The acetylene conversion rate and vinyl chloride production capacity are the main economic indexes for vinyl chloride synthesis, and the reaction temperature is an important operating parameter to prevent the Hg active component from being loss. These three factors have not been taken into consideration simultaneously in the traditional optimization process, making it difficult to achieve optimization targets perfectly for industrial application. To overcome this problem, an efficient strategy framework was proposed based on a hybrid model. Compared with conventional paradigms, the proposed framework could not only reduce computational expense but optimize these two economic indexes with a constrained reaction temperature simultaneously. In addition, a machine learning method was used to conduct a feature analysis, which can reveal the potential interaction between different variables so key variables of this reactor could be identified. To demonstrate and verify this framework, multi-objective optimization involving multiple variables with constrained conditions for the industrial reactor was conducted from design and operation perspectives, respectively. The proposed strategy could provide optimal operational direction for the industrial reactor from these design and operation aspects, which contribute to the sustainable and highly efficient process development in this field. For the first section of an industrial vinyl chloride reactor, this strategy could realize significant improvement in the acetylene conversion rate from 81.34% to over 95.00% and in the vinyl chloride production capacity from 2.60 to above 3.40 mol/h in the operation scenarios, which can meet production requirements. So, the second section of the traditional reactor system is not needed at all.
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