Isonicotinic acid yield prediction by BP neural network based on optimization of grey wolf algorithm

Abstract

Isonicotinic acid is used as a pharmaceutical intermediate, mainly for the production of the anti-tuberculosis drug isoniazid. Prediction of isonicotinic acid yield using data from the production process is helpful to ensure product quality and improve production efficiency. Traditional BP neural networks have lots of disadvantages such as slow convergence, easy to fall into local minima and sensitive to the selection of initial weights and thresholds. In order to predict isonicotinic acid yield efficiently and accurately, a prediction model of isonicotinic acid yield based on the Grey Wolf Optimizer (GWO) optimized BP (GWO-BP) neural network was proposed. The prediction model was used to predict the historical production data of isonicotinic acid in a plant, and the experimental results showed that the accuracy of the proposed GWO-BP prediction model was higher compared with the traditional BP and GA-BP prediction models.