Reducing Hidden Costs and CO₂ Emissions: Development of Practical User Interface for Underground Stope Dilution Analysis

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This study provides a practical interface for evaluating dilution, financial outcomes, and CO₂ emissions in mine planning.

Abstract

Stope dilution is a major hidden cost driver for the underground operation, especially in terms of reducing ore quality, increasing the amount of processing feed, and effects on operational cost. Accurate calculation and consideration of planned and unplanned dilution and mining loss amounts are essential during mine planning. The user interface named D–Loss has been developed with MATLAB R2023b, which provides a multiparadigm numerical computing environment for faster and more practical calculation of these dilution amounts to address these challenges by quantifying dilution and linking them directly to economic and CO₂ emissions indicators. By determination and analysis of the stope overall dilution amounts, it helps us understand greenhouse gas emissions and ensures the efficient use of underground equipment. Calculation of stope dilution in a practical and rapid manner allows for stope design and operational improvements, which can help reduce dilution in underground operations. This progress is tracked through the D–Loss interface within the short- and long-term production planning. Moreover, by quantifying dilution impacts on comminution and haulage costs, D–Loss becomes a critical software for tracking economic losses and optimizing financial outcomes in the mining industry. D–Loss helps users iteratively assess the efficiency of updates and provides support in mine design, scheduling, and environmental impact control by comparing planning and operational improvements before and after.

1. Introduction

The key success for underground mining projects is to increase production rates while lowering operating costs. Stope dilution and recovery are two of the parameters that directly affect financial results and operational efficiency [1,2]. Dilution causes uneconomic material to mix with the run of the mine, which raises processing costs; lowers the quality of the ore; and, due to the increased number of truck cycles, impacts CO₂ emissions underground. Additionally, it causes delays in production schedules, which impacts short- and long-term planning targets. Dilution-related costs can make up 20–30%, and in some cases as much as 40%, of the total metal or concentrate costs, according to studies from different mines [3].

There is still a lack of useful modular tools for precisely monitoring and controlling stope dilution in the regular base at many underground mines. Conventional methods frequently miss out the relation of the results of dilution to their wider financial and environmental results, instead placing a strong emphasis on geotechnical design and stope geometry [4,5]. Also, mine planning usually does not differentiate between planned and unplanned dilution, even though they come from very different reasons: design choices versus unexpected operational issues [6]. This lack of information makes it hard to make accurate reserve estimates and efficient production planning. This lack of clarity makes it harder to plan production and figure out how much reserve you really have.

Mineral resource estimation and creating a block model that includes the grade, block coordinates, density, and volume of each block are usually the first steps in the planning process [7]. After that, stope optimization is carried out using mine planning software, considering modifying factors, including equipment capacity, production targets, and geotechnical parameters [8]. The relationship between stope designs and their actual implementation is so critical, especially if loss and dilution are not dynamically monitored.

Dilution has several consequences for underground production. In addition to this, operational costs increase especially when it comes to increasing CO₂ emissions. Extra cycles are needed to haul extra waste material to the surface, which increases the ventilation requirement and fuel consumption. According to recent studies, optimizing underground operations’ energy efficiency, especially in ventilation and haulage, can decrease CO₂ emissions by as much as 24% [9]. As a result, controlling dilution management both environmentally and economically is the key factor for successful operation [10,11].

In order to track planned and unplanned dilution, as well as mining losses, this study presents D–Loss, a user-friendly interface created using MATLAB’s academic license. D–Loss allows mine planners to evaluate the wider effects of their design and operational results by directly connecting the dilution result to the D–Loss financial and CO₂ emissions module. Using block model and stope design data, it enables quick evaluation of stope tonnage, recovery, and grade, offering both immediate flexibility and long-term planning advantages.

Despite the growing awareness of dilution’s impact on underground mining performance, current tools often fall short in integrating technical, economic, and environmental results into a single decision-making framework. While some software applications address stope reconciliation, they rarely provide analysis of how dilution levels affect both financial returns and CO₂ emissions.

This study addresses that gap by presenting D–Loss, a MATLAB-based software tool designed to quantify planned and unplanned dilution, evaluate stope-level profitability, and estimate haulage-related CO₂ emissions. Unlike traditional tools, D–Loss allows for iterative comparisons and planning support based on user-defined scenarios. The objectives of this study are threefold: (1) to develop a user interface that links dilution to key financial and environmental indicators; (2) to apply the tool to actual industry data from an underground mine; and (3) to demonstrate how dilution scenarios impact stope selection, profitability, and emissions at the production-planning level.

