the effects of weather on CCPPs' performance, power augmentation methods are developed. In the present research, a novel technique is proposed to reduce the air temperature entering the GT by recovering waste heat from the exhaust gas. The heat content of the exhaust gas is used as the heat source of an ejector refrigeration cycle (ERC), and the produced cooling capacity is used to cool down the air entering the GT. Exergy and environmental analyses are performed to investigate the proposed method's effect on exergy efficiency, environmental factors, and sustainability index. The results indicate that by the proposed method the power production of the CCPP is increased 6.26%.

Heat recovery steam generator (HRSG) three-element drum level control utilizing Fractional order PID and fuzzy controllers.

Shrink and swell is a phenomenon that causes transient variability in water level once boiler load variation occurs. The leading cause of the swell effect is the steam demand changes and the actual arrangement of steam generating tubes in the boiler. Steam bubbles beneath HRSG drum water make the level control very difficult, particularly with significant disturbances in the input heat to HRSG. Plant shutdown may occur in some situations, and combined cycle plant efficiency is diminished. The recently applied control methods in industry are single-element and three-element control with PID controllers, but these methods are not well suited for substantial load changes. The main aim of this paper is to investigate the shrink and swell phenomenon inside HRSG power plants. In addition to the existing PID loops, two different standalone controllers, namely, the FOPID controller and fuzzy controller, are implemented with the HRSG model. Besides, Artificial Bee Colony (ABC) algorithm is used to tune FOPID efficiently. Based on overshoot, rise time, ISE, IAE, ITAE as performance measures, the comparison has been held between the three controllers. Simulations show that how the ABC optimization algorithm is efficient with PID, FOPID. It turns out that the proposed method is capable of improving system responses compared to the conventional optimal controller.