With the improvement of systematicness and integrality of the object model description

the complexity of process optimization problem is increased and then higher requirements are put forward for the performance of optimization algorithm. The existing nonlinear programming algorithms have both merits and demerits in their solving performance. A convergence depth control based polynary hybrid nonlinear programming algorithm was proposed in this paper. Each nonlinear programming algorithm was regarded as a meta-algorithm. In order to take full advantage of each meta-algorithm

convergence depth was used to control interactive cooperation among them and then the ability for solving large-scale complex optimization problem could be enhanced. Data reconciliation problem of air separation system and optimization problem of depropanizer and debutanizer distillation column systems were taken to test the proposed algorithm. The numerical results showed that the solving ability of the polynary hybrid nonlinear programming algorithm was better than the single nonlinear programming algorithm.