Modeling and Simulation of Chemical Machinery Performance Based on GA-Bp Algorithm

As a whole energy system, chemical machinery equipment has become a research topic in today's research through the research on the thermal behavior of its working fluid, especially the effective utilization of energy directly related to its internal energy conversion, transfer and expansion. One of the important contents of chemical machinery. The purpose of this paper is to study the modeling and simulation of chemical mechanical properties based on GA-Bp algorithm. Traditional chemical- mechanical models do not have the ability to simulate, and the results obtained are often very inaccurate. To address this problem, a new chemical-mechanical performance model based on the GA-Bp algorithm is developed in this paper. GA-Bp algorithm has the advantages of Bp algorithm and genetic algorithm at the same time, and it is easy to use. Combined with the breadth of the Bp algorithm and the characteristics of the genetic algorithm full search algorithm, combined with the general ability, mapping ability and full search ability of the two algorithms, a neural network training algorithm was developed. The GA-Bp algorithm reduces the search for the best solution by increasing the size of the unit, thereby increasing the number of training sessions for neural network training. The experimental results show that the chemical model based on the GA-Bp algorithm is more accurate. Experiments have proved that the gap between the chemical mechanical performance model of this paper and the traditional pure Bp is about 3–4 times, and the prediction performance speed can be reduced to 1/10 compared with the traditional algorithm.