Optimization Control of Coal Methanol Chemical Process Based on Neural Network Algorithm

Abstract

Nowadays, the world has become a unified factory. However, with the adoption of coal resources gradually increased, the waste of coal resources and environmental pollution has increased. Under such occasion, methanol as an alternative energy source plays a more prominent role in the economy. But, the process and equipment of coal-to-methanol are far from perfect, which directly leads to the result that the adoption of coal- to-methanol technology is disadvantageous and lacks of development. Therefore, the production process of coal to methanol requires the following economical design. First, the coal should be saved through the intelligent improvement and the optimization of process, so as to continuously improve the efficiency of the process. Secondly, in the process of coal to methanol, electric energy should be saved. Through the renewal of equipment, the energy consumption can be continuously saved. The real-time optimization control method can solve the problem of optimization and control the complex process industry, so as to make the process run as economically optimized as possible. Besides, the on-line learning ability of neural network makes it a unique advantage in on-line controller, and hence, it is an important tool for real-time optimization control. This paper makes full use of the advantages of neural network algorithm to optimize the process production of coal-to-methanol. First of all, the process of coal to methanol is mainly focused on, and the key links of the production process are introduced. Secondly, the neural network algorithm is studied. Then, a control method based on neural network algorithm is proposed for real-time optimization control of coal-to-methanol production. Finally, in the simulation experiment, the proposed method is verified and analyzed. Experimental results show that the control method of neural network algorithm can make sure the smooth operation of coal- to-methanol production process, and realize a high control precision. When the efficient production conditions can be guaranteed, the energy consumption of the system can also be effectively reduced. Similarly, the energy saving effect is quite remarkable.