{'fraction_non_alphanumeric': 0.012573344509639563, 'fraction_numerical': 0.0, 'mean_word_length': 5.56043956043956, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Study on metal corrosion model in chlorine environment based on BP neural network

In the process of the development of chemical industry, with the continuous improvement of the production automation of chemical industry, the corrosion problem of chemical instruments is becoming more and more obvious. The corrosion of chemical instruments will directly or indirectly cause great economic losses to chemical enterprises. Therefore, in the development of chemical industry, with the continuous development and application of the automation trend of production units, the solution of chemical instrument corrosion problem is the key problem in the current chemical production process. In this paper, the corrosion data of instrument materials in chlorine environment are selected in the production of chlor-alkali industry. Based on the BP neural network training data, a mathematical model is established to predict the corrosion rate of metal materials. The experimental results show that the prediction results of BP neural network are basically consistent with the actual data, which proves the feasibility of applying BP neural network to predict instrument corrosion in chemical industry.