Customers & Industries: Integral Consulting

Integral Consulting Uses @RISK to Help Protect the Environment

  • Industry: Environment, Mining
  • Product(s): @RISK
  • Application: Mine Remediation

Summary

Integral Consulting was hired by a Fortune 100 mining company to assist with evaluating remediation (aka cleanup) strategies for a 100-year-old mine. The Integral team used @RISK to evaluate multiple strategies and select a solution that minimized environmental impact and achieved full regulatory approval, while enabling the client to save more than USD $100M.

It's hard to imagine a decision problem that you can't model with @RISK. The software is incredibly powerful in that it doesn't limit the user. In fact, the only restriction is the creativity of the modeler.
Tim Havranek, Principal Consultant, Integral Consulting

Integral Consulting, Inc. is a science and engineering firm that provides multidisciplinary services in the fields of health, environment, technology and sustainability. The company’s mission is to help clients across a wide range of public and private enterprises to identify technically sound, cost-effective and environmentally friendly solutions to complex problems. Integral staff were hired by a Fortune 100 mining company to assist with evaluating remediation (aka cleanup) strategies for a 100-year-old mine. The Integral team used Palisade’s @RISK to evaluate multiple strategies and select a solution that minimized environmental impact and achieved full regulatory approval, while enabling the client to save more than USD $100M.

Interest in sustainability is steadily increasing as individuals, corporations, and governments consider issues such as global warming, highly volatile financial markets, and urban sprawl. Sustainability concepts have become a key part of managing cleanups at hazardous waste sites, with a growing focus on the integration of sustainable ‘green’ practices to increase the environmental, social, and economic benefits of cleanups.

Integral staff assisted a Fortune 100 mining company with evaluating cleanup strategies for a large, hard rock mine. The company needed to address acid mining drainage from large rock piles as well as the potential for rock slide risks at the site. The cleanup solution originally supported by the Environmental Protection Agency (EPA) and State was complete removal of the rock piles and transport to an offsite disposal location. However, this could take more than 20 years to complete, at a cost of approximately USD $180M. The solution would also increase hazards to the public through exposure to years of heavy truck traffic through small towns, and produce millions of tons of greenhouse gases. “This was an incredibly complex situation,” explained Timothy Havranek, Principal Consultant for Integral Consulting. “The mine had been in operation for nearly 100 years, and our client needed to understand the full environmental impact of all possible cleanup solutions. There were numerous uncertainties including potential risks to human health, safety, and the environment, as well as significant project costs, not to mention multiple stakeholders with different definitions of success.” The Integral team proposed an analysis process that would evaluate the multiple costs, risks, and benefits of each cleanup strategy to clearly identify the trade-offs among alternative strategies.

The Integral team developed a multi-criteria decision analysis (MCDA) model, fitting the analysis criteria into the context of a standard CERCLA (Comprehensive Environmental Response Compensation and Liability Act) Feasibility Study 9-criteria evaluation. Two of these nine criteria, overall protection of human health and the environment and compliance with applicable, relevant and appropriate requirements (ARARs, i.e. laws and regulations) are known as the threshold criteria. Therefore, only those remedial alternatives that met the threshold criteria were retained for detailed evaluation. The remaining seven criteria include: short- and long-term effectiveness; implementability; long-term effectiveness; reduction in mobility, toxicity and volume; cost, State acceptance; and community acceptance. A potential drawback to the CERCLA framework is the subjectivity of the analysis. "It doesn't really quantify or address uncertainty regarding how well something works," said Havranek. "Our goal was to stay within the CERCLA study guidelines but introduce a more quantitative method that considered all factors, including the uncertainties, in a more measurable way." This was where Palisade’s @RISK software played a major role.

Building the Model

The Integral team started by breaking down the non-threshold seven CERCLA criteria into more detailed, quantifiable criteria, such as tons of greenhouse gas generated and increased risk of worker injury. A total of 18 criteria were eventually identified for evaluation, which were applied to four possible cleanup scenarios:

  • Complete removal and off-site disposal (the alternative originally preferred by EPA / State)
  • Complete removal and on-site disposal
  • Partial removal and capping
  • Re-grading and capping

The Integral team used Palisade’s @RISK to analyze the considerable number of uncertainties presented by each scenario, primarily using pert distributions. Integral prefers pert distribution for two reasons. The first is that estimating how well each alternative will score (i.e. perform) with regard to a particular criterion often requires interviewing subject matter experts. These experts are typically able to provide estimates in the form minimum, most likely and maximum. The second reason is that the pert distribution will take on a form that is similar to a lognormal or normal distribution, which unlike the triangular distribution, is commonly associated with many environmental and financial parameters.

In addition to modeling financial variables such as the duration and cost of completing each alternative, the Integral team considered the impact of several community variables, including aesthetics (time required to blend into the surrounding environment), employment (quantity and duration of new jobs) and reputational impacts (negative feedback at Town Hall meetings).

The process of finding the optimum alternative involved the use of a normalization function (also known as a value function) which transformed each sampled criterion score for each alternative into relative value based on the potential range of scores for all alternatives (i.e. lowest low to highest high). The normalized function returned a score ranging from 0 to 100 for each criterion for each alternative for each iteration of the model. The total MCDA score for any alternative was the weighted sum of the sampled criteria values. Determining how much weight to place on the individual criteria involved the use objective survey techniques that enabled statistical determination of criteria weights based on user responses.

Rapid Results, Ample Applications

The resulting graphs of the @RISK model yielded a power curve that approximates the expected number of cars at each speed, thus giving researchers a fast, convenient tool for better understanding and estimating vehicle numbers in traffic:

Results of Weighting Process

The model produced output probability and cumulative distribution functions for the total MCDA score for each alternative. The alternative having the highest score with the least amount risk was the one that best meets stakeholder preferences while minimizing uncertainty.

"@RISK enabled us to do sensitivity analysis on each criteria, which helped us determine how specific uncertainties could drive different decisions," said Havranek. "It let us easily identify which choice was optimum for our client, for the community and for the environment."

The final results of the analysis indicated that regrading and capping was the cleanup strategy that best fit the overall objectives for all stakeholders. These results were incorporated into the CERCLA feasibility study and provided a sound basis for the selected alternative. The solution was approved by the EPA and State, and resulted in savings of USD $136M over the cleanup strategy originally supported by the regulatory agencies.

"It's hard to imagine a decision problem that you can't model with @RISK," added Havranek. "The software is incredibly powerful in that it doesn't limit the user. In fact, the only restriction is the creativity of the modeler."

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