2. Technical Mining Fundamentals

We present the essential technical parameters directly related to the D–Loss user interface, with emphasis on the parameters used in dilution and loss calculation, financial evaluation, and CO₂ emission estimation. Rather than providing a broad review of underground mining methods, we place emphasis on planned and unplanned dilution, mining loss, and the tier approach as environmental assumptions that are essential for understanding D–Loss functionality.

2.1. The Mining Dilution

In mining, dilution is defined as a widespread and destructive “disease” in the industry, resulting from the dilution of ore with waste rock or material below the cut of grade [12]. In the ramp-up of production, many underground operations were suspended due to uncontrolled dilution [13,14]. Dilution negatively affects the feasibility of mining activities and, consequently, profitability margins [15].

Dilution is categorized into categories of planned and unplanned [16]. A schematic drawing for dilution is provided in Figure 1.

Unplanned underground mining dilution occurs when adjacent rock (or backfill) outside the designed production panel boundaries enters the production panel and mixes with the blasted ore during mining. It is assumed that it would not be practical to separate waste material from waste-mixed material. For this reason, the diluted ore is planned to be processed in the mill. Several factors contribute to planned dilution, including mining methods, production, and ore continuity [17]. In the sublevel mining method, dilution can be more than in other commonly used methods, such as cut and fill. Based on this, orebody continuity with practical stope design results in larger production stopes, which are mainly a result of planned dilution. Using the sublevel stoping method, especially for vein-type ore production, increases dilution levels [18]. In the production outcome, another point, it has been observed that unplanned dilution increases as stope sizes decrease due to production difficulties in ore extraction at narrow stopes. Mining dilution in stopes with a width of 10 m is around between 22 and 25%, while in stopes with a width of 5 m, dilution was recorded at 32% [19].

2.2. Mining (Ore) Loss

Mining loss refers to inefficient extraction and loss of material or ore of economic value in the production process [20]. It is influenced by many factors, such as improper drilling and blasting operations, inadequate mine planning and design, the nature and type of ore, and the selection of equipment. In addition to planning and design issues that cause mining loss, the other requirements, such as operational or technical deficiencies and the inexperience of the operator, have an impact on mining loss. The mining loss rate and dilution are not standalone production success metrics for the mine. They are also key indicators for the level of technical knowledge and quality of the planning team [21].

Mining loss refers to the fact that the economically extractible material cannot be produced or remains in the stope, which poses a risk to the profitability and efficiency of the project. To ensure the continuity and correct execution of mining activities, it is necessary to identify the factors that lead to losses in operations and to use practical software to minimize mining losses [22]. In addition to operational factors, the estimation of mineral resources and the estimation of reserves can also be counted among the factors that lead to ore losses. Errors in geological modeling or resource classification, as well as inaccurate data in ore reserve estimation, can lead to a loss of ore. Causes of ore loss include incorrect calculation of the cut-off grade or changes in the cost and selling price of the ore [23].

2.3. CO₂ Emissions in Mining

CO₂ emissions have become a more critical issue around the world. Different industries have different negative effects on greenhouse gas emissions. Considering only the mining industry, it accounts for approximately 3% of CO₂ emissions in the overall picture [24]. Determining and understanding CO₂ emissions from operating underground or open-pit mines is becoming more critical year by year. Among greenhouse gases, CO₂ has the highest concentration [25].

Diesel underground trucks are utilized for ore or waste haulage from underground to the surface. To reduce underground haulage CO₂ emissions, electric trucks and more efficient haulage technologies are considered by mining companies. In addition, the total amount of hauled material from underground increases as a result of uncontrolled dilution in mining operations. Consequently, diesel consumption increases by underground trucks and scoops, leading to higher CO₂ emissions.

2.4. Effect of Financial Analysis on Stope Production

Detailed diluted stope financial analysis is essential for understanding the economic feasibility of underground production. The life of the mine, production unit cost, and net present value of the project are impacted by dilution. Financial analysis and the profitability of a stope are determined by several parameters, including the stope tonnage, diluted grade, recovery, commodity price, and related operational costs. The net profit per stope is defined using the formula below [26]:

Net Profit = (Ore Tonnage × Diluted Grade × Recovery × Metal Price × Payable Factor) − Total Operating Cost

(1)

The parameters of this equation have a specific role:

Ore Tonnage: Total tonnage of material mined after applied for mining dilution and loss.

