Optimization of diesel substitution rate based on piston maximum temperature pattern recognition in dual-fuel engine

The diesel and natural gas dual-fuel engine has gained increasing interest in recent years because of its excellent power and economy. However, the diesel substitution rate cannot be controlled optimally, owing to the lack of a feedback indicator reflecting the cylinder combustion process, which easily leads to a serious thermal load problem. This paper presents a closed-loop control with feedback from a piston maximum temperature (PMT) pattern to regulate the diesel substitution rate in real time. A v-support vector machine (v-SVM) is proposed to train classifiers for online recognition of the PMT pattern. Nitrogen oxide (NOx) emission levels, excess air coefficient, engine speed and inlet pressure are chosen as feature variables. The PMTs, calculated by finite element analysis in ANSYS, are utilized to determine the labels of feature data. Moreover, 10-fold cross-validation is employed to choose the optimal kernel function, kernel parameters and penalty factor. A synthetic minority oversampling technique (SMOTE) is introduced to remedy the class imbalance problem in training classifiers. Furthermore, a timer-based debouncing mechanism is employed to alleviate the dynamic process influence on the PMT pattern recognition. Experiment revealed that the classifiers yield desirable predictions, with classification accuracies higher than 90%. Meanwhile, the diesel substitution rates are regulated to appropriate values through the closed-loop control algorithm, which guarantees that the dual-fuel engine runs in its safe region and maintains its excellent economy.