Model Development For Turbine Energy Yield (TEY), Carbon Monoxide (CO) And Nitrogen Oxide (NOx) From Gas Turbine Power Plant - Wan Anuar, Wan Ahmad Aizat (Universiti Sains Malaysia, 2021)
In order to combat the environmental issues that have been constantly rising since the start of the first Industrial Revolution in the 18th century, many solutions have been introduced and been applied around the world. One of the approaches for overcome air pollution issues from greenhouse gas emissions is by monitoring their release from its most abundant sources, for example, gas turbine power plants. Predictive emission monitoring system (PEMS) is one of the methods for monitoring these greenhouse gas emissions. It is powered by an artificial neural network (ANN) by taking into account the collected data from Kaya et al. (2019) such as ambient temperature, ambient pressure, ambient humidity and many more from selected gas turbine power plants for the emission prediction purpose. Several models will be developed and will be classified according to their responding outputs. Multi input single output (MISO), where carbon monoxide (CO), nitrogen oxide (NOx) and turbine energy yield (TEY) will be operated as separate output and multiple inputs multiple outputs where CO, NOx and TEY will be its output simultaneously. For each model’s type, it will be further classified into the model with input selection and the model without input selection. The performance of the model will be demonstrated by the value of its respective mean squared error (MSE), R and R2. The model with input selection is having almost the same performance as the model without input selection although having fewer input variables compared to the latter. R2 values for each training model with input selection are 0.5094, 0.8260, 0.7573 and 0.6922 for the model with CO, NOx, TEY as output and MIMO model respectively compare to the R2 values for each training model without input selection are 0.5382, 0.8278, 0.7627 and 0.6950 for model with CO, NOx, TEY as output and MIMO model respectively. MIMO model is the better model compared to MISO, even though it combines 3 outputs and could be more complex, but ANN still able to predict accurately. Therefore, developing MIMO model could be better than developing MISO model as it will reduce model times (one model for 3 outputs rather than a separate model for each output).