Diluted Grade: Average diluted grade of the extracted material.

Recovery: Metallurgical recovery rate.

Metal Price: Market price of the metal (e.g., USD/tonne).

Payable Factor: Fraction of recovered metal that is payable by the buyer, after refining charges.

Total Operating Cost: Sum of mining, processing, and administration costs.

It is also known that dilution has a negative impact on operating costs. To achieve the planned metal content, more feed must be processed at the processing plant, which will increase total operating costs. With dilution, the decrease in grade, along with the additional material produced, is expected to raise transportation and ore processing costs [27]. Dilution impacts revenue, expenses, and profit margins. In a study, a 10% reduction in dilution resulted in a 6% increase in the internal rate of return [28]. In another study, where the per-ton cost of dilution was reported as USD 30, a 1% decrease in dilution saved USD 150,000 over a total of 500,000 t [29].

Assessing financial metrics in stope design and production planning provides more robust decisions on the schedule, minimizes the economic risks, and contributes to the sustainability of the mining operation.

3. Materials and Methods

The mining industry is demanding software that improves production efficiency; reduces ore loss and dilution; provides environmental compliance; and offers quick, data-driven financial insights more and more. These criteria result from the requirement to maximize production use while increasing profitability and sustainability. D–Loss is developed for use in common underground mining methods such as sublevel longhole stoping and cut&fill. Designed as a direct result of these difficulties, D–Loss presents an all-inclusive software supporting;

• Short-term planning,
• Dilution analysis and control,
• CO₂ emission assessment,
• Level-based financial evaluation.

The input datasets used for the case study were derived from an operational underground mine including stope solids (.obj files) and the block model (.csv). While specific mine names are not disclosed, the dataset reflects representative values observed in similar underground hard-rock mining operations. Cost figures used in financial analysis (e.g., mining cost of USD 38.70/t) were based on internal feasibility estimates typical for operations and are meant to illustrate D–Loss functionality.

The values used for recovery, payability, and the selling cost were selected to reflect conservative and commonly observed parameters in early-stage economic analysis. To increase methodological transparency, we acknowledge that sensitivity analysis on these inputs would strengthen the result robustness. While not included in this study, such analysis is planned for future D–Loss releases.

Through matching with present industry needs, D–Loss seeks to close the operational planning and strategic decision-making gap. This section explains the materials, datasets, development, and implementation of the D–Loss software that was designed for underground mine planning, particularly, the sublevel longhole and C&F production method. The methodology integrates geometric stope design principles, field data, and MATLAB-based algorithms to evaluate stope performance with different parameters. All calculations and results are performed with the focus of the improvement of the short- to long-term decision-making mine planning process.

3.1. D–Loss Software

3.1.1. General

The D–Loss interface is developed to support mine planning by enabling the monitoring of mining performance and production stope improvements. It is designed to incorporate underground production methods, particularly, sublevel stoping and cut&fill. Optimization results from these methods generate stope or development solids, which can be easily analyzed within its platform. In addition, it is a multi-functional modular interface created to help users in an efficient and practical way. A general overview is provided for the D–Loss interface in Figure 2.

It supports various stages of planning, from the short-term to long-term planning process. Ore reserve estimation is a complex and multistage process that starts with mineral resource estimation and follows through a different number of related steps: process followed by mine design and production scheduling. Once scheduling is completed, financial modeling is carried out to understand the economic viability under different scenarios. Using an environmentally friendly technique, CO₂ emissions-linked different production phases are also computed. Level−by−level analysis is conducted to assess each level’s financial results once all the steps have been finished. D–Loss is particularly useful for financial analysis, short-term planning, loss and dilution estimation, and CO₂ emissions. Incorporating stope settings and environmental parameters, D–Loss helps to evaluate underground mining performance holistically and with a data-driven approach. This improves operating efficiency, as well as sustainable mine planning, and helps to guide wise decisions. Figure 3 demonstrates the overall mine planning process and highlights its integration with D–Loss points to be used effectively.

