Conducting Project Feasibility Studies

💡How to choose right UX research methods Selecting the best UX research method depends on the situation and the goal of your research. Two key criteria help guide this choice: ✅ Situation vs. Solution ✅ Qualitative vs. Quantitative 📕 Situation vs solution This criterion distinguishes whether you are exploring a problem space or evaluating a solution. Situation research is all about understanding users, their pain points, needs, and context in which they interact with your product. It typically includes methods like ✔ Interviews ✔ Ethnographic studies ✔ Contextual inquiry ✔ Diary studies Solution research is all about testing concepts to understand the effectiveness of a design solution. This research typically includes methods like ✔ Usability testing ✔ Heuristic evaluation https://lnkd.in/dJSw2KyH  ✔ A/B Testing https://lnkd.in/dYeD_yKG  ✔ Tree testing https://lnkd.in/dHsFc3te Situation vs solution: How to Decide? If you are in the early design phase → Use situation-focused methods to explore user needs. If you have a prototype or product → Use solution-focused methods to evaluate and optimize. 📘 Qualitative vs quantitative This distinction determines whether you need deep insights (why & how) or measurable data (what & how much). Qualitative methods will help you understand behaviors, motivations, and experiences of your users. Use methods like ✔ User interviews ✔ Concept testing ✔ Field studies ✔ Diary studies Quantitative methods aim to measure patterns, trends, and statistical significance. Examples of methods include ✔ User surveys ✔ Analytics ✔ A/B testing ✔ Heatmaps Qualitative vs quantitative: How to Decide? If you need rich, detailed insights → Choose qualitative methods. If you need large-scale, statistically valid data → Choose quantitative methods. Often, the best approach is a mixed-method strategy, using both qualitative and quantitative research. For example: 1️⃣ Start with user interviews (qualitative) to uncover pain points. 2️⃣ Validate findings with surveys or analytics (quantitative). 3️⃣ Conduct usability testing (qualitative) to identify issues in a prototype. 4️⃣ Run A/B testing (quantitative) to measure which solution performs better. 🖼️ Landscape of UX research methods by Konrad Group #UX #uxresearch #design #userresearch #productdesign

Rules of thumb for mine planning: **Economic Guidelines:** - Ore reserves should support at least 10-15 years of operation for new mines - Stripping ratio (waste:ore) should typically be below 3:1 for open pit viability - Cash operating costs should be in the bottom 50% of the cost curve for long-term sustainability - Capital payback period should be under 3-5 years **Open Pit Design:** - Overall slope angles: 35-50° depending on rock strength and groundwater conditions - Bench heights: 10-15m for most operations, up to 20m for very large mines - Minimum mining width: 3-4 times the largest equipment width - Road grades: maximum 8-10% for loaded haul trucks **Underground Planning:** - Pillar safety factor should be 1.6-2.0 minimum - Development costs are typically $1,000-3,000 per meter - Extraction ratios: 60-85% depending on mining method and ground conditions - Ventilation requirements: 2-3 m³/s per person minimum **Production Planning:** - Annual production should be 8-12% of proven reserves - Equipment availability: 85-90% for mobile equipment, 90-95% for fixed plant - Stockpile capacity should handle 7-14 days of mill feed - Maintain 2-3 months of developed ore ahead of production **Resource/Reserve Conversion:** - Typically 60-80% of resources convert to reserves after feasibility studies - Grade control drilling should be 4-10 times denser than resource drilling - Include 10-15% dilution and 5-10% ore loss in reserve estimates **Infrastructure:** - Power requirements: 15-25 kWh per tonne of ore processed - Water consumption: 0.5-2 m³ per tonne of ore processed - Tailings facility should accommodate 120-150% of planned production **Risk Management:** - Commodity price assumptions should be conservative (often 80-90% of current prices) - Include 15-25% contingency in capital cost estimates - Plan for 10-20% lower grades than resource model predictions These guidelines help ensure technically feasible and economically viable mine plans while managing key risks.

Pre-feasibility is when investors stop believing stories and start reading spreadsheets. If you look at the lifecycle of a junior mining company, there’s a very specific moment when everything changes. That moment is pre-feasibility. Suddenly, the investor base shifts. You move from early-stage risk lovers — those who see “lottery tickets” in drill results — to institutional investors, strategics, and private equity funds that don’t like surprises. These aren’t the folks betting on blue-sky geology. They’re betting on cash flow. They care about IRR, payback periods, and, most importantly, confidence in the technical model. In the typical risk graph, this is where the profile shifts — from “exploration uncertainty” to “execution uncertainty.” In plain terms: no one’s worried anymore about whether the deposit exists. Now they’re worried about whether you can make money extracting it. And that’s when most projects start sweating. Because this is the moment when investors start doing the math. They don’t care about pretty grade shells. They want to know if your mill will choke. If your throughput assumptions are real. If your CAPEX has been stress-tested against variability. If your 15-year mine plan won’t collapse in year three. What they absolutely don’t want is a metallurgical surprise. And yet, that’s still one of the most common reasons projects fall apart at PFS or FS — not because the resource isn’t there, but because the flowsheet wasn’t grounded in representative data. The real problem? These surprises aren’t unpredictable. They’re just unmeasured. It’s not that the ore “got harder.” It was always hard — you just didn’t test enough of it in the right places to find out. Your throughput assumptions came from 30 composite samples. Your mill configuration was based on the friendly parts of the orebody. Your block model had no idea what was coming down the conveyor belt. At Geopyörä, we’re not offering more complexity. We’re offering a way to connect the data you already have — your assay samples — to the data your investors need: true comminution behavior. By extracting comminution parameters from small assay samples and combining them with geochemistry and mineralogy, we enable predictive geomet models that support financing conversations — rather than derailing them. It means you don’t have to pretend that 42 samples define your throughput curve. It means you can build a block model that doesn’t just map grade, but maps processability. And it means that when an investor asks, “How confident are you in your P80 across domains?”— you won’t need to change the subject. This is about moving from storytelling to data. From geologist optimism to metallurgical realism. From fundraising based on inferred ounces to project delivery based on predicted performance. Because in the eyes of risk-averse investors, confidence isn’t a feeling — it’s a dataset. And if you don’t have it, someone else will.

I'm seeing industrial operators and data centers commission feasibility studies that don't answer the right questions. And with NERC's 2025 Long-Term Reliability Assessment flagging 13 of 23 regions at resource adequacy risk through 2030, the stakes just got higher. MISO, PJM, Texas ERCOT, WECC-Northwest, WECC-Basin, SERC-Central. High-risk regions. The same regions where data center and industrial load growth is heaviest. That's not a coincidence. The grid reliability problem isn't just about capacity. It's about the type of capacity. Coal retirements are accelerating. Solar and batteries are coming online fast. But when you model dispatch during tight hours (winter peaks, extreme weather), the reliability attributes aren't the same as the baseload capacity they're replacing. Layer surging peak demand from data centers and electrification on top of that, and the gap widens between what the grid can reliably deliver and what industrial operators need to run 24/7. Which brings us to behind-the-meter generation and microgrids. Legal since the 1970s. What's changed: the economics now justify it as a competitiveness strategy, not just a resiliency backup. Most industrial teams commission a feasibility study. It comes back with a topline number: "Yes, on-site generation is possible. Here's the estimated cost." That's not enough. You need to know: • What's the optimal configuration for the best price per megawatt? • How does on-site generation compare to utility rates over 10+ years, including rate escalation? • Which combination of assets (gas, solar, battery, hybrid) delivers the best economics under high growth, low growth, and base case scenarios? • How does this hold up if fuel costs spike or equipment costs come in higher? Most feasibility studies don't model that. They give you a snapshot, not a stress test. In the microgrid space, we do feasibility analysis, but it's a techno-economical study. We model your load. Simulate multiple generation configurations. Run sensitivity analysis across different futures. Compare on-site vs. utility economics even if you already have grid access. The result: you know the optimal price per megawatt configuration and whether the economics hold up when the assumptions change. That's the difference between making an informed decision and hoping the utility can keep up. —— Evaluating behind-the-meter generation or microgrid solutions for your data center or industrial facility? Let's talk. I'll walk you through what a proper techno-economical study covers and what the numbers look like for your site. Grab time on my calendar or give me a call. 🗓️ https://t2m.io/mMoKxRy | 📱 1-888-218-6001 Image Source: NERC LTRA 2025

Factors that Influence whether a Deposit can be Mined ? The viability of mining a deposit depends on geological factors like ore grade, tonnage, and mineralogy, which affect extraction and processing. Location impacts operational costs, while economic factors such as metal prices and demand influence profitability. Technological advancements improve extraction efficiency, even for lower-grade ores. Political stability and regulatory conditions also affect investment risk. A thorough analysis of these factors determines if a deposit can be mined profitably. 1️⃣ Grade, Tonnage, and Economic Thresholds Grade-Tonnage Relationship: High-grade and large-tonnage deposits are preferred, but economic thresholds like the cut-off grade determine mineability. Metal Prices: Price fluctuations influence cut-off grades, expanding or contracting mineable reserves. 2️⃣ Mineralogy and Ore Processing Ore Complexity: Mechanically bound ores (e.g., placer gold) are simpler to process compared to chemically bound ores (e.g., sulfides and silicates). Grain Size: Coarse-grained ores (e.g., Broken Hill) are easier to process than fine-grained ones (e.g., McArthur River). By-Products and Impurities: Valuable by-products (e.g., silver in copper ores) enhance viability, while impurities (e.g., arsenic) increase costs. 3️⃣ Geological and Structural Factors Depth and Accessibility: Shallow deposits favor open-pit mining, whereas deep deposits require costlier underground methods. Continuity: Regular ore bodies (e.g., Bushveld Complex) are easier to mine than disrupted ones (e.g., Great Dyke). Host Rocks: Soft sedimentary rocks are cheaper to mine than hard igneous formations. 4️⃣ Location and Infrastructure Proximity to Infrastructure: Accessibility to transport, power, and water reduces costs. Geopolitical Risks: Political stability is critical for uninterrupted operations. Environment: Harsh climates or remote locations increase capital and operational costs. 5️⃣ Technological Advancements Innovative Techniques: Hydrometallurgy and bioleaching enable processing of complex ores. Reprocessing: Tailings reprocessing, like in Western Australia, recovers additional value from waste. Automation: Enhances operational efficiency and reduces costs. 6️⃣ Market and Economic Factors Demand for Minerals: Increasing demand for critical minerals (e.g., lithium, rare earths) drives project viability. Metal Prices: Higher prices make marginal deposits economically viable. 7️⃣ Environmental and Social Considerations Sustainability: Practices like tailings recycling and carbon-neutral mining reduce environmental impact. Community Engagement: Maintaining good relations with stakeholders ensures smoother operations Mining a deposit depends on an intricate balance of geological, technical, economic, and social factors. Advances in technology and adherence to sustainable practices continue to shape the industry’s future. #Geology #OreDeposits #MiningEconomics #Mineralexploration

Renewable-Powered Battery Swaps: Unlocking Ship Electrification At Global Canals When discussing maritime electrification, the idea of mid-ocean recharging frequently emerges. It's a good notion except for everything about its feasibility, costs and operations, but there is a variant that makes sense. Full article: https://lnkd.in/guSKaB9r The Maersk McKinney Moller Center's recent analysis correctly concluded that battery-electric ships are viable and increasingly competitive, driven by falling battery prices, rising energy density, and easy integration of containerized battery packs onto vessels. However, their assumptions were already outdated. They used battery costs of $300 per kWh, whereas current grid-scale battery packs in China are available for $51 per kWh, dramatically improving the economics and expanding where hybridization will take hold to transoceanic ships. The recharging concept that was mooted again by several commenters when I published on the Maersk study suggests placing large wind farm and charging facilities in mid-ocean locations, allowing ships to carry smaller battery packs. Though attractive in theory, the harsh economic reality of offshore infrastructure quickly sets in. Marine engineering costs escalate exponentially. My rule of thumb is that infrastructure that costs $1 onshore typically rises to about $10 offshore, $100 subsea, and potentially over $1000 for deep ocean locations. Offshore projects only succeed under highly favorable or strategically critical conditions, such as offshore wind near dense energy demand centers or over high-value oil reserves, not in isolated, storm-prone regions like the Aleutians or mid-Atlantic. However, the broader concept of intermediate charging shouldn't be dismissed entirely. There's significant practical and economic potential for containerized battery exchanges at existing maritime choke points like the Panama Canal, Suez Canal, Strait of Malacca, and possibly Gibraltar. These locations offer strong renewable resources, existing port infrastructure, predictable ship stops, and operational simplicity. Containerized battery swaps could easily integrate into routine ship operations, drastically cutting onboard battery requirements, vessel weight, and costs. The maritime industry should prioritize developing standardized containerized battery exchange solutions at these established chokepoints rather than chasing economically unfeasible mid-ocean charging stations. Collaboration among maritime stakeholders — ship operators, port authorities, regulators, investors, and suppliers — is essential. Pilot projects at locations like the Suez or Panama canals could rapidly prove the economic and operational case, paving the way for wider adoption and accelerated maritime decarbonization.

Tonnage and grade get a project discovered. Geometallurgy, Geotech, and Hydrogeology get it built or break it. From my experience in exploration and production, the most expensive mistake in mining is waiting until the Feasibility Study to seriously think of these "non-grade" factors. A 3D grade-only model is an incomplete map. To truly de-risk a project and protect its NPV, we must integrate the "how" with the "what" from day one. Geometallurgy: Your model must include recovery, hardness , and processing domains. A high-grade, refractory ore block is a liability, not an asset, if your plant can't handle it. Geotechnical: Your model must include RQD and structural domains. A weak hanging wall will destroy your economics with dilution long before a pit slope failure suspends your operations. Hydrogeology: Your model must include high-permeability zones. Unbudgeted dewatering (OPEX) or a catastrophic water inrush can sink a project faster than low grades. The goal isn't separate reports. The goal is a single, unified 3D block model a "Single Source of Truth" that informs mine planning, metallurgy, and engineering simultaneously. That is how you build a resilient, profitable mine. #Mining #MineralExploration #Geology #Geometallurgy #Geotechnical #Mining_Project_Risk_Management

𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐄𝐕 𝐄𝐦𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐭 𝐃𝐢𝐚𝐦𝐞𝐫 𝐁𝐚𝐬𝐡𝐚 𝐃𝐚𝐦 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 ⚡🚜 At Diamer Basha Dam, our fleet includes Volvo FX400/440 dump trucks, Hyundai 40Ton excavators, Volvo front-end loaders, telescopic handlers, drilling machines (DTH, TH), and a range of utility vehicles. Currently, around 40% 𝒐𝒇 𝒐𝒖𝒓 𝒐𝒑𝒆𝒓𝒂𝒕𝒊𝒐𝒏𝒂𝒍 𝒆𝒙𝒑𝒆𝒏𝒅𝒊𝒕𝒖𝒓𝒆 𝒊𝒔 𝒄𝒐𝒏𝒔𝒖𝒎𝒆𝒅 𝒃𝒚 𝒅𝒊𝒆𝒔𝒆𝒍/𝒃𝒊𝒐𝒇𝒖𝒆𝒍– c̳o̳s