Abstract: For a determined mathematical model,it is not only necessary to obtain the estimates of parameters,but also their reliabilites.The present work deals with a sequential statistical design strategy for anexperimental program to obtain precise parameter estimetes for a nonlinear kinetic model.It aims at de-creasing the indeterminatness of the parameter estimates,in reducing the correlation between parameterestimates,and in inceasing the adequacy and accuracy of the model.The paper discusses the experimental design criterion for precise parameter estimates-the minimun vol-ume design criterion and the spherical shape design criterion,and the practical value of a sequential statis-tical design strategy.At last,an example was performed to precise estimate the parameters in the kinetic model of ammoniasynthesis reaction which was selected as being adequate in the previous work.Comparison of the results from the industrial unit and the corresponding values calculated with themodel using the estimates of parameters reveals the reasonable reliability of the model.

摘要： For a determined mathematical model,it is not only necessary to obtain the estimates of parameters,but also their reliabilites.The present work deals with a sequential statistical design strategy for anexperimental program to obtain precise parameter estimetes for a nonlinear kinetic model.It aims at de-creasing the indeterminatness of the parameter estimates,in reducing the correlation between parameterestimates,and in inceasing the adequacy and accuracy of the model.The paper discusses the experimental design criterion for precise parameter estimates-the minimun vol-ume design criterion and the spherical shape design criterion,and the practical value of a sequential statis-tical design strategy.At last,an example was performed to precise estimate the parameters in the kinetic model of ammoniasynthesis reaction which was selected as being adequate in the previous work.Comparison of the results from the industrial unit and the corresponding values calculated with themodel using the estimates of parameters reveals the reasonable reliability of the model.