A new approach for hierarchical system optimization and parameter estimation of a large-scale industrial process is described. This approach can be viewed as a hierarchical implementation of the approximate linear model approach with a three iterative loop structure. The internal double loop iteration where only model-based information is involved is a two-model approach. Augmentation is introduced to enforce and accelerate convergence in the internal double loop. The third iterative loop arises from the hierarchical implementation of the technique and involves coordination of price vectors to ensure balance between separable sub-optimization problems. The major advantages of this triple iterative loop configuration are that it significantly reduces the required on-line iterations in order to reach the optimum, especially when the process is linear or nearly linear and it has a balanced distribution of model–based computations in the internal double loop. Optimality and convergence conditions are examined. Computer simulations are provided to demonstrate the behaviour of the algorithm.