Optimization of extraction technology of alkaloids in lotus leaf based on BP neural network

Abstract

In order to overcome the bad precision of fitted error, lower accuracy optimization results and other flaws, when extraction technology of the lotus leaf alkaloids was optimized by response surface method or regression analysis method, a linear constraint optimization method based on BP neural network is proposed. The testing program of three factors, three level was designed, which selected the hydrochloric acid mass fraction, ultrasound time, liquid-solid ratio as experimental factors. Taking the experiment data as training sample, the BP neural network model of the lotus leaf alkaloid yield and the influencing factors was obtained, and it was optimized by the proposed optimization method. The optimal parameter combination of extraction technology for lotus leaf alkaloid was obtained as follows: extraction temperature 60 °C, ultrasonic power 500W, hydrochloric acid mass fraction 0.3%, ultrasonic time 43 min, liquid-solid ratio 27, the yield of lotus leaf alkaloids under this process condition is 4.26 mg/g. It better than the best extraction technology obtained by response surface method. The obtained results is used for verification experiment, the verification results shown that the method has high fitting accuracy and stable optimization results, which optimize the extraction technology for lotus leaf alkaloid.

Batch synthesis of 2,4,6‐trinitro‐3‐bromoanisole and its thermolysis and combustion performance

“…Moreover, the thermal decomposition of TNAZ is significantly earlier than that of TNBA. The various thermodynamic parameters of TNBA and other carriers were calculated using Equations ( 3)-(10) [18][19][20][21][22], and the thermodynamic, kinetic, and thermal stability of its thermal decomposition were characterized. The results are shown in Table 2.…”

Parameter Optimization Design of Precision Seeding Device Based on the Bp Neural Network for Panax Notoginseng

“…The BP neural network-based constrained optimization method is a new approach for the optimization of black box problems and includes single-objective optimization methods and multiobjective optimization methods (Dong et al, 2021;Wang et al, 2017;Dong et al, 2018a;Dong et al, 2019). The proposed method is based on the principle of iterative optimization via mathematical programming methods and the functional relationship fitting of BP neural networks.…”