Updated Mine Water Balance Model

 Posted by Jason Lillywhite

Our conceptual Mine Water Balance model, built in GoldSim, has been updated to take advantage of the latest software features and align it with to our own Style Recommendations. This model, designed as an educational template rather than a site-specific solution, demonstrates the interconnectedness of key facilities such as a Process Water Dam (PWD), a secondary buffer dam (Dam 2), a Tailings Storage Facility (TSF), and the Process Plant.

A glimpse into the simulated mine water system.

This updated version includes a re-organization of model elements to provide a clearer and more intuitive visualization of the water balance pathways throughout the simulated mine site. To build confidence in simulation outputs, a mass balance validation test has been integrated, allowing users to verify the conservation of water across the system. The accompanying model documentation has been updated, offering more detailed explanations of the model's logic, components, and operational assumptions. Finally, the dashboards have been refreshed to demonstrate how to create a "navigation tabs" feel.

Modeling Spatially Correlated Rainfall in GoldSim

 Posted by Jason Lillywhite

Effective water resource management hinges on accurately modeling precipitation. But what happens when rainfall patterns differ significantly between 2 locations within your study area. For example, precipitation on a valley floor compared to the mountainous watershed nearby? This post explores a practical method using GoldSim to simulate precipitation that is linked, or spatially correlated, across different locations. 

We'll use real-world daily rainfall data from two distinct sites in Utah to demonstrate how to set up and parameterize such a model. Read on to see how rainfall correlation between these valley and mountain locations led to more realistic hydrological simulations in GoldSim.

An evaluation of scenarios for a radioactive repository using GoldSim

  Posted by        Jason Lillywhite

Safely isolating high-level radioactive waste for millennia requires more than just deep geological disposal; it relies on a "multi-barrier" approach. This involves several layers of protection designed to contain the hazardous waste. Key components are the "engineered barriers," which are man-made structures placed within the repository. These can include the waste's immediate disposal container, the surrounding structure often called a silo, and robust materials like concrete used extensively within the repository environment. These work together with the natural geological barrier (the surrounding rock) to prevent or slow the release of radioactive materials.

Today, I want to showcase a study evaluating scenarios for a radioactive waste repository using GoldSim. This study was presented at the recent GoldSim 2024 User Conference. It looks at the long-term durability of concrete and how its deterioration might affect the overall safety of these facilities over vast timescales.

This study, by researchers at Chosun University, explores how dynamic, probabilistic simulation can help predict the performance of these engineered barriers. Ensuring the long-term safety of high-level radioactive waste disposal requires understanding complex phenomena, and GoldSim provides a powerful way to evaluate potential scenarios.

Here's a visual representation of the kind of post-closure scenario they're modeling, showing how groundwater might interact with the repository barriers:

Figure 1: Disposal facility post-closure performance modeling evaluation scenario

Read on to explore how they combined experimental data with GoldSim modeling to assess the impact of concrete degradation.

Using Conditional Containers to Simulate Crop Growing Seasons

 Posted by Jason Lillywhite

Modeling sequential, time-dependent processes lies at the heart of many dynamic simulations. But what happens when the duration of each step is uncertain or changing during the simulation? Consider simulating crop growth stages based on the FAO Irrigation and Drainage paper 56 for modeling crop water demand. Accurately capturing the variability in crop stage durations is important, and ensuring stages trigger correctly using traditional conditional logic can become complex and error-prone under uncertain conditions.  A previous version of the model described here that I built years ago relied heavily on nested IF statements to manage stage transitions. I was always bothered by this implementation, knowing that if the precise duration of each stage wasn't fixed at the simulation start, dynamically ensuring the correct sequence could become extremely difficult to implement reliably.

Screen Capture of the Crop Growing Season Scheduler using Conditional Containers

This post explores how GoldSim's Conditional Containers provide an improved solution. I'll walk through the new version where each growth stage resides in its own Container, dynamically triggered by the completion of the previous one (as shown in the model structure pictured). Discover how this approach not only simplifies the representation of sequential logic but also seamlessly integrates stochastic durations, leading to a more robust, understandable, and maintainable model for Monte Carlo analysis. Read on to see this powerful technique in action! 

GoldSim as a Predictive Tool for Oil Sands Mining Operations

Posted by  Jason Lillywhite

We are pleased to share insights from a recent presentation by Candace Whitten, GIT and Matthew Ryans, P.Eng from WSP.  This work was presented at the 2024 GoldSim User Conference. 

Oil sands mining operations generate multiple tailings types requiring various treatment methods, storage components, and time for tailings maturation. GoldSim was employed to develop a dynamic material mass balance model simulating future tailings production and treatment alternatives.

