Chemical process optimization problems based on the open-equation modeling approach are frequently characterized by large sparse models. To solve large-scale on-line optimization problems

efficient and reliable optimization algorithms should be developed and considered. In this paper

a full space sparse SQP algorithm is presented. First/second finite derivatives are used to build Jacobian and Hessian matrices

while the inherent sparse structure existing in systems is maintained. A preprocess is employed to exploit the sparse structure. Through this phase

the elements in Jacobian and Hessian matrices are divided into two parts: zero and nonzero ones. Only nonzero elements are computed in the algorithm. Moreover

to reduce the computing time for Jacobian and Hessian matrices

the constants are identified and withdrew from nonzero elements. Those constant elements are stored as global variables so that they can be called at any time without being computed in each iteration. Thus the computational demands and storage requirements for large matrices could be reduced. Compared with traditional SQP algorithm

the performance of this algorithm is enhanced significantly. These enhancements are demonstrated on a number of test problems

including scalable mathematical problems and chemical optimization problems. Computing results indicate the possibility and efficiency of this algorithm for the large-scale on-line optimization of chemical process systems.