Hanford waste blending and the value of research: stochastic optimization as a policy tool

Abstract

A new optimization framework is applied to management of radioactive wastes stored in belowground tanks at the US Government's Hanford, WA, nuclear fuels facility. Current remediation plans call for vitrification of the tank contents. Blending of the wastes prior to glass formation reduces the amount of material required for processing, therefore decreasing disposal costs. Uncertainty in the tank contents, the error inherent in the glass property models governing vitrification, and computational difficulties, however, render determination of an optimal tankblend assignment a challenge to existing optimization techniques. Previous studies have focused exclusively on minimization of processing and disposal costs, ignoring such management-related dimensions as the value of reducing select sources of uncertainty. Moreover, the stochastic framework employed by these studies could not guarantee that the glass property requirements were met on more than a probabilistic basis. This paper presents a more flexible, efficient, and robust optimization framework that facilitates analysis of the trade-off in reducing select sources of uncertainty. Specifically, the prediction error of the glass property models is found to be more significant than variation in tank component mass fraction estimates, and constraint violations are traced to the need to meet a limited set of glass property characteristics.

The “Value of Research” Methodology and Hybrid Power Plant Design

“…However, in the problem at hand, we have to consider several objectives, such as efficiency, emissions, current density (proxy for reliability and operability), other than just the cost or the profit. Taking all the above factors into consideration, we decided to utilize the concept of “value of research”, , which uses qualitative information to provide knowledge about tradeoffs inherent in allocating scarce resources to alleviate uncertainty sources, not all of which are significant. It is also suitable for multiple objective problems.…”

“…A new paradigm called “value of research” is used , to provide a policy dimension to the traditional optimization problem. The new paradigm is based on a key assumption: that time spent on research increases understanding and, therefore, decreases variation in quantitative estimates derived from this knowledge.…”

Multi-objective convex programming problem arising in multivariate sampling

“…And, in the design of an aircraft, we may actually hope to optimize each and every one of these parameters. The importance of multi-objective optimization can also be seen by the large number of applications presented in the literature as Agrell et al (1998), Armann (1989), Eschenauer (1988), Ferreira and Machado (1996), Fu et al (2000), Fu and Diwekar (2004), Johnson and Diwekar (2001), Kumar and Tewari (1991), Miettinen (1999), Ohkubo, Dissanayake and Taniwaki (1998). Most of these applications are multi-objective problems of nonlinear nature, which is why we need tools for nonlinear programming capable of handling multiple conflicting or incommensurable objectives.…”

Greener by Design

“…Thus, multi-objective optimization methods are necessary to handle the conflicting and different objectives involved in the problem of greener by design. Multi-objective optimization approach is particularly valuable in the context of pollution prevention ( , ), waste management ( , ), life cycle analysis (LCA; 8 ), and sustainability as there are a large number of desirable and important objectives that are not easily translated into dollars. Extending the envelope from simulation to chemical synthesis on one end and manage ment and planning on the other end and broadening the scope to include multiple objectives other than profitability increase uncertainties.…”