Exploring the Impact of Reconciliation in Mining

Reconciliation in mining is a crucial process that aligns estimated values in mine planning with actual operational data. This process is vital for ensuring accuracy in every stage of mining, from extraction to processing. Inaccurate data from tools such as plant auto samplers, weightometers, and truck dispatch systems can negatively impact reconciliation and, consequently, the mine's overall performance. Effective reconciliation requires adherence to best practices across the mining value chain, impacting both efficiency and financial outcomes.

Introduction

Reconciliation has become increasingly important in the mining industry as a crucial practice. It utilizes standardized definitions and methods to improve evaluation and boost both planning and operational performance. The challenge in the mining sector arises from the variety of reporting and calculation methods used, along with the lack of a universal standard. This variety makes it difficult to compare data and to stick to governance standards consistently.

Key Performance Indicators (KPIs), which are derived from reconciliation factors, play a significant role in providing insights into operations. These KPIs are particularly useful in highlighting differences or variances in operations, contributing to the enhancement of accuracy and reliability in mining activities. By focusing on and addressing these variances, the mining industry is able to increase the precision and dependability of its operations.

Understanding Reconciliation

Mining reconciliation is a crucial process that connects the theoretical aspects of mining planning with the actual on-ground operations. It primarily involves comparing the planned estimates, like ore grade, tonnage, and geometry, with the actual data gathered during mining activities. This comparison plays a vital role in several key areas:

1. Validating Geological Models: The process ensures that the theoretical geological models are in line with the actual materials extracted, thus maintaining the models accuracy and reflecting the true conditions of the mine.

2. Improving Operational Efficiency: By identifying where and how actual mining operations deviate from the planned models, reconciliation enables companies to optimize their processes, leading to enhanced efficiency.

3. Financial Decision Making: The process helps in recognizing discrepancies that might impact revenue and investment choices, thereby aiding in more informed financial decisions.

4. Quality Control: Reconciliation ensures that the expected ore grade and quality are achieved, minimizing risks associated with processing lower quality materials and maintaining the mining operation's value and reputation.

5. Resource Management: It assists in estimating the depletion and reserve of the orebody, which is critical for planning the life span of the mine and ensuring sustainable resource management.

6. Regulatory Compliance: It ensures that the production reporting is accurate and meets jurisdictional requirements, crucial for maintaining legal and ethical standards in mining operations.

In practical application, mining reconciliation uses factors like "BMA F and R", Mine Call Factors (MCF), and Mine Value Chain Reconciliation (MVCR) to evaluate performance across different mining models. Utilizing these factors helps in effectively bridging the gap between planning and actual execution. This leads to more precise forecasting, improved efficiency, better financial management, strict quality control, and strong resource management, all while adhering to regulatory standards. This comprehensive approach underlines the importance of mining reconciliation in enhancing the overall effectiveness and compliance of mining operations.

Detailing Calculations for Key Reconciliation Factors

Before exploring calculations, it's essential to understand the key terminology:

• Long Range Model (LRM): Used for strategic, long-term planning, the LRM is based on initial resource estimates from exploration drill holes. Its early-stage nature often leads to inaccuracies.
• Short Range Model (SRM): Focused on production optimization, the SRM is developed from infill drill holes and provides better estimates than LRM but faces challenges like higher dilution due to inefficiency in segregating ore from waste.
• Grade Control Model (GCM): An extension of SRM for daily planning, defining ore and waste boundaries, and estimating internal dilution. The GCM ensures precision in operational guidance, particularly for ore extraction boundaries in surface operations.

This article focuses exclusively on the calculations related to Mine Value Chain Reconciliation (MVCR), a topic that is widely discussed and detailed in existing literature.

The essence of MVCR lies in its approach to reconciliation calculations, which are integral to each step of the mining process. These calculations are methodically aligned with the sequence of the mining value chain. The key aspect of these calculations is to identify and quantify the differences that occur at each step of the process. This is referred to as the reconciliation relationship.

Two formulas are presented for this purpose. The first one, intended for measuring tonnes, is calculated by (Node 2 - Node 1) / Node 1, this formula also has a more straightforward variant: (Node 2 / Node 1) - 1. The second formula is used for determining the grade, which is simply the subtraction, expressed as Node 2 - Node 1.

In figure 1, where each arrow symbolizes a reconciliation node. At these nodes, the calculations mentioned above are applied to compare and analyze the data. This visual representation helps in understanding how these calculations are integrated into the mining process at various stages.

Table 1 displays comparative fictitious data for the Resource Model and Plant Received. Observations reveal that the Resource Model has lesser tonnes and grade compared to Plant Received. This suggests a trend of increment along the value chain, reflected as a "plus" outcome in their relationship. Conversely, a downward trend would have yielded a 'minus' result.

For tonnes calculation:

Calculus for tonnes:

Node 1 (Resource Model) = 782674.1 toneladas
Node 2 (Plant Received) = 864814 toneladas

(Node 2 / Node 1) - 1:
(Plant Received / Resource Model) - 1:

(864814 / 782674.1) - 1 ≈ 10.48%         

For grade difference, simply subtract:

Calculus for grade (%):

Node 1 (Resource Model) = 49
Node 2 (Plant Received) = 51.5

Node 2 - Node 1:
Plant Received - Resource Model
51.5 - 49 = 2.5%         

In the reconciliation process from Resource Model to Plant Received, there is an approximate increase of +10.48%, signifying a rise in tonnes. Additionally, there's a 2.5% difference in grade. This method offers a consistent framework for interpreting results in similar reconciliation scenarios. In calculating dispatch-related data, it's essential to accurately record the ore's journey to the processing plant. This encompasses the ore directly sent to the plant, the ore routed to stockpiles for later use, and any losses, such as ore used in linings. To determine the true delivered quantity, calculations should focus on the ore that goes straight to the plant and the ore that is brought in from the stockpiles.

Effective Reporting of Results

Having completed the calculations for each node to analyze the differences, a straightforward approach to report these findings is through a dashboard. This tool can effectively display the differences between any selected nodes for easy comparison.

Conclusion

The success of reconciliation in mining hinges not only on technical calculations but also on broader factors. High-quality data is essential for accurate decision-making. A clear understanding of the mining value chain allows for identifying and addressing inefficiencies. Cross-functional teamwork ensures a holistic approach to data collection and analysis. Finally, a continuous improvement mindset drives long-term efficiency and sustainability in the industry. These elements together form the cornerstone of operational excellence in mining.

In essence, the ultimate goal of reconciliation in mining goes beyond mere compliance or operational efficiency. It's about building a resilient, adaptable, and forward-looking industry capable of meeting both current needs and future challenges.

References

MACFARLANE, A.S. Reconciliation along the mining value chain. J. S. Afr. Inst. Min. Metall., 2015, vol.115, n.8, pp.679-685.

Morley, Craig. (2003). Beyond reconciliation: a proactive approach to using mining data.

Morley, Craig & Arvidson, Heath. (2017). Mine value chain reconciliation – demonstrating value through best practice.

ROSSI, M. E.; DEUTSCH, C. V. Mineral resource estimation. [S. l.]: Springer, 2014

Parker, H. (2012). Reconciliation principles for the mining industry. The Austrailian institute of mining and metalullurgy. Mining Tech, 160-176