(568l) Development of a Sensor Placement Algorithm for Efficiency Maximization of Processes with Estimator-Based Control Systems

Process plants should be operated efficiently without violating the environmental and operational constraints. The measurement technology plays a key role in achieving this goal. Inaccuracies in the measurements of the controlled variables can lead to inefficient operation (or even unsafe operation under extreme conditions) of the plant. Inaccuracies in the measurements of other variables that are used for monitoring purposes can lead to undesirable conditions and can damage equipment and reduce their life. On the other hand, there are a number of variables that cannot be measured or can be measured but the measurements are noisy, inaccurate, or unreliable. In addition, there are variables that can be measured satisfactorily, but the measurement technology is costly or the measurement is available after undesired delay. It may be possible to estimate these variables accurately by measuring appropriate variables even though the measurements are noisy.   For a large-scale process plant, it is difficult to decide which variables should be measured for maximizing the plant efficiency while satisfying desired estimation accuracy in other variables. With this motivation, the focus of the current work is on development of a sensor placement (SP) algorithm to obtain the numbers, locations, and types of sensors for a large-scale process with the estimator-based control system. For a given budget, the sensor network should minimize the loss in efficiency due to the measurement technology while providing satisfactory estimate for the desired variables.

The SP algorithm is developed considering that a Kalman filter is being used as the optimal estimator for the estimator-based control system. This leads to a mixed integer nonlinear programming (MINLP) problem that is solved using a sequential optimization approach using the genetic algorithm. The developed SP algorithm is implemented in an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with pre-combustion CO­₂ capture. The high-fidelity plant model is developed in Aspen Engineering Suite.  The model has approximately 1500 states and more than 160 candidate sensor locations. Due to the transient nature of the underlying problem formulation and the large number of candidate sets that are feasible, the resulting problem becomes computationally challenging to be solved in a reasonable amount of time. A number of novel approaches is developed by modifying the SP algorithm, problem formulations, and computational approaches. Finally by using parallel computing, the SP algorithm is solved within a reasonable amount of time.  The results show interesting impact of the sensors budget on the plant efficiency and estimation accuracy of the key variables.