# Heat Load Forecasting of Marine Diesel Engine Based on Long Short-Term Memory Network
## 3 Case Study  
### Principle Analysis and Data Processing  
In a ship, the power source is composed of the main engine and an auxiliary engine. The auxiliary power system is composed of machinery other than the diesel engine (main engine), including the fuel system, lubricating oil system, air system, cooling system, and other mechanical equipment. The main and auxiliary engines work together to propel the ship, and its composition structure is shown in Figure 3.  
On the basis of the mechanism and data of the ship, the heat load of the marine diesel engine during operation is accurately reflected by the exhaust temperature. The amount, perfection, and timeliness of fuel combustion in the combustion chamber can be reflected by the exhaust temperature, as well as the high temperature heating time and brightness of combustion chamber components. Hence, exhaust temperature can be used to predict the heat load of diesel engine set. The exhaust temperature of a single cylinder is predicted as an example in this study to analyze the trend of heat load variation and the operating performance of the combustion chamber. The high exhaust temperature of the cylinder is due to poor internal combustion, which is related to the amount of fresh air in the cylinder, cooler cooling effect, injector atomization quality, fuel viscosity, and cylinder compression pressure. The sensor is used to monitor its working condition and collect factors related to exhaust temperature, including high-temperature cooling, freshwater outlet temperature, cylinder liner cooling water inlet pressure, piston cooling oil outlet temperature, and fuel pressure after the fuel filter. Determining the correlation and dependence among these data is important to predict the exhaust temperature of marine diesel engine sets. Sensor monitoring data of the Chinese vessel Shanghai Fuhai are used in this study, which are uploaded every 25 min. The sampled relevant data of initial variables are listed in Table 2. Field data for two months show that 28,160 pieces of ship data are measured via the ship's sensors and constitute the data set, which is randomly divided into training and test sets at a ratio of 7:3. Figure 3: Sketch of the composition structure. The time series correlation data \(X^{\prime}_{t}\) associated with the exhaust temperature are collected as follows. \[X^{\prime}_{t}=\left\{T_{a1},T_{o1},T_{o2},P_{o1},T_{o3},T_{w1},P_{w1},T_{w2},T_{ a2},T_{a3},T_{f1},T_{o4},P_{f1},P_{f2},P_{w2},P_{o2},T_{a4},N_{T},P_{a1}\right\}\]  
The turbocharger is driven by the inertial impulse of the exhaust gas to drive the turbine, and then fresh air is pressurized into the cylinder. Thus, overlapping variables and supercharger front \(T_{a3}\) and rear \(T_{a4}\) exhaust temperatures must be eliminated to obtain time series data as follows. \[X^{\prime\prime}_{t}=\left\{T_{a1},T_{o1},T_{o2},P_{o1},T_{o3},T_{w1},P_{w1},T_{ w2},T_{a2},T_{f1},T_{o4},P_{f1},P_{f2},P_{w2},P_{o2},N_{T},P_{a1}\right\}\]  
Spearman correlation coefficient method is used for feature selection of data, and the input of neural network is determined by the correlation between two factors, as shown in Figure 4.  
According to the correlation matrix in Table 1 and the above figure, the correlation coefficient between the exhaust temperature and \(T_{o2}\) is 0.8997. Hence, the turbocharger lubricating oil outlet temperature is highly relevant to the exhaust temperature. This finding is consistent with the actual scenario. The viscosity of the lubricating oil will be affected by the temperature of the lubricating oil and increase the exhaust temperature. The correlation of the variable \(T_{w2}\) is 0.8639, and how much heat is taken away from