Design and Deconstruction of Chemical Process Flow Based on Deep Learning

Abstract

Chemical process design is something that researchers must do before conducting chemical reaction experiments, and this step is crucial for the entire chemical production. Because even if the relevant basic information of the institute is obtained, most of the above data have not been verified by experiments, and researchers need to confirm through experiments. In addition, because the market demand for chemical process products is very large, the types of chemical substances are increasing, chemical equipment and instruments are becoming more and more complex, and these require researchers to design and study in advance. This ensures the smooth production of products and the safety of researchers. However, the expansion of the equipment scale and the complexity of the equipment make it more and more difficult to design the chemical process flow. There are many influencing factors and levels to be considered when designing the process, and the data is also very difficult to predict and classify. In order to solve these problems, this study discussed the countermeasures to deal with chemical process flow design in depth. Using the method of deep learning, the problem of chemical process design was analyzed, and the performance of the method was experimentally studied. The results show that the chemical process flow based on deep learning is better than other process designs, and its accuracy rate is higher than 94% in 10 experiments, which is higher than the other three methods. It can be seen that this chemical process method can meet the needs of the current chemical process, and the product quality and work efficiency are greatly improved.