Figure 1 - Schematic diagram of bitumen extraction and tailings storage and treatment

The model provided insights into the production of coarse and fine solids, informed by ore grade and production schedules. It identified high-sensitivity parameters and ensured compliance with site-specific thresholds, as established by Directive 085. This compliance guarantees that the modeled treatment technologies are sufficient for managing fluid tailings and that there is no net growth of fluid tailings beyond the life of mine (LOM) production. Additionally, the model offered insights into optimizing tailings management to minimize environmental impact and support sustainable mining practices. 

Physically Based vs. Feedback Control Models: Choosing the Right Approach for Water System Simulation

 Posted by  Jason Lillywhite

We’ve often been asked about the best way to simulate flow between two tanks: should it be modeled physically, based on fluid mechanics, or actively controlled using a feedback mechanism? With the release of GoldSim 15 and its new Controller element, now is a good time to compare these approaches and perhaps even combine them. In this post, we’ll examine the performance of our existing physically based model (Flow Between Two Tanks) alongside a feedback-controlled version using the Controller element. By comparing these methods, we’ll explore their strengths, limitations, and when one approach might be preferable over the other.

Building Better Water Balance Models for Tailings and Mine Rock Stockpiles

Posted by  Jason Lillywhite

We are pleased to share insights from a recent presentation by Tony Zheng (University of Alberta Geotechnical Centre / OKane Consultants), Rebecca Hurtubise (AECOM), and Nicholas Beier (University of Alberta Geotechnical Centre).  This work was presented at the 2024 GoldSim User Conference. Their work focuses on building better water balance models for tailings and mine rock stockpiles, addressing critical challenges in mining operations.

In this blog post, you'll discover how physics-based models developed in GoldSim can provide insights into water balance, acid rock drainage (ARD) risks, and effective closure strategies. These advanced modeling techniques offer actionable knowledge to improve mine closure practices and ensure long-term environmental sustainability.

Read on to explore their innovative approaches and practical applications in the field of mining hydrology and geotechnical engineering.

Testing PrecipGen Across Four Sites

Posted by  Jason Lillywhite

This blog post presents the results of testing PrecipGen across four diverse locations to evaluate its performance:

• Australia Plains, AU
• Grand Junction Walker Field, CO, USA
• Dublin, EI
• Orlando, FL, USA

The findings demonstrate that PrecipGen successfully represents both short- and long-term daily precipitation totals at individual locations, validating its ability to replicate observed climatic behavior across varied environments.

To read more about the PrecipGen model and how to use it, please refer to the GoldSim Library where you can read about it and download PrecipGen PAR and PrecipGen for your own use: Precipitation Simulator (PrecipGen) – GoldSim Help Center

Estimating Exchange Fluxes Between Surface Water and Groundwater Using Stable Isotope and Radon Models

Posted by  Jason Lillywhite

I'm excited to share a recent paper by Lloyd Townley and Jeffrey Turner, which showcases the use of GoldSim software in modeling surface and groundwater interactions. The research introduces the Lake Water Balance Calculator (LWBC), a tool developed using GoldSim, to estimate groundwater-surface water flow rates. This tool leverages transient balance solutions for radon, coupled with volumetric flow equations, and presents results from multiple case studies.

LWBC dashboard in GoldSim

PrecipGen: Long-Term Precipitation Forecasting

Posted by Jason Lillywhite

A GoldSim model that simulates long-term daily precipitation (PrecipGen) has been added to the GoldSim Model Library. We applied the model to locations in Logan, Utah and Dublin Ireland to help evaluate the effectiveness of this model's ability to forecast daily precipitation rates and capture long term droughts. This application is useful for GoldSim modelers looking to incorporate randomly generated rainfall forecasts in their water balance models. The model shows promise in simulating precipitation for a site that is influenced by multi-year wet and dry cycles. Informal testing has been showing signs of robustness and some refinements to the testing are currently underway.

Figure 1 - Screen Capture of PrecipGen with Results

Simulating Daily Precipitation Using a Time Series Element

Posted by Jason Lillywhite

In GoldSim, the Time Series element serves as an effective tool to represent precipitation data. Given the versatility of this element in portraying such data, I felt compelled to share some best practices and tips for those keen on utilizing this kind of data. 

In this article, I will illustrate how to view and interact with measured daily precipitation using a Time Series element. Additionally, I will delve into some data definition properties and demonstrate the use of a Time History Result element to visualize historical trends and compute period statistics. 

This blog post aims to provide insights into modeling with time series data more efficiently, regardless of whether you are a GoldSim novice or an experienced user.

2024 GoldSim User Conference

Posted by Jason Lillywhite

We are excited to announce the return of the GoldSim User Conference, which will be held from September 9th to 11th, 2024 in the beautiful city of Seattle. September is one of the best months to visit, with an average temperature of 71F (22C). 

