Open AccessPerformance prediction of tobacco flavouring using response surface methodology and artificial neural network
Author(s) -
Chen Lin,
Yuan Ruibo,
Liu Ze
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.9038
Subject(s) - artificial neural network , response surface methodology , matlab , design of experiments , volumetric flow rate , software , linear regression , computer science , process engineering , biological system , mathematics , artificial intelligence , engineering , machine learning , statistics , mechanics , physics , biology , programming language , operating system
This study was to predict the optimum condition for leaf flavouring in cigarette manufacturing. To this purpose, an integrated research was used by using response surface and artificial neural network. A series of tobacco flavouring experiment's factors were designed by Experimental Design software. The MATLAB software's Neural Network function was used to forecast the responses, and the optimal solution configuration was coming out from the Response Surface Analysis Method. In the optimum condition, moisture removal opening, roller speed and tobacco process flow, pressure and feed liquid gas ejector flow are 18.60%, 10.74 rpm, 5314.11 kg/h, 3.70 bar and 243.63 kg/h, uniformity of the evaluation index and the utilization rate of material liquid distribution are 93.088% and 98.694%. With the corresponding experimental, results are consistent, under the condition of the error to less 7%, the test results show that through a few experimental data of predictive results of the neural network and response surface design has a certain practicability.