Technical guidance, assumptions, and limitations for every tool

• Capital cost by year in currency units.
• Operating cost per tonne or per year.
• Production schedule in tonnes per year or similar.
• Commodity price assumptions in currency per tonne.
• Discount rate in percent.

Inputs should be consistent in units and reflect realistic ranges based on site or market data. Purely speculative numbers reduce the value of the result more than any algorithmic improvement can recover.

The module uses standard discounted cash flow principles. It calculates annual net cash flow from revenues and costs and then computes Net Present Value, Internal Rate of Return, and related indicators. No exotic financial instruments are modelled. Taxes, royalties, and escalation can be handled only to the extent that they are explicitly represented in the input structure.

Main assumptions include deterministic cash flows for each year, constant discount rate within a scenario, and no explicit optimisation of schedule beyond what the user enters.

• NPV indicates value created at the chosen discount rate. A positive value suggests economic viability under the stated assumptions.
• IRR is the discount rate for which NPV would be zero. It is useful for comparing alternatives with similar risk profiles.
• Cumulative cash flow charts help identify payback period and exposure periods.

Outputs should not be interpreted as guarantees. They are conditional on the specific input scenario and do not capture political, social, or permitting risk.

• Results are sensitive to price and grade assumptions.
• Overly smoothed schedules can hide ramp up issues and capacity constraints.
• The tool does not model detailed tax codes, inflation, or financing structures unless represented in the input cash flows.

When citing results, always pair the NPV or IRR figure with a clear statement of the discount rate, price deck, and schedule basis. For example:

"Using MiningToolkit economics module with a constant real discount rate of 10 percent and the price and production schedule described in Section X, the project shows an NPV of Y and an IRR of Z. These results are indicative and do not replace detailed financial modelling."

• Uniaxial compressive strength and, where available, tensile strength.
• Rock Quality Designation, joint spacing, condition, and orientation.
• Groundwater conditions in the relevant horizon.
• In situ stress estimates if available.

The module follows widely used empirical systems such as RMR and Q. It converts field and lab observations into numeric classes that can then be mapped to expected behavior and empirical design charts. It does not perform finite element or boundary element analysis.

Outputs include classification scores and qualitative risk pointers for rockburst, squeezing, and other behaviors. These are best viewed as structured summaries of input data rather than as fully predictive models.

• Out of range input values reduce the validity of empirical ratings.
• Highly anisotropic rock masses or structurally complex settings are not fully represented by single index values.
• Poor field logging quality directly degrades output quality. The tool cannot compensate for incorrect or inconsistent logging.

When reporting, pair any classification with a short description of the data basis and logging procedure. Avoid phrasing that implies certainty. A suitable phrasing is: "Rock mass classifications were derived using MiningToolkit based on RMR and Q inputs from logged core and face mapping. Results should be interpreted as screening level indicators and cross checked with detailed mapping and numerical analysis where appropriate."

• Seam or ore thickness and depth of cover.
• Proposed pillar width, length, and gallery dimensions.
• Unit weight of overburden and relevant strength parameters.

Uses tributary area based stress estimation together with published strength formulas for coal and hard rock pillars. Optional optimisation routines search for pillar sizes that achieve a target Factor of Safety while maximising extraction. Local calibration factors can be applied where back analysis data exist.

Main outputs are estimated pillar strength, stress, Factor of Safety, and extraction ratio. These should be compared with local experience and regulatory guidelines, not used as final approval values in isolation.

• Assumes regular layouts and tributary loading conditions.
• Does not explicitly capture time dependent deformation or creep.
• Local geological discontinuities such as faults and dykes are not explicitly modelled and must be handled by engineering judgement.

Use wording such as "Pillar dimensions were screened using MiningToolkit pillar design calculators based on tributary area concepts and empirical strength formulas, with local calibration where available. Final layouts were checked against site experience and regulatory practice."

• Opening dimensions and shape.
• Rock mass classification parameters or pre computed classes.
• Basic support system options such as bolt length and capacity.

Uses empirical design charts and ground response curve concepts where applicable. It does not run full structural analysis of each element but focuses on ranges that are known to be workable in typical conditions.