3.1.2. Overview of MATLAB

MATLAB is a synonym for “Matrix Laboratory” since all the variables are defined as matrices. Compared to others, MATLAB is a high-level programming language software introduced by MathWorks. It not only provides coding opportunities but also visualization. The user can write functions, as well, but their power depends on code called “function”, written and provided by the software.

3.1.3. Interface and D–Loss Validation

The D–Loss software, written in MATLAB and executable through the compiler, enables it to function as a standalone application without the need for users to install the program. It is a user-friendly interface designed to quickly generate reports based on the selected stopes, with an emphasis on ease of use. The work flowchart of D–Loss is provided in Figure 4, while a user-friendly overview of the interface is provided in Figure 5. To ensure the calculation reliability of D–Loss, validation tests were conducted using Excel. The same formulas used in the software for dilution calculation, grade estimation, and CO₂ emissions were independently implemented in Excel using identical input data. The results were found to be identical across all tested scenarios, deviation observed with less than 0.06%, which is considered negligible. This confirms that D–Loss accurately reproduces the expected outputs, and its internal logic aligns with established fundamental calculations.

3.1.4. Data Upload

The first step for the D–Loss interface is to select the corresponding files of interest. The required information about the stopes is obtained through the selected stope solid (obj) and block model (csv) files. Whenever the “Data Upload” button is hit, a pop-up menu allows the user to navigate through the computer to select the corresponding files.

The pop-up interface about attributes for the block model is provided in Figure 6. The name of the selected .csv file, along with the coordinate components, block dimensions, ore type, density, and resource classification, are listed on the screen. Each column name is assigned to the standard name, but the user can change it by using the drop-down menu.

The information about the stopes, as well as the vertices and faces of each, is obtained from the .obj file. The .csv file is used to obtain information about homogeneous partitions. Their locations (X, Y, Z) within the stope and distances in each direction (dx, dy, dz), as well as the information about grades and density, are obtained from it. Once all the information is controlled and necessary changes are performed, the “Data Load” button is hit to load the data.

3.1.5. Stope Selection

The user can select any stope listed in the “Stope Number” panel. The selected stopes will be visualized in 3-D on the right-hand side in red color, while all of the stopes are displayed in gray (Figure 7). In such a way, the user can see the extension and location of the stope to analyze. If the user wants to keep the stope, then the “Select Stope” button can be hit, where it will list the selected stopes in the “Selected Stope” panel. They will also be visualized in 3-D on the right-hand side in green color. In the meantime, the user can continue examining the other stopes. The user can also select all stopes by hitting the “Select All Stopes” button, where all the stopes will be listed in the “Selected Stope” panel. A detailed cross section of Stope 4 is provided in Figure 8 as an example, with 5% mining loss and 10% dilution applied.

If a stope is selected from the “Selected Stope” panel, then the corresponding information about this stope, including its name, planned dilution, in situ grade, in situ tonnage, diluted grade, diluted tonnage, and CO₂ emission, are calculated and displayed in the interface. The only editable parameters on the interface are the “Mining Loss” and “Dilution” percentages. By default, 5 and 10 percent are used for these parameters, respectively, but the user can change them as desired. The output of the other parameters will be provided in the corresponding box as the stope selection is performed in the Selected Stope panel. The user can also press the “Remove Selections” button to clear the selected stope list and restart examining the desired stopes.

3.1.6. CO₂ Emissions

D–Loss makes it easier to calculate and track CO₂ emissions due to ore haulage to the surface. It allows for the estimation of the short- or long-term carbon footprint of stope production. Depending on various rates of dilution, it can easily estimate overall CO₂ emission changes. Since dilution can be controlled by operation and analyzed by software, it will help to understand and reduce overall CO₂ emissions during mining operations.

In the development stages of this software, the Tier 1 and Tier 2 approaches, developed by the Intergovernmental Panel on Climate Change (IPCC), were considered. Tier 1 considers fuel consumption from national energy statistics and default emission factors, while Tier 2 considers fuel combustion from national energy statistics, together with country-specific emission factors, where possible, derived from national fuel characteristics [30].

Tier 1: CO₂ Emissions = Fuel Consumption × Default Emission Factor

(2)

Tier 2: CO₂ Emissions = Fuel Consumption × Country − Specific Emission Factor

(3)

The Tier 1 approach was used in the development of D–Loss using the following equations.

