# ISO 50001 Data Driven Methods for Energy Efficiency Analysis of Thermal Power Plants
## 2 Case Study
Thus, starting from the analysis of the physical process, a parameter was defined to filter out the outlier over-consumptions during the real operation time of the past three years, used as the available input dataset. A correlation study was preliminarily performed between the available data and the actual consumptions of the fan system. The selected variables were then used as inputs for an ad hoc feed-forward neural network model trained on the pre-filtered dataset. The complex parameters affecting the performance of shower cooling towers include geometrical parameters (initial diameter of water droplets, height of the tower, etc. ), physical parameters (water to air mass flow rate ratio, inlet air velocity, initial water droplets speed, etc.) and environmental conditions (ambient air temperature, relative humidity, other minor factors). A correlation study was preliminarily performed on the wide data set available (more than thousands of plant sensor parameters from the DCS) to limit the input variables to the most significant ones adopting the Pearson correlation coefficient method, and many of these parameters have a strong cross-correlation that cannot be neglected. For example, the dependence of the outlet water temperature on the variation of the ambient relative humidity decreases when the ambient dry-bulb air temperature reduces [23]. An important variable to consider in the neural model is also the condenser vacuum level. It is a key aspect for the efficiency of the steam turbine since the potential work available is proportional to the pressure differences between the inlet turbine pressure and the condenser pressure. The vacuum is created thanks to the vacuum ejectors and the huge  
Figure 4: Cooling tower representation [23]. difference between the specific volume of the steam, which for simplicity can be considered as an ideal gas, and the specific volume of water. The vacuum level again is strictly dependent on the condensation of the steam, and thus on the temperature of the cooling water circulating into the towers: the lower the temperature of the cooling water, the more steam condensates and the more vacuum is created. Many other correlation issues could be found in other plant units, and the analysis takes quite a long time, but we use the active power produced by the turbine units as the main output variable related to the overall plant performance. The need for a low value of the vacuum requires maximum effort from the fans. Thus, when the vacuum level is not at its minimum, more fans are activated by the field operators even if the environmental conditions would not require it. For this reason, the inefficiencies are searched only at vacuum levels considered already optimal for the steam turbine efficiency. Optimal vacuum values can be considered in the range of 30-36 mbar. In Table 1, we summarize the critical variables chosen as input of the neural network. The highest positive and negative correlation coefficients have been included. Alongside the correlation coefficient, the physical explanation of the chosen variables has also been considered. The last row of Table 1 represents the chosen single output of the neural network; the remaining 10 variables are the reduced input data set used in the design of the neural model. Thus, the data set is composed of a matrix of 10 \(\times\) 16,171 rows that correspond to the filtered hourly data from March 2018 to August 2020. The expected output is the estimation of current absorptions of the fans running in the cooling towers. In the next section, the developed machine learning model is described in order to be included in the PDCA cycle, with the aim of improving the process and its efficiency.