Insight into mechanisms behind complex reactions by high-dimensional vectorized dynamic analysis

Insight into the mechanisms governing complex reactions requires a thorough and reliable analysis integrating various detected signals. This paper presents a vectorized method for describing chemical reactions and comprehensively discusses the theory and framework of high-dimensional vectorized dynamic analysis for complex reactions. Experimental studies of typical reaction processes such as thermal decomposition of calcium carbonate, thermal oxidation of copper(II) sulfide (CuS), and thermal treatment of industrial waste of aluminum (Al) dross were conducted and analyzed within the high-dimensional vectorized framework. The study identified five sub-reactions of thermal oxidation of CuS, six sub-reactions of Al dross, particularly cross-reactions, competitive reactions, and consecutive reactions that emphasize the mechanisms governing complex reactions. Besides, the study discusses the feasibility, functionality, and application of high-dimensional vectorized dynamic analysis for complex reaction processes including dimension selection, sub-reaction identification, and determination of sub-reaction kinetic parameters. Overall, the high-dimensional vectorized dynamic analysis is confirmed as a scientifically sound method for gaining insight into the mechanisms behind complex reactions. A combination of artificial intelligence (AI) and the high-dimensional vectorized dynamic analysis will further improve significantly the capabilities of identification and analysis of the complex reaction processes.