Outputs include suggested bolt spacing, layout density, and indicative support demand. A compact summary appears on the dashboard as the Support Design Snapshot.

• Does not cover special systems such as yielding bolts or advanced arches.
• Assumes stable ground beyond the support horizon.
• Complex three dimensional interactions between multiple openings are not captured.

Treat suggestions as starting points to be adjusted with site experience and detailed numerical or observational methods where required.

• Borehole diameter, burden, spacing, and bench height.
• Explosive type, density, and charge distribution.
• Stemming length and initiation pattern.
• Distances to structures or instruments for vibration checks.

Use field measured distances and product properties where possible. Local site calibration data for PPV or fragmentation should be captured and reused rather than default constants.

Fragmentation estimates follow Kuz Ram style relationships and similar empirical formulas. Vibration predictions use scaled distance laws and user supplied or literature based constants. Simulations interpolate these results into visual fields but do not change the underlying formulas.

• Expected mean fragment size and distribution description.
• Predicted PPV at specified distances and qualitative damage bands.
• 2D pattern and 3D blast volume views for review meetings.

Outputs are most reliable inside the range where scaling laws were derived. They should be checked against trial blasts and instrumentation results.

• Complex geology or jointing may cause fragmentation to depart from models.
• PPV laws are very sensitive to site specific calibration constants.
• Simulations show patterns; they are not substitutes for blast trials.

State clearly that blast designs were screened using MiningToolkit calculators with stated burden, spacing, product properties, and calibration constants, and that final patterns were confirmed against trial results and DGMS limits.

Each simulation type has its own control panel. Typical inputs include slope angles and bench widths, tunnel size and lining properties, pillar dimensions, overburden height, and basic material strength indicators. Labs also accept parameter sets sent from calculators for consistency across tools.

Simulations use simplified stress and flow models that are fast enough to run interactively. They are not full finite element or CFD solvers. The goal is to show direction and relative sensitivity rather than to match absolute values from detailed codes.

• 2D and 3D views of geometry and indicative stress or displacement fields.
• Basic numeric summaries in the MetricsSummary panel.
• Snapshots that can be exported for reports or toolbox talks.

• Not suitable for direct use in statutory design submissions.
• Does not capture complex non linear behaviour, time dependence or anisotropy.
• Highly simplified boundary conditions compared with specialist codes.

When using screenshots in reports, label them clearly as conceptual simulation views and pair them with a short explanation of what they are intended to show and which detailed analyses support the final design.

• Equipment fleet, personnel count and production levels for airflow sizing.
• Airway dimensions and roughness for pressure loss estimates.
• Gas readings by location, time and gas species for monitoring.

Airflow and pressure tools use standard network and resistance equations with user controlled unit systems. Gas monitoring combines entry forms, history tables, trend charts and compliance dashboards that compare readings with statutory limits and internal trigger levels.

Outputs include recommended airflow values, estimated fan duties, tabulated gas histories, compliance status per gas and simple derived indicators such as minimum, maximum and trend directions.

• Network simplifications may not capture detailed leakage paths or local recirculation.
• Sensor accuracy and calibration quality directly affect gas results.
• Missing metadata such as exact location or time can make trends hard to trust.

Use compliance dashboards to support daily reviews and DGMS reporting, but keep raw logs and calibration certificates as the primary record for audits.

• Fleet composition and capacities.
• Cycle time components and haul distances.
• Shift lengths and number of shifts per day.

The tools use standard production and match factor formulas, with options for heterogeneous fleets and scenario comparison. Time series views use synthetic or logged data to highlight peaks, troughs and potential bottlenecks.

Outputs include daily production estimates, match factors, truck throughput, and cycle time distributions, which also feed into the dashboard analytics section.

• Does not capture all maintenance and operator behaviour effects.
• Assumes that haul road conditions and delays match the input values.
• Real time dispatch systems may show different numbers due to live delays.

Use these outputs to frame operations meetings and to support what if discussions, then compare against actual production and delay statistics each shift.

• Hazard type and monitoring mechanism, for example slope radar or prisms.
• Displacement and velocity histories or back analysis results.
• Chosen generation method such as Factor of Safety, mechanism based or generic.

