# Heat Load Forecasting of Marine Diesel Engine Based on Long Short-Term Memory Network
## 1 Introduction  
In dealing with the increasing severity of fossil energy crisis and the strict emission requirements of internal combustion engines, the effective use of energy and environmental protection are also increasing in importance. If the diesel engine set has insufficient combustion, then the fuel-generated heat decreases, resources are wasted, black smoke and a large amount of CO and other harmful gases are discharged, and the environment is polluted, which will cause harm to human body through direct inhalation [1; 2; 3]. The diesel engine set is an important power source for ship navigation, and its normal working cycle is a major contributor to efficient transportation by sea, saving energy, and reducing emission [4]. Taking exhaust manifold as an example, the finite element method is used by [PERSON] et al. [5] to verify the effect of thermal load on its fatigue life. The high efficiency heat transfer model is used by [PERSON] et al. [6] to analyze the direct relationship between cylinder head fatigue life and average gas temperature. In addition, Chaboche model is established to analyze the local deformation and leakage of cylinder head under thermal cycle test [7]. The failure of the ship's exhaust valve was investigated and analyzed by [PERSON] et al. [8], and it is determined that the high temperature environment would lead to the expansion of microcracks and easy fracture. According to the above research, main equipment of the diesel engine set will be damaged by high heat load, the ship will be stopped, which will greatly increase the navigation cost, and the safety of ship equipment and environment willbe seriously affected [9; 10]. Previously, a Belize foreign ship lost control of its main engine due to excessive heat load at the floating attachment of the Yangtze River No. 20. Fortunately, it was timely assisted by the maritime department and did not cause a second accident. At present, ship data are detected by sensors and transmitted to terminals. However, when excessive heat load is detected by sensors, ship equipment and personal safety may have been damaged [11]. Therefore, the prediction of its heat load can achieve the preventive effect. The heat load of diesel engine can be accurately characterized by exhaust temperature, which can be estimated by predicting the exhaust temperature. However, factors affecting the exhaust temperature are typically influenced by uncertain dynamic environmental factors. As such, heat load prediction of marine diesel engine units is a challenging and meaningful task. It is usually used to analyze the heat load of marine diesel engine units through traditional methods, such as finite element analysis and linear regression modeling [12]. However, the accuracy of predicted parameters, results, and complex mapping relationships are difficult to model due to the complex process inside the combustion chamber. Complex and variable dynamic processes and nonlinear systems are modeled by neural network methods, and its continuous development led to various applications in marine diesel engines [13; 14; 15; 16]. Artificial neural network (ANN) was used by [PERSON] to replace traditional modeling to predict engine fuel consumption, effective power, and exhaust temperature. The mean error percentage (MEP) of the training test data was less than 2.7% [17]. Ignition timing, engine speed and air-fuel ratio were used as model inputs by [PERSON] et al. [18] to analyze whether machine learning can be used to effectively predict engine exhaust temperature. Four different algorithm combinations were used to evaluate the applicability of ANN. ANN was used by [PERSON] et al. [19] to predict the emission and performance of an ether single-cylinder diesel engine. The maximum mean absolute error range of 5% was obtained, and the regression coefficient (R2) was in the range of 0.9640-0.9878.