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Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of certain power systems equipped with compressors. To calculate and predict the performance of a compressor under all operating conditions through limited data, the interpolation method was combined with a support vector machine (SVM). Based on the known data points of compressor design conditions, the interpolation method was adopted to obtain training samples of the SVM. In the calculation process, preliminary screening was conducted on the kernel functions of the SVM. Two interpolation methods, including linear interpolation and cubic spline interpolation, were used to obtain sample data. In the subsequent training process of the SVM, the genetic algorithm (GA) was used to optimize its parameters. After training, the available data were compared with the predicted data of the SVM. The results show that the SVM uses the Gaussian kernel function to achieve the highest prediction accuracy. The prediction accuracy of the SVM trained with the data obtained from linear interpolation was higher than that of cubic spline interpolation. Compared with the back propagation neural network optimized by the genetic algorithm (GA-BPNN), the genetic algorithm optimization of extreme learning machine neural network (GA-ELMNN), and the genetic algorithm optimization of generalized regression neural network (GA-GRNN), the support vector machine optimized by the genetic algorithm (GA-SVM) has a better generalization, and GA-SVM is more accurate in predicting boundary data than the GA-BPNN. In addition, reducing the number of original data points still enables the GA-SVM to maintain a high level of predictive accuracy. [ABSTRACT FROM AUTHOR]

In our work, the novel sparsely and compactly MoO3 nanoflowers were synthesized by a hydrothermal method assisted with polyvinyl pyrrolidone (PVP) and without any template, respectively. Through X-ray diffraction and scanning electron microscopy, we can confirm that we have prepared high purity MoO3 and can observe that both nanoflowers are formed by self-assembly of thin nanosheets. They have similar morphology, size and formation mechanism, but differ from pore size and quantity. Because of the above differences, sparsely MoO3 nanoflowers have a larger specific surface area and form more enclosed micro-reaction chambers that make it difficult for ethanol to escape. Allowing gas molecules to be more widely distributed among the sparsely sample surface and obtained fully reaction time. Thus, the gas sensor based on compactly MoO3 nanoflowers exhibits a better response and recovery characteristic. [ABSTRACT FROM AUTHOR]