GoldSim user conferences provide great learning and networking opportunities for both expert and novice users. The conference will be held at the beautiful Seattle Marriott Waterfront and the adjacent Bell Harbor International Conference Center, both with stunning views of the downtown Seattle waterfront. In addition to oral presentations, poster sessions, panel discussions, and training sessions, registration also includes several fun networking events (a dinner cruise on Elliot Bay and a Seattle Mariners game in luxury suites). We look forward to seeing you in Seattle! Register now!

Simulating Controlled and Uncontrolled Reservoir Discharges

Posted by Jason Lillywhite

When simulating ponds and reservoirs in GoldSim, one of the most common types of systems we see are the combination of controlled and uncontrolled discharges. Examples of uncontrolled discharges include evaporation losses and flood flows over a spillway. Examples of controlled discharges include water that is pumped out or flows through an opening in the outlet works. Controlled outflows deliver water to service necessary uses downstream such as irrigation or industrial water demands.

In this blog post, I will walk through the process of setting up this type of model in GoldSim. Hopefully this will provide a good resource for those of you working on conceptual water balance models for your projects.

Best Practices for Modeling Interacting Ponds

Posted by Jason Lillywhite

A new demonstration video was posted that can help you improve complex water balance models. In this video, I demonstrate how we can more clearly show the flows within a complex network of ponds.

I do this by comparing the difference between directly defining flows (see image on the left) and keeping actual flows separate from requests that drive the flows (see image on the right). Following these ideas can significantly improve the visualization of the system you are trying to model, especially when you have many flows being passed between components of your system.

In-Person GoldSim Training - September 13, 2023

Posted by Jason Lillywhite

I will be teaching a 1-day hands-on GoldSim training course at the upcoming Arizona Hydrological Society's Annual Symposium on September 13 in Flagstaff Arizona. Visit their conference website for more information: GoldSim Workshop – 2023 AHS Symposium. We hope to see you there!

Improve Workflow with Shortcut Keys in GoldSim

Posted by Jason Lillywhite

The graphical features of the GoldSim user interface are highly intuitive and easy to use, making it one of the most popular simulation software tools available today. The user-friendly interface allows even novice users to quickly and easily create models, organize their elements into containers, and navigate through them using the built-in browser and navigation tools. With its intuitive interface, GoldSim allows users to focus on the task at hand rather than on the software, which ultimately leads to better results and improved productivity.

But did you know that there are many shortcut keys available that can make your modeling experience even smoother and more efficient? In this blog article, we'll explore some useful shortcut keys in GoldSim. Whether you're a seasoned GoldSim user or just starting out, these shortcuts are sure to save you time and make your modeling experience more enjoyable.

GoldSim 14.0 Revision 2 Has Been Released

Posted by Jason Lillywhite

This update includes a number of corrections, reliability improvements and general user-interface enhancements. All users are strongly advised to always use the latest Revision available. Anyone currently with access to GoldSim 14 has access to this Revision.

Linking GoldSim to Python-Enabled Excel

Posted by Jason Lillywhite

I recently presented a webinar called "Effectively Working with Excel", demonstrating some of GoldSim's Excel interface features. In addition to demonstrating how GoldSim can import/export data, I also talked about how GoldSim can dynamically interact with Excel during a simulation. One of the attendees asked me if GoldSim will wait for user-defined functions to be called by external programs, DLLs, or scripts used by Excel before reading in values from Excel. At the time, I didn't have a ready answer because I couldn't think of a time that I've actually seen this done.  

To answer his question, I built a simple GoldSim model that interfaces with an Excel file and this Excel file relies on user-defined functions written in a Python script. I thought this example might also be useful for a wider audience, so I decided to blog about it.

Simulating Precipitation with Long-Term Droughts

Posted by Jason Lillywhite

One of GoldSim's most popular models downloaded from our Model Library is the GoldSim implementation of a Stochastic Weather Generator (WGEN). This model simulates probabilistic climate that drives hydrological processes in environmental systems models, such as precipitation, temperature and solar radiation.

One of the limitations of this model is that the generated precipitation time series generated by the model doesn't account for long-term drought cycles. Using the National Integrated Drought Information System (by NOAA), we can apply the Standardized Precipitation Index (SPI) to precipitation generated by WGEN to account for droughts. This blog post summarizes an approach used to incorporate droughts for a site during the years 1910 - 2022 and found to produce results that compare well to the historic precipitation measured at a site in Manti, Utah.

The GoldSim Contaminant Transport Module Online Course is Now Available!

Posted by Rick Kossik

Over the last two years I posted six excerpts from the Contaminant Transport Module Online Course. I had promised that it was under development and would eventually be posted in full. I posted the last of these excerpts a year ago and then went silent, so you may have wondered "what happened?". With the exception of the last excerpt, these were from the (early) conceptual parts of the Course. The remainder of the Course is "hands-on" and procedural and would not have necessarily worked well in a blog format. Hence, the silence. Well, FINALLY (after 2 years of effort) the full Course is now available. In this post, I briefly provide an overview of what you can expect.