Energy Consumption [TJ] = Fuel Consumption [l] × Conversion Factor [TJ/kt] × 10⁻³

(4)

Carbon Content [Gg C] = Carbon emission factor [t C/TJ] × Energy Consumption [TJ] × 10⁻³

(5)

Carbon Emission [Gg C] = Carbon content [Gg C] × Carbon Oxidation Ratio [%]

(6)

CO₂ Emission [Gg CO₂] = Carbon Emission [Gg C] × 44/12

(7)

The user can see the corresponding CO₂ emission amount by changing the dilution and loss percentages on the main page of the interface. Alternatively, the calculation details for the stope selected are available by selecting the “CO₂ Analysis” button. The editable parameters are changeable to observe the final CO₂ emission (Figure 9). The diluted tonnage result for the stope is carried here for the selected loss and the dilution percentages for the stope. The estimated CO₂ emission result is also updated on the original page of the interface.

3.1.7. Financial Analysis

D–Loss helps to calculate the “Stope Profitability” by considering the income and cost as a result of the production of the diluted stopes. These financial inputs allow level-by-level stope analyses to be performed to postpone the production of stopes falling below the breakeven point and to re-determine the production sequence based on their profitability. Parameters provided by the user are process recovery (%), concentrate sale price (t), payability (%), mining processing, general administration costs (USD/t), and the selling cost (USD/t.con).

The user can also choose the “Stope Financial Analysis” button in the main interface (Figure 7) for the selected stope to see and change the detailed calculation for the financial analysis. The sample output of the pop-up interface is provided in Figure 10 for Stope 4. The pop-up menu allows users to input the parameters in white color to see how parameters like stope profit, expenses, and net profit vary, while the gray parameters are not editable. The estimated stope profit provides direct support for financial analysis and production planning. If the user wants to choose the “Add Stope” button on the bottom right, then the required financial information about the stope is saved to be used in the visualization of the financial parameters by the “Graph Dashboard” button.

3.1.8. Financial Analysis Visualization of the Selected Stopes

The “Graph Dashboard” button in Figure 7 is used to visualize the financial analysis of the selected stopes. Figure 11 provides a sample output where tonnage and the net profit for the selected stopes are provided for all stopes.

3.1.9. Report Generation

The user can navigate through the different stopes and hit the “Report” button to generate a report of the corresponding stope in Excel format, where the maximum and minimum of the grade values are provided. The pop-up interface is provided in Figure 12. The default intervals between the minimum and maximum grades are provided in the interface, but the user can also define the desired interval amounts between the two.

When the user hits the “Report” button, the code generates an Excel output file including the density, tonnage, volume, and grade for the selected interval amounts, as well as the total amount (

Table 1. The output report for Stope 4.

Grade Interval (%)	Density	Tonnage (t)	Volume	Grade
[0–8]	2.33	3864	1658	0.00
[>8–16]	0.00	0.00	0.00	0.00
[>16–24]	0.00	0.00	0.00	0.00
[>24–32]	2.92	224	76	31.48
[>32–40]	3.06	739	241	37.95
[>40–48]	3.18	35,459	11,148	43.97
Grand Total	3.10	40,287	13,126	39.57

).

3.1.10. Key Differences Between D–Loss and Other Software

The integration of advanced technology and the use of different brands of software are increasing day by day in the mining industry. Controlling and improving loss and dilution are key technical concerns affecting ore extraction, operational efficiency, cost, and CO₂ emissions in the mining sector.

Mining software usually offers features to calculate dilution and perform reconciliation of stope production using block models and stope solids. Once survey data from underground surveys or stope designs are obtained, they can be evaluated, as well as loss and dilution can be reported. D–Loss software sets itself apart by including economic analysis and CO₂ emission calculations in addition to calculating loss and dilution, making it a more comprehensive software for mining operations.

Table 2. Comparative summary of D–Loss and other industry standard software.

