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.