Abstract
AbstractIn order to solve the problem that the total sugar content of the chlortetracycline fermentation tank can not be automatically detected online, a prediction method which combines the output recursive wavelet neural network and the Gauss process regression is proposed in this paper. A soft sensor model between the measurable parameters (inputs) and the total sugar content (output) of the chlortetracycline fermentation tank was established. The soft sensor model was trained by self updating algorithm. Based on field data, the accuracy and generalization ability of the soft sensor model were analyzed. It is shown that the prediction accuracy of the combined model proposed in this paper is better than that of other single models. The results demonstrate the superiority of the method, and MRE and RMSE are used to evaluate the performance of the soft sensor model. It shows that the prediction precision of the soft sensor model based on ORWNN-GPR combination is relatively high in the long period of fermentation, and is suitable for on-line prediction of the total sugar content of the chlortetracycline fermentation tank. The soft sensor method can effectively reduce the labor intensity of the analysts and saves the production cost for enterprise.
Highlights
Chlortetracycline is a tetra ring spectrum antibiotic, widely used in medical treatment, agriculture and animal husbandry
The results show that the Output Recursive Wavelet Neural Network (ORWNN)-GPR integrated model has better prediction accuracy than the single model and higher online prediction accuracy of total sugar content
In the working cycle of the chlortetracycline fermentation tank, the soft sensing method based on ORWNN-GPR model can maintain the high precision of the total sugar content prediction value, effectively solve the problem of long time and serious lag in artificial sampling analysis, and provide rapid and reliable data support for the optimization control of the rate of sugar supplement, which can effectively reduce the cost of production and improve the production efficiency of the chlortetracycline fermentation tank
Summary
Chlortetracycline is a tetra ring spectrum antibiotic, widely used in medical treatment, agriculture and animal husbandry. Due to the large viscosity of fermentation broth, lack of total sugar content online monitoring instrument, and the current detection method is offline analysis of artificial site sampling. It has large labor intensity, large time lag and low measurement efficiency. In this paper, based on the fermentation process of chlortetracycline, artificial intelligence method was used to establish an online soft sensor model of total sugar content. The experimental results show that the soft sensor model has higher prediction accuracy of total sugar content and it can meet the prediction requirement of difficult parameters in industrial fermentation process. Each node in the wavelet layer must perform the operation of the wavelet function, it can be expressed as
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