The development of Information Technology has intruded the chemical industry. In the conventional chemical industry, humans are involved in monitoring the chemical evaporation processes. If there is any damage, then humans suffer enormously. These drawbacks are overcome in the chemical industry by implanting the sensors to the required blocks for monitoring the levels of chemical substances. An alert system can be introduced with an artificial intelligence algorithm to regenerate the process using details updated in the database. In this research, the machine-based Time-Temperature Superposition (TTS) method is implemented to monitor the chemical reactions in the chemical component manufacturing company.
Chemical enterprises are presently confronted with several difficult issues, including high power consumption, dangerous risk evaluation, and environmental regulation, all of which push industrial and academic institutions to develop new technologies, catalysts, and materials. Chlorinated polyethylene (CPE) is a polymer made by replacing H2 molecules in high density-(C2H4)n with chloride ions. CPE elastomers are made from a high density-(C2H4) backbone, and it was chlorinated using a free radical aqueous slurry technique. However, such fundamental polymer characteristics are insufficient to explain the performance characteristics of chlorinated polyethylene elastomers. Artificial intelligence (AI) has had a massive effect on all sections of the chemical sector, with tremendous potential that has revolutionized value supply chains, enhanced efficiency, and opened up new ways to the marketplace. As a result, in this research, we offer a methodology for the performance characterization of chlorinated polyethylene based on artificial intelligence (AI) and wireless network technology. The AI tools can search through enormous databases of known compounds and their attributes, leveraging the data to generate new possibilities. The dataset is first gathered. The chemical characterization is classified using the
K
-nearest neighbor (KNN) technique. This program was created to examine molecule structures and forecast the outcomes of new chemical reactions. Bayesian optimization is used to improve characterization performance. The proposed method will contribute to the future usage of AI in the chemical sector.
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