458489 A Multi-Objective Optimization Approach to Optimal Sensor   Location Problem in IGCC Power Plant in the Face of Uncertainties

Thursday, November 17, 2016: 8:30 AM
Monterey II (Hotel Nikko San Francisco)
Pallabi Sen, Industrial Engineering, University of Illinois at Chicago, Chicago, IL, Kinnar Sen, CUSTOM, Vishwamitra Research Institute, Crystal Lake, IL and Urmila M. Diwekar, Vishwamitra Research Institute, Center for Uncertain Systems: Tools for Optimization and Management, Clarendon Hills, IL

Integrated Gasification Combined Cycle (IGCC) power plants provide a cleaner and more efficient way to obtain energy from coal. In order to operate an IGCC power plant in a safe and stable manner, many input and output process parameters need to be monitored. However, due to economic and operational constraints it is infeasible to place sensors at locations pertaining to all of these parameters. Hence, it becomes important to select the most effective sensor locations which can lead us to gain maximum information about the plant conditions. Drawing on the work done by Lee & Diwekar, 2012, this work attempts to broaden the realm of the optimal sensor location problem to address simultaneous optimization of multiple objective functions. In addition to “Fisher Information”, two other objective functions, viz., “thermal efficiency of the power plant” and “cost”, will be considered. Practical issues present in an IGCC power plant such as harsh physical conditions and variability in process parameters make the optimal sensor location problem an especially complicated one. Advanced simulation software and data from a set of virtual sensors are used to collect information about the output parameters which do not have any physical sensors to gauge them yet. In order to solve this real world large scale problem, we use a novel algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). BONUS works in probability distribution space and avoids sampling for each optimization and derivative calculations iterations. In order to avoid infeasibilities, we derived a new 2-tier constraint method specific to this multi-objective optimization problem. The results of this nonlinear stochastic multi-objective problem is the non-dominated or Pareto set which provides trade-offs between various objectives like observability, cost and thermal efficiency.

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See more of this Session: Design, Operations, and Analysis of Power Systems
See more of this Group/Topical: Computing and Systems Technology Division