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Research and development funding: • • • National Science Foundation South Texas Project Nuclear Operating Company Electric Power Research Institute The University of Texas at Austin Operations Research and Industrial Engineering People and organizations involved in RIAM From the University of Texas at Austin: Risk Informed Asset Management (RIAM) A joint research and development program between • • The University of Texas at Austin Operations Research and Industrial Engineering, ETC 5.120 1 University Station, C2200 Austin, TX 78712, USA Phone: 512-471-3078 Fax: 512-232-1489 [email protected] www.me.utexas.edu/~popova/riam.html • • ORIE at the University of Texas at Austin McCombs School of Business at the University of Texas at Austin Risk Management at STPNOC Nuclear Asset Management Program at EPRI Elmira Popova, Ph.D., Associate Professor, Department of Mechanical Engineering, [email protected] This research program is a joint effort between the Operations Research and Industrial Engineering Graduate Program and the McCombs School of Business at the University of Texas at Austin, David Morton, Ph.D., Associate Professor, Department of Mechanical Engineering, [email protected] Paul Damien, Ph.D., Professor, Department of Information, Risk, and Operations Management, McCombs School of Business, [email protected] Alexander Galenko, Ph.D. candidate, Department of Mechanical Engineering, [email protected] From the Risk Management group at South Texas Project Nuclear Operating Company: Ernie Kee, [email protected] Alice Sun, [email protected] Rick Grantom, [email protected] Drew Richards, [email protected] http://www.me.utexas.edu/~orie http://www.mccombs.utexas.edu the Risk Management Group at South Texas Project Nuclear Operating Company (STPNOC), http://www.stpnoc.com and the Nuclear Asset Management Program at the Electric Power Research Institute (EPRI). From EPRI: Stephen Hess, Program Manager, [email protected] http://www.epri.com Risk Informed Asset Management (RIAM) The main objective of the RIAM program is: to make optimal risk-informed decisions at both operational and executive management levels by taking into account budget, internal project dependencies, outage duration, and regulatory safety constraints; to appropriately model and include the uncertainty related to rates of return on investments, energy prices, failure mechanisms, and costs for replacement and spare parts; and to provide decision-makers quantified feedback on decision-making performance. We strive to advance the state of knowledge, through basic research, on decision making, uncertainty, optimization, simulation, and statistics. We apply these novel methodologies to optimal decision-making at operational and executive level in electric power generation. The main areas of research are: Operational (decision-making) level: • • • Single item maintenance policies Portfolio of maintenance strategies Proper modeling of failure distributions, maintenance costs, and life-cycle management Executive (decision-making) level • • Project valuation • Risk metrics for project valuation and analysis Optimal allocation of funds among projects Major Accomplishments Basic Factors to Forecast Maintenance Cost and Failure Processes for Nuclear Power Plants: In this project we found the most important factors to predict the cost for planned and unplanned maintenance. The cost incurred includes labor cost, cost for new parts, and emergency order of expensive items. At the plant management level there is a budgeted amount of money to be spent every year for such operations. It is important to have a good forecast for this cost since unexpected events can trigger it to a very high level. In this research we present a statistical factor model to forecast the maintenance cost for the coming month. One of the factors is the expected number of unplanned (due to failure) maintenance interventions. We introduce a Bayesian model for the failure rate of the equipment, which is input to the cost forecasting model. The importance of equipment reliability and prediction in the commercial nuclear power plant is presented along with applicable governmental and industry organization requirements. A detailed statistical analysis is performed on a set of maintenance cost and failure data gathered at STPNOC. The paper will appear in the Nuclear Engineering and Design journal in 2006. Operational Level Models and Methods for Risk Informed Nuclear Asset Management: In this research, we model and solve the problem of choosing a maintenance policy for a single item, from a particular class of policies, that minimizes total expected cost over a finite horizon. We optimize the replacement time for a system that fails according to an increasing failure-rate distribution. Our work is applied to a system at STPNOC. We developed a computational algorithm that solves the problem of finding the optimal preventive maintenance time and implemented it at STPNOC by creating an Excel-based add-in. The paper appeared in the ANS, PSA 2005 Proceedings. Optimal Preventive Maintenance Under Decision Dependent Uncertainty: We analyze a system of N components with dependent failure times. The goal is to obtain the optimal block replacement interval (different for each component) over a finite horizon that minimizes the expected total maintenance cost. Our model captures the change in the future joint failure time distribution due to selecting a particular preventative maintenance action. The paper appeared in ICONE14 2006 Proceedings. Ongoing and Future Projects Bayesian Nonparametric Modeling of Failure Times and Cost Parameters: Typically, the mathematical modeling of failure times are based on parametric models. These models fail to capture the true underlying relationships in the data; indeed, under a parametric assumption, the hazard rates are treated as unimodal and skewed, which, we show, is incorrect. Importantly, assuming an increasing failure rate, is way off the mark in the present context. Since hazard and cost estimates are important from a risk management perspective, potentially gross errors, resulting from purely parametric models, can be obviated. Project Prioritization via Optimization with Applications to Electric Power Generation: Managers of electric power generation plants are routinely faced with the problem of allocating limited resources over a collection of potential plant projects. One can consider each project as an “asset” and the plant manager as a “portfolio manager.” When selecting a portfolio of projects, a fundamental question is whether one should optimize or prioritize? In this project, research will be conducted to develop an optimal priority list. The approach requires specifying budget scenarios with weights estimating the likelihood of the respective scenarios. The research will develop an optimal prioritization approach and apply it to a collection of candidate projects at STPNOC, as defined in the STPNOC plant long-range plan. We will demonstrate the benefit the prioritization approach provided by accounting for multiple budget scenarios. Failure Probability From Fault Tree Cut Sets: To properly capture the effect of an equipment failure, we not only have to understand the maintenance cost but also we have to understand the effect of the failure on production revenue. To do this requires the probability that the equipment failure will result in production loss as well as the magnitude of the loss. STPNOC uses fault trees to represent failure logic leading to production loss at the plant level on an average basis. We are now developing the required technology to reduce the fault tree solutions to get the probability (and magnitude) of production loss for each failure represented in the STPNOC fault tree model.