The invention discloses a mechanism-
data driven mercury adsorbent design and wet
elution optimization method, which comprises the following steps of: firstly, analyzing adsorption characteristics of mercury and
hydrogen sulfide on
transition metal oxide and element reaction kinetic parameters of mercury species in a solution through
density functional theory calculation and thermodynamic calculation; then extracting characteristic values influencing adsorption and
elution performance, and carrying out data preprocessing; a
machine learning method is used for screening out key characteristic values leading the adsorption performance of mercury and
hydrogen sulfide, and
transition metal oxides with the maximum adsorption performance of mercury and the lowest
desorption energy of
hydrogen sulfide are predicted; meanwhile, the characteristic value of the
elution performance of the mercury species in the solution is screened out, and the elution solution with the optimal elution effect of the mercury species is predicted; and finally, based on a prediction result, synthesizing a high-performance
transition metal oxide suitable for removing mercury in the synthesis gas
flue gas, and realizing efficient
desorption of mercury species and regeneration of an adsorbent through a matched eluent. The research and development efficiency and the wet elution effect of the mercury adsorbent are improved.