Title: A multi-objective optimization approach to optimal sensor location problem in $$\mathrm{IGCC}$$ power plants

Abstract

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, several input and output process parameters need to be monitored. However, due to economic and operational constraints, it is infeasible to place sensors at every input and output process parameter location. Hence, it becomes important to select the most effective sensor locations which lead to maximum information gain about the plant conditions. 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. Further, sensors can have multiple objectives and they can produce uncertainties due to measurement errors. Here this work considers hybrid hardware and virtual sensing for advanced power systems with multiple objectives. 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. A new algorithm for multi-objective optimization is also developed specifically for problem. The result of this nonlinearmore » stochastic multi-objective problem is the non-dominated, or Pareto set, which provides trade-offs between various objectives like observability, cost, and thermal efficiency. This is the first attempt at solving the problem of optimal sensor deployment in advanced power plants, with consideration of hybrid hardware and virtual sensing and incorporation of uncertainty with multiple objectives.« less

Authors:

Sen, Pallabi ^[1]; Sen, Kinnar ^[1]; Diwekar, Urmila M. ^[1]

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+ Show Author Affiliations

1. Vishwamitra Research Institute, Clarendon Hills, IL (United States)

Publication Date:

Wed Aug 24 00:00:00 EDT 2016

Research Org.:

Univ. of Illinois at Urbana-Champaign, IL (United States); Vishwamitra Research Institute, Clarendon Hills, IL (United States)

Sponsoring Org.:

USDOE Office of Fossil Energy (FE)

OSTI Identifier:

1533534

Alternate Identifier(s):

OSTI ID: 1396986

Grant/Contract Number:  

FE0011227

Resource Type:

Accepted Manuscript

Journal Name:

Applied Energy

Additional Journal Information:

Journal Volume: 181; Journal Issue: C; Journal ID: ISSN 0306-2619

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

01 COAL, LIGNITE, AND PEAT; sensor placement; BONUS algorithm; multi-objective optimization; stochastic optimization