Research On Data Acquisition For Wheat Processing And Optimization Algorithms

The food industry is closely related to the development and survival of human, and it is also related to the national economic development, social stability and national independence of the overall major strategic issues of China. The wheat processing is the important part of the food industry. With the development of science and technology, the wheat processing industry has entered a large-scale period. The development of the automation technology, information technology and artificial intelligence theory gradually meets the new demand for the wheat processing development, and made it to the direction of digital, information and intelligence technology. The scientific and technological level of the wheat processing in China has been greatly improved after decades of effort. However, due to the weak foundation and the lack of long-term investment, there is a big gap in general in production technology and information technology compared with foreign advanced countries. Therefore, considering that there is still much room for improvement in production technology of the wheat processing, we can use the relevant knowledge of the information technology and the artificial intelligence, develop a data acquisition system, get the real-time production data, and analyze the data with the artificial intelligence knowledge. On the basis of improving product quality and production efficiency, we can reduce energy consumption to enhance their market competitiveness.The main research contents in this paper as follow:(1) Through studying on the data acquisition and its components, and based on the wheat processing, a data acquisition system of wheat production process has been developed. The entire data acquisition is analyzed, including the design of hardware and software. Taking the data collection of the workshop energy consumption for example, the entire workflow of data acquisition system is described. It provides a foundation for the following analysis of the data on the workshop.(2) For the energy efficiency optimization problem of wheat processing, a method based on the GRNN(Generalized regression neural network) and CPSO(Chaos particle swarm optimization) is proposed. The method is based on the data which is collected in the workshop, and a predictive model based on generalized regression neural network is established by the data. The predictive model and the chaos particle swarm optimization are combined to find the optimal combination of parameters to make the energy consumption of flour production minimum. At the same time, the effectiveness of the chaos particle swarm optimization is analyzed. Finally, the simulation shows that the predictive model is effective, and the predictive model and the improved CPSO which are combined together can find the optimal parameter combination, and can optimize energy efficiency of wheat processing effectively.(3) For the problem of critical control points(CCPs) selection in wheat processing HACCP(hazard analysis and critical control point), an automatic CCPs identification method based on SVM(support vector machine) model was introduced with the artificial intelligence theory. In order to improve the model’s recognition stability and accuracy, an adaptive dynamic search particle swarm optimization(ADS-PSO) for the optimization of kernel function parameters in SVM was proposed. ADS-PSO introduces an evolutionary factor and threshold(ET) to estimate the evolutionary state and adjusts the search strategy adaptively. When evolutionary factor is larger than threshold, the original search method will be adopted; Otherwise, an opposite search method(OSM) will be used to increase the diversity. Besides, ADS-PSO defines an inertia parameter for the velocity. The simulation results show that the improved SVM model can identify the CCPs in wheat processing HACCP, and extend the application of HACCP.