The phosphine-catalyzed [3 + 2] and [3 + 3] annulation reactions of azomethine imines and ethyl 2-butynoate were developed, providing 1,2-dinitrogen-containing heterocycles tetrahydropyrazolopyrazolones and tetrahydropyrazolopyridazinones in moderate to good yields.
In order to address the challenge of accurately predicting nitrogen oxide (NOx) emission from diesel engines in transient operation using traditional neural network models, this study proposes a NOx emission forecasting model based on a hybrid neural network architecture combining the convolutional neural network (CNN) and long short-term memory (LSTM) neural network. The objective is to enhance calibration efficiency and reduce diesel engine emissions. The proposed model utilizes data collected under the thermal cycle according to the world harmonized transient cycle (WHTC) emission test standard for training and verifying the prediction model. The CNN is employed to extract features from the training data, while LSTM networks are used to fit the data, resulting in the precise prediction of training NOx emissions from diesel engines. Experimental verification was conducted and the results demonstrate that the fitting coefficient (R2) of the CNN-LSTM network model in predicting transient NOx emissions from diesel engines is 0.977 with a root mean square error of 33.495. Compared to predictions made by a single LSTM neural network, CNN neural network predictions, and back-propagation (BP) neural network predictions, the root mean square error (RMSE) decreases by 35.6%, 50.8%, and 62.9%, respectively, while the fitting degree R2 increases by 2.5%, 4.4%, and 6.6%. These results demonstrate that the CNN-LSTM network prediction model has higher accuracy, good convergence, and robustness.
The purpose of this study is improve calibration efficiency and obtain accurate diesel engine operating parameters, achieving improved diesel engine emissions and fuel efficiency. A PSO‐RBF (particle swarm optimization‐radial basis function) diesel engine performance prediction model combining an improved PSO (particle swarm optimization algorithm and an RBF neural network is proposed. A space‐filling experimental design method for diesel engine performance prediction is proposed based on the actual operating conditions of diesel engines. Training data are collected at the bench to build the RBF prediction model. An optimization PSO search method is proposed to improve the PSO optimization capability. An improved PSO algorithm is used to optimize the model and improve prediction accuracy. Then the BSFC (diesel brake‐specific fuel consumption), NOx ((Nitrogen Oxid), CO (Carbon Monoxide), and HC (Hydrocarbon) prediction models are constructed. Results show that the PSO‐RBF can find the global solution with good prediction accuracy and generalization ability during small amounts of data. The PSO‐RBF model fitting degrees of BSFC, NOx, CO, and HC are 0.9952, 0.9910, 0.9820, and 0.9870 respectively. Mean relative errors are 3.02%, 2.78%, 1.39%, and 2.01% respectively. Mean absolute percentage errors are 1.58%, 3.26%, 3.69%, and 2.96% respectively. The optimized model R2 (Model determination coefficient) is improved by 0.065, 0.102, 0.10, and 0.085, respectively.
This paper studies the operating uniformity of a high-pressure common-rail diesel engine based on crankshaft segment signals. Engine nonuniformity or unevenness refers to the crankshaft torque fluctuation caused by cylinder-to-cylinder differences, which are caused by misfiring or difference in fuel supply or air supply. The experiments were conducted on a diesel engine of the YN30CR model. Based on the relationships among the cylinder pressure, the instantaneous crankshaft angular speed, and the crankshaft segment signal, it is found that the crankshaft segment signal can be used to characterize the operating nonuniformity of the diesel engine. Based on the Fast Fourier Transformation (FFT) from the angular domain to the frequency domain in analyzing the fluctuation patterns of the crankshaft segment signals at different operating conditions, the nonuniformity information was extracted. In order to further reveal the relationship between the fluctuation phenomena of the crankshaft segment signal and the cause of the fluctuation, the torque acting on the crankshaft of the multi-cylinder diesel engine was studied to quantify the operating nonuniformity of the diesel engine. The results show that the crankshaft segment signal can reflect the uniformity of the engine torque very well in both phase and amplitude, and the degree of nonuniformity of the engine torque can be reflected by the amplitudes of the low-order non-dominant harmonics of the crankshaft segment signal below the firing frequency. The crankshaft segment signal is very consistent with the variation pattern of the engine torque.
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