The module can derive thresholds from Factor of Safety, from mechanism templates, from back analysis of failures or from generic templates. It can also apply filters such as EWMA, compute cumulative damage indices, Poisson style exceedance probabilities and spatial hazard indices, and then convert scores to TARP levels.

Outputs include threshold tables, current and previous TARP levels, monitoring dashboards, DGMS style TARP templates and exportable reports. These are meant to be integrated into site control room procedures.

• Requires reliable and consistently sampled monitoring data.
• Thresholds generated from poor back analysis will not behave better than the underlying data.
• The tool does not replace continuous engineering review of alarming trends.

When presenting TARPs, always state which generation method was used, what data period it was based on, and how escalation and de escalation rules are applied in practice.

• Tabular datasets with clear column names and units.
• Hypothesis and study brief written in plain language.
• Choice of statistical model and covariates.

Regression follows standard methods such as OLS, Ridge and Lasso with clear diagnostics and all numerical analysis (models, ML, diagnostics, exports) happens in the Regression Analysis tab. Innovative Approaches and the AI Research Agent sit around that core: they help you choose and explain methods, and the Agent can suggest practical tasks and offerCSV templates for data capture, but they do not run regression or ML themselves and never silently change numerical calculations.

Outputs include model summaries, diagnostic plots, robustness checklists and exportable reports. These can be attached as appendices to theses or internal research notes.

• Garbage in, garbage out applies strongly to regression work.
• Correlated predictors and small sample sizes can give unstable results.
• The AI research agent can suggest ideas but cannot take responsibility for methodological choices.

When citing, name both MiningToolkit and the underlying methods or packages you rely on, and provide version and date information so that reviewers can repeat the work if needed.

Map and globe views draw from built in datasets and from data that you import. Import tools typically accept CSV or similar tabular formats with clear column names and units. Session and audit tools require that you use the calculators while signed in so that work can be associated with a user and a time stamp.

The mapping and database views do not perform heavy numerical calculations. Their role is to aggregate and display data from multiple modules and sites in a consistent way so that spatial patterns and cross site comparisons are easy to see. Session and audit tools capture which modules were used, with which input sets, and when exports were generated.

• Map and globe layers showing mines, deposits and relevant context.
• Imported data tables linked to sites or projects.
• Session lists with titles, time stamps and associated tools.
• Audit style views of exports and key calculation runs.

• Public or example datasets may be incomplete or simplified and should not be treated as authoritative for resource reporting.
• Import errors such as misaligned columns or wrong units can quietly distort downstream interpretations if not checked.
• Session and audit logs record how the toolkit was used but do not track every external step taken in spreadsheets or other software.

Use mapping and global datasets to give context in presentations and reports, but always name the original data source. For audit and review, export or print session summaries and pair them with a short narrative of the engineering decisions taken between runs.

The Hub does not require data entry. Instead, you browse topic cards, search across sections, and drill into subsections with worked examples and formulas. The glossary and formulas reference share the same spirit: structured definitions, units, and equations that you can cite in reports.

No hidden calculations run in the background. Knowledge Hub pages are static explanations, diagrams and examples that mirror what a careful set of lecture notes or a chapter in a textbook would contain. They are deliberately separate from calculators so that reading them cannot change any regression, economics or design outputs.

The Rare Earth & Critical Minerals section covers geology and deposit styles, processing flowsheets, basket economics, environmental and social issues, recycling and the Indian context. It is meant to help you frame REE and other critical mineral projects, not to replace detailed project models. For example, basket price examples in the Hub should be treated as worked illustrations; formal economics still belong in the Economics and Research modules with explicitly documented assumptions.

• Content is generic by design. It does not reflect site-specific geology, regulatory settings or commercial agreements.
• Worked examples use illustrative numbers. Never copy them directly into bankable models or statutory submissions.
• Where topics summarise external standards or literature, always refer back to the primary documents for final wording and thresholds.

When you use the Knowledge Hub to support a conclusion, cite it explicitly as a secondary reference (for example, alongside the original paper or code of practice) and note the date you accessed it. Treat it as structured lecture notes that help you explain your reasoning, not as a substitute for primary standards or peer-reviewed sources.