Improving state-of-the-art power plant availability using Bayesian Networks

Abstract

The Integrated Gasification Combined Cycle technology in combination with coal as a natural resource is claimed to be one of the leading power generation alternatives for the near future. One of the main challenges facing this technology today is plant availability. Due to the specific characteristics of this technology, current practices cannot sufficiently address plant availability. A different approach is needed. This paper proposes the use of Bayesian Networks as an extension of existing methods. Ultimately, the approach described in this paper is directed towards scenario-informed decision making to improve plant availability. This includes the prioritization of potential scenarios of unavailability and allocation of resources to prevent or mitigate the most serious scenarios, as well as operator training and identification of indicators of potential unavailability. A case study that demonstrates the application of the initial steps of the proposed approach has been conducted within a Dutch IGCC plant. A Bayesian Network was constructed for the syngas treatment unit of the plant, in which identified unavailability scenarios were modeled. Results from the case study indicate that our approach can help a company to identify, quantify, and prioritize scenarios, which can act as an input to improve availability management.