Assessment of Texaco Syngas Components Using Extreme Learning Machine Based Quantum Neural Network

Quantum neural computing has nowadays attracted much attention, and tends to be a candidate to improve the computational efficiency of neural networks. In this paper, a new quantum neural network (QNN) is proposed based on quantum mechanics of superposition and collapse, etc. Instead of gradient descent methods and evolutionary algorithms, extreme learning machine (ELM) is introduced to analytically identify the parameters of the QNN. The ELM-QNN model is applied to the online and real-time assessment of the syngas components in a Texaco gasification process. The application would effectively avoid the problems of time delay and low accuracy which result from the manual analysis. In order to eliminate the redundant information stored in variables, principal component analysis (PCA) is adopted to reduce the number of input variables of ELM-QNN. The results indicate that ELM-QNN combined with PCA method has satisfied computational accuracy and efficiency. The PCA-ELM-QNN is very capable of being used for the real-time measurement of Texaco syngas components.