Effects prediction of heavy components in natural gas on purification unit by BP artificial neural network

As the heavy components in natural gas will affect both the operation effect of the desulfurization unit and the gas quality of the product gas, this actual production problem should be solved effectively.The absorption performance of MDEA solution under different conditions was determined with the MDEA solution absorption performance evaluation device.The effects of different heavy components on the absorption performance of MDEA solution was studied systematically.The key factors were selected by multiple factor analyses of variance to determine the influence degree, and an artificial neural network was used to establish a prediction model for the adverse effects of heavy components in natural gas.The results showed that heavy components i-C₅, C₆, C₇, C₈ and C₁₀ in the natural gas had obvious influences on the absorptive capacity of MDEA solution.All of them were effective input signals of the prediction model of BP neural network.The predicted value approximated to the real value, and the BP artificial neural network showed good accuracy and stability.Therefore, the BP artificial neural network could predict the adverse effects of heavy components in natural gas on the absorption performance of MDEA solution accurately and reliably.