Stochastic Programming Approach versus Estimator-Based Approach for Sensor Network Design for Maximizing Efficiency

The measurement technology with sensors plays a key role in achieving efficient operation of the process plants, and optimal sensor placement is very important in this endeavor. The focus of the current work is on the development of sensor placement algorithms to obtain the numbers, locations, and types of sensors for a large-scale process with the estimator-based control system. Two sensor placement algorithms are developed and investigated. In one algorithm, dynamics in the process efficiency loss that are due to the estimator-based control system that receives measurements from a candidate sensor network are explicitly accounted for. For a large-scale process with a large number of candidate sensor locations, this approach leads to a computationally expensive mixed integer nonlinear programming problem. In another algorithm, the estimation error is accounted for in terms of probability distributions, and therefore, a stochastic programming approach is used to solve the sensor placement problem. A novel algorithm called BONUS is used to solve the problem. The developed sensor placement algorithms are implemented in an acid gas removal unit as part of an integrated gasification combined cycle power plant with precombustion carbon dioxide capture. In this article, we compare and contrast these two sensor placement algorithms by evaluating the efficiency loss of the optimal sensor network synthesized by each of these algorithms along with their computational performance.