(637a) A Multi-Objective Optimization Approach to Optimal Sensor   Location Problem in IGCC Power Plant in the Face of Uncertainties

Authors: 
Diwekar, U., Vishwamitra Research Institute
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 â??Fisher Informationâ?, two other objective functions, viz., â??thermal efficiency of the power plantâ? and â??costâ?, 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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