We examine the performance of a new optimization algorithm on a real industrial case concerned with data validation. In the proposed Successive Quadratic Programming called SQPIP, the QP subproblem is based on an interior point method. The SQP method involves three major tasks : the QP subproblem, a line search procedure and an approximation method of the Hessian of the Lagrangian. The QP is resolved according to the predictor-corrector primal-dual path-following interior point algorithm proposed originally for linear programming by Mizuno, Todd and Ye. To determine a suitable steplength, we use the Powell exact penalty function and a basic watchdog technique. Finally, the Hessian is approximated by the BFGS formula. SQPIP has been implemented so as to be able to determine a search direction when infeasible QP subproblems are encountered by relaxing the equality constraints without violating the simple bounds on the variables. Exploiting the sparsity of process optimization, the algorithm is used to resolve the problem of data validation in plants. The performance of the algorithm is illustrated with the help of the steam production network of the Schering (D) power plant.