Dynamic model-based sensor network design algorithm for system efficiency maximization

• Developed a dynamic model-based sensor network design algorithm for efficiency maximization.
• Reduced the computational expense of the combinatorial optimization problem significantly.
• Algorithm applied to an acid gas removal unit.
• Optimal sensor network leads to minimum deviation from optimal transient profile of efficiency.
• Negligible variation in efficiency beyond a threshold budget.

A dynamic model-based sensor network design (DMSND) algorithm has been developed for maximizing system efficiency for an estimator-based control system. The algorithm synthesizes the optimal sensor network in the face of disturbances or set point changes. Computational expense of the large-scale combinatorial optimization problem is significantly reduced by parallel computing and by using combination of three novel strategies: multi-rate sampling frequency, model order reduction, and use of an incumbent solution that enables early termination of evaluation of infeasible sensor sets. The developed algorithm is applied to an acid gas removal unit as part of an integrated gasification combined cycle power plant with carbon capture. Even though there are more than thousand process states and more than hundred candidate sensor locations, the optimal sensor network design problem for maximizing process efficiency could be solved within couple of hours for a given budget.