Soft Sensor Modeling Of Methanol Synthesis Tower Conversion Rate Based On The Fuzzy Neural Network

This thesis is sponsored by a company process optimization control of methanol project. Focus the mainly studies on the soft sensor of the methanol conversion rate. Some researchers have been done on the soft sensor of conversion rate in conversion process of methanol production. Improved differential evolution algorithm is applied in fuzzy neural network model soft sensor.Based on research platform of the methanol project, generate researches have been done in soft sensor area to find effective schemes to solve the soft sensor problem.Firstly, the architecture of soft sensor system and its key technologies are reviewed by studying some typical model methods, and the improved differential evolution algorithm has been proposed and used in soft sensor. In order to avoid getting into local optimum and improve the global optimization ability, a improved algorithm based on differential evolution and simplex method is proposed to solve and improve the algorithm convergence speed and global optimization efficiency, as well as to avoid the high system resource cost.Secondly, the algorithm is used for the prediction of conversion rate in conversion process of methanol production. The comparisons with a differential evolution and improved differential evolution show that the soft sensor, which is proposed in the thesis, can approach the expected result effectively, and can also improve the prediction precision.Finally, C program language is used to develop a soft sensor system which is implemented in methanol project.The algorithm proposed in this thesis can realize the prediction more reliability, accurately and effectively, under the preconditions of guaranteeing real-time ability and reliable of system operation. Besides, the success of the project the system not only can save the survey cost, but also can increase the economic efficiency of enterprises.