Feature/Capability	D–Loss	Industry Standard Software
Dilution Calculation	Yes—Quantifies both planned and unplanned dilution in detail	Yes—Based on stope solids and block models, but unplanned dilution is fixed
Loss Assessment	Yes—Integrated with financial and CO2 impact	Yes—Via reconciliation tools
CO2 Emission Calculation	Yes—Included as core feature	Requires external calculations or custom integration
Economic Analysis	Yes—Directly evaluates financial impacts of dilution/loss	Requires export to external tools
Production Planning Support	Yes—Integrates technical and economic parameters	Yes—Primarily focused on geometric design and reconciliation features
Level-by-Level Performance Analysis	Yes—Evaluates each level’s grade, tonnage, and financial outcome	Typically requires manual configuration or external data processing
User Interface	MATLAB-based GUI, tailored for ease of use	Advanced, but often complex and license-heavy
License Access	Available under Hacettepe University license agreement	Commercial licenses required
Customizability/Academic Research-Oriented	High—Adaptable for research and project-specific needs	Vendor-controlled configurations

shows a comparative overview of D–Loss and other often-used mining software. This table is intended for academic benchmarking only and does not imply functional inadequacy for other software solutions. All commercial software tools mentioned are highly capable and widely adopted in the mining industry. D–Loss provides a complete framework by combining not only dilution and loss estimates but also economic assessment and CO₂ emission analysis, thereby addressing important industry needs successfully.

By calculating the amount of waste and ore within these solid boundaries, the total planned and unplanned dilution can be determined. At this stage, D–Loss integrates economic and carbon emissions calculations, providing a strategic approach for short- and medium-term stope production planning. The amount of unplanned dilution in the current software remains constant. Nonetheless, D–Loss enables the identification of unplanned dilution levels and presents the analysis of their impacts on grade and tonnage. Through the analysis of these economic aspects, the financial repercussions of dilution and losses can be quickly and precisely assessed.

4. Case Study

The case study was conducted using D–Loss software based on stope designs optimized for an underground chromite mine. The production planning was based on the transverse longhole stoping method. Within this framework, profitability analyses were carried out for stopes 4, 8, and 10 under different dilution and mining loss scenarios. In addition, the tonnage and grade of each stope were calculated using D–Loss. The impact of varying dilution levels on CO₂ emissions was also assessed within the platform, and the results are presented in the graphs and table below.

4.1. Data Preparation

Block model (.csv) and stope solids (.obj) files are used to assign the coordinates of each block, block size, grade values, and density, as well as the face and vertices information of each stope. Additional attributes, such as resource classification, can also be incorporated if needed. In this study, a block model containing chrome grade values and the corresponding optimized stope solids for the relevant mine were used as input data for the software.

User-defined inputs are required for accurate calculations for the stope margin due to every mine having a different processing recovery, portability, and mining operational cost. The editable parameter modular design of D–Loss allows delivering flexible and adaptable results. User-defined inputs for the financial analysis are provided in

Table 3. Input parameters for financial analysis for each stope.

Items	Unit	Cost
Processing Recovery	%	60.00
Concentrate Price	USD/t	400.00
Payability	%	95.00
Tax	%	10.00
Mining Cost	USD/t	38.70
Processing Cost	USD/t	16.80
General Administrative Cost	USD/t	2.40
Selling Cost	USD/t.con	4.00
Total Cost	t/USD	59.77

for each individual stope.

A total 20 number of stopes were uploaded using the files, but Stopes 4, 8, and 10 were selected for the analysis in the case study. Three different unplanned dilution scenarios were applied as 8%, 10%, and 15%, where impacts on the stope tonnage and grades were analyzed to understand how unplanned dilution affects the overall stope grade, financial performance, and associated CO₂ emissions.

4.2. Output Evaluation

The results for the tonnage and grade amounts, alongside the in situ and planned dilution parameters, as well as the CO₂ emissions, are obtained from the D–Loss interface for Stopes 4, 8, and 10 (

Table 4. Output from D–Loss for the selected parameters.

	In Situ		Diluted		Profit	Unplanned Dilution (%)	Mining Loss (%)	CO2 Emission (t)
	Tonnage	Cr2O3 (%)	Tonnage	Cr2O3 (%)	(USD)
Stope 4	40,287	39.57	41,335	36.64	4,267,959	8	5	96
	40,287	39.57	42,100	35.97	4,223,638	10	5	98
	40,287	39.57	44,014	34.41	4,112,836	15	5	103
Stope 8	25,468	13.01	26,130	12.05	−128,322	8	5	61
	25,468	13.01	26,614	11.83	−156,339	10	5	62
	25,468	13.01	27,823	11.31	−226,383	15	5	65
Stope 10	23,282	41.86	23,887	38.76	2,689,530	8	5	55
	23,282	41.86	24,329	38.06	2,663,918	10	5	57
	23,282	41.86	25,435	36.4	2,599,886	15	5	59

).

