Tuesday, June 15, 6:30-9:00 PM

Wednesday, June 16, 6:30 PM - 9:00 PM


A Workshop on Applying Optimization Algorithms and Contaminant Transport Models to Reduce Costs and Speed Remediation at Pump and Treat Sites Top

Tuesday, June 15, 6:30-9:00 PM

This workshop provides an overview of the process of using optimization algorithms in conjunction with ground water flow and transport models (“simulation optimization”) to help identify the optimal combinations of well locations and extraction/injection rates to accomplish ground water remediation with the least cost or in the least time subject to site-specific constraints. The two-hour workshop is intended for site managers, regulators, and technical staff who desire an understanding of the benefits of this type of optimization, the general process, and what tools are publicly available to perform such simulation optimization. The workshop attendees will learn what simulation optimization is, what the potential benefits of applying the approach at their sites are, what the general process entails, and what the typical costs and expertise requirements are for application of the technology.

The workshop:

  1. defines “transport optimization” as the use of optimization algorithms with ground water contaminant-transport models to find the optimal combination of well locations and flow rates (subject to certain constraints) that minimizes some measure of performance such as cost or time to cleanup,
  2. identifies the potential benefits relative to more traditional “trial and error” approaches for groundwater extraction design,
  3. describes the optimization process as one that includes the development of an objective function to be minimized or maximized (e.g., total cost, total pumping rate, contaminant mass remaining, etc.) and identification of constraints that must be satisfied (e.g., maximum flow from each well), followed by the application of software that uses optimization algorithms. This software repeatedly runs the flow and transport model (e.g., MODFLOW/MT3DMS) with different well locations and flow rates until a “better answer,” as measured by the objective function, can not be found by the optimization algorithm.
  4. briefly presents the general capabilities of two publicly available optimization algorithm packages, MGO (University of Alabama) and SOMOS (Utah State University).
  5. presents the results of a multi-site demonstration project where the optimal solution determined with MGO and SOMOS are compared to the results of the best efforts of experienced modelers using a traditional “trial and error” approach to solve the same optimization problem.

The development and presentation of the workshop and demonstration project were funded through the Department of Defense’s Environmental Security Technology Certification Program (ESTCP) and the EPA Office of Superfund Remediation and Technology Innovation.

ITRC Presents: Essentials of Remediation Process Optimization by the ITRC RPO Team Top

Tuesday, June 15, 6:30-9:00 PM

The Interstate Technology Regulatory Council is a State lead member organization that includes members from the DoD, USEPA, DOE and many different corporate and private interests. ITRC formed an RPO team who’s goal was to develop RPO technical regulatory guidance and related training that both State regulators, who will receive and develop RPO proposals, and organizations performing RPO could use as a resource. State regulators, so they could understand the key elements of RPO; RPO practitioners, so they could understand what level of knowledge a State regulator should have about RPO. The presentation will discuss: key findings of the team in their investigation of RPO; lessons learned during the document – training development process; and allow time for questions from the audience.

The document and training development process is important to understand in order to appreciate the value of the guidance. Air Force, Army, and Navy representatives made major contributions. The State regulators represented a broad range of disciplines and regional interests: ME, NJ, SC, GA, FL, CA, and OR worked on the team. The USEPA and DOE provided valuable input. Private interests: facility owners, consultants, and engineers were well represented. A discussion of how each of these varied interests came together as a team, an RPO microcosm in itself, will be presented. The State concurrence process and lessons learned by the State regulators will be highlighted.

The ITRC RPO Team identified several key RPO areas of the process that needed to be understood. Key areas include: site selection criteria, building the team, exit strategy assessment, evaluating remedial action objectives, conceptual site model, evaluating remedy performance, evaluating monitoring programs, cost-efficiency assessment, schedules and other RPO considerations, optimizing the exit strategy, optimizing the remedial system, optimizing the monitoring program, and implementation of the optimization strategy. A discussion of these key areas will be presented. Case studies will be presented to illustrate the RPO process.

The Triad Approach to Managing Decision Uncertainty for Better Cleanup Projects Top

Wednesday, June 16, 6:30 PM - 9:00 PM

“The Triad Approach to Managing Decision Uncertainty for Better Cleanup Projects” will be presented as a 2-hour seminar that will cover the basics of the Triad approach to site cleanup and will briefly use data from actual projects to illustrate key concepts. The Triad approach combines systematic planning and decision uncertainty management with dynamic work strategies and state of the art real time measurement technologies. This approach lowers project costs while increasing confidence that data are correctly interpreted so that exposure and remedy decisions are correct. The session will be relevant to regulators, environmental program managers, risk assessors, and data quality control staff. By taking the course, participants will achieve the following objectives:

The instructional methodology for this course includes lecture with handouts.

Long-Term Monitoring Optimization Methods and Software Top

Wednesday, June 16, 6:30 PM - 9:00 PM

This workshop provides an overview of the following field-tested methods/software for optimizing existing site-specific long-term groundwater monitoring programs that are tracking contaminant migration.

Monitoring and Remediation Optimization System (MAROS 2.0) software is a decision support tool that accounts for relevant current and historical site data as well as hydrogeologic factors and the location of potential receptors. Based on this site-specific information, the software uses both temporal methods (Mann-Kendall Analysis, Linear Regression Analysis, or Cost Effective Sampling) and spatial methods (Delaunay Triangulation or Moment Analysis) to determine the minimum number of wells and the minimum sampling frequency required for future compliance monitoring at the site. Graphical and spatial visualization tools within the software assist the user in assessing the trend results at each monitoring point. The MAROS software is available for free download at

Parsons’ three-tiered method for long term monitoring optimization consists of a qualitative evaluation, an evaluation of temporal trends in contaminant concentrations, and a geostatistical spatial analysis. The results of the three evaluations are combined to assess the degree to which the monitoring network addresses the primary objectives of monitoring. A decision algorithm is applied to assess the optimal frequency of monitoring and the spatial distribution of the components of the monitoring network, and to develop recommendations for monitoring program optimization.

The Geostatistical Temporal/Spatial (GTS) algorithm is a decision-logic-based strategy for optimizing long-term ground-water monitoring networks developed for the Air Force Center for Environmental Excellence (AFCEE). GTS allows one to optimize both sampling frequencies and the number of wells used in a particular network. GTS has been employed at a number of Air Force sites with cost savings typically on the order of 30% or more of total LTM budget. In this workshop, the geostatistical methodology undergirding GTS will be explained in the context of a recent case study application. We will also discuss the current effort to convert GTS into free-standing software.

Multi-objective LTM Optimizer (M-LTMO) was developed at the University of Illinois and Moire, Inc., for identifying spatial and temporal redundancies in monitoring networks. The software combines a suite of interpolation modeling approaches (in both space and time) with user-friendly automated optimization approaches. It employs state-of-the-art multi-objective genetic algorithms that allow users to identify tradeoffs among multiple monitoring objectives and to explicitly consider data uncertainty in developing optimal monitoring designs. M-LTMO accesses a library of analytical tools for visualizing and analyzing data, developing interpolation models, entering any type of monitoring objectives and constraints in a user-friendly interface, automatically setting appropriate optimization parameters, and visualizing multi-objective optimization results. The software is being tested at two BP field sites.