This article presents a new energy model that predicts the energy infrastructure required to maintain oil production in the Canadian Oil Sands operation at minimum cost. Previous studies in this area have focused on the energy infrastructure for fixed energy demands (i.e., the production schemes that produce synthetic crude oil (SCO) and commercial diluted bitumen remained fixed in the calculation of the optimal infrastructure). The key novelty of this work is that the model searches simultaneously for the most suitable set of oil production schemes and the corresponding energy infrastructures that satisfy the total production demands under environmental constraints, namely, CO 2 emissions targets. The proposed modeling tool was validated using historical data and previous simulations of the Canadian Oil Sands operation in 2003. Likewise, the proposed model was used to study the 2020 Canadian Oil Sands operations under three different production scenarios. Also, the 2020 case study was used to show the effect of CO 2 capture constraints on the oil production schemes and the energy producers. The results show that the proposed model is a practical tool for determining the production costs of the Canadian Oil Sands operations, evaluating future production schemes and energy demand scenarios, and identifying the key parameters that affect Canadian Oil Sands operations.

The following work presents a reformulation of the modifier-adaptation methodology for real-time optimization as a nested optimization problem. Using the idea of iteration over the modifiers, this method makes it possible to find a point that satisfies the necessary conditions of optimality (NCO) of the process, despite modeling mismatch, using an outer optimization layer that updates the gradient modifiers with the objective of minimizing the Lagrangian function estimation of the process. Moreover, if a direct search algorithm is implemented in this layer, we can find the optimum without explicitly computing the gradients of the process. The presented scheme was tested in three optimization examples, assuming the absence and presence of process noise, with parametric and structural uncertainty. The results show that in all the cases studied, the method converges to a close neighborhood of a point that satisfies the NCO of the real plant, being robust under noisy scenarios and without the need to estimate process derivatives.

No abstract

No abstract

No abstract