4.3. Results

In mining operations, short-term planning for weekly plans is an iterative and fast process that can be easily handled by proceeding with financial analysis. D–Loss evaluates all stope data and provides results for the diluted overall stope tonnage and grade based on various dilution amounts. In Table 4, the overall tonnage is stated as 89,037 t for the selected three stopes after dilution, which increases to 90,689 t. It is also observed that the overall grade dropped from 32.57% to 30.29% dilution at 8% for Stopes 4, 8, and 10. The same iterative process was applied for 10% and 15% unplanned dilution and reported results, while the loss remained constant.

When analyzing sublevel stoping, a planned dilution of 8% was applied to stopes 4, 8, and 10. As shown in Figure 13 and Figure 14, the results are presented as a total tonnage and grade comparison.

The stope tonnage and profit comparison in Table 1 showed that Stope 8 indicated a negative profit for different dilution percentages. In such cases, production from this stope may be postponed, or a stockpile blending strategy could be considered in the short- to mid-term. Additionally, it is critical to note that increasing unplanned dilution from 8% to 15% across these three stopes resulted in an overall stope profit reduction of approximately 5%, which could have a significant impact on the life of the mine.

The visualization, loss, and dilution, as well as the financial analysis performed versus tonnage and profit analysis, support decision-making in short-term production scheduling. The next step involves evaluating CO₂ emissions for total or individual stopes production, which not only contributes to monitoring carbon emissions underground but also helps to improve ventilation efficiency.

Evaluation of the parameters in Table 1 indicates that unplanned dilution has a critical effect on CO₂ emissions. In three stopes, a total production of 214 t of carbon is emitted if production is performed at 8% dilution. When unplanned dilution increases from 8 to 15%, it is recorded that carbon emission increases from 214 t to 228 t in total, which corresponds to a 6.1% increase (Figure 15). As a result of the extra broken waste material being extracted, it increases the number of truck cycles and diesel consumption.

5. Conclusions

The findings show that even a moderate increase in unplanned dilution, rising from 8% to 15%, can cause a noteworthy decrease in the average stope grade from 32.57% to 30.29%. It directly affects higher CO₂ emissions and increases the operational cost. To be able to understand such results before the implementation of drilling and blasting is the key to successful operations.

CO₂ emissions rising from 214 t to 228 t also leads to a potential increase in underground ventilation requirements and operational costs. The monitoring and control of dilution are not only critical for optimizing ore recovery and operational performance but also for mitigating environmental impacts and maintaining a sustainable underground mining operation.

An overall profit reduction of around 5% is one of the key indicators that dilution and loss impact profitability. From this point, mine planners and operations need to take a series of actions that will reduce dilution and loss by investigating drilling, blasting, stope ground support control, bogging practices, and stope designs. D–Loss closes this gap by not only pointing out the physical causes of dilution but also tying them to cost-based indicators, which can help to prioritize stope redesign or operational control and operational strategies.

D–Loss provides flexibility for working with individual stopes while also allowing for multi-stope analysis. In addition, the impact of unplanned dilution on stope revenues and CO₂ emissions can be analyzed; accordingly, the number of haul trips expected to increase due to dilution and the amount of carbon dioxide emissions generated as a result can be easily calculated on a per stope basis or for all stopes. In addition, the calculation of stope revenues and costs enables economic analysis and graphs, allowing for quick comparisons of stope profitability. In this context, it offers the user the ability to perform practical calculations for daily, weekly, and monthly planning. The software is fully compatible with all block model types and provides detailed analysis of selected stopes with an average processing time of 40 s. The other key is its significantly lower capital cost compared to other alternatives, making it more accessible to a broader range of users. All outputs of D–Loss were cross-checked using manual Excel calculations and yielded identical results, confirming the accuracy and consistency of the software.

D–Loss V1.1 version is still under development, and upcoming updates aim to create stope production sequencing algorithms and scheduling features. While designed as a useful tool for both industry and academic applications, D–Loss currently relies on user-provided parameters. A key limitation of the current study is that the work is based on one site case study. Future development will include integration with automated cost databases and mine sequencing algorithms to enhance decision-making automation. In addition, incorporating data inputs, as well as geotechnical and optimization capabilities, could further expand its practical relevance in both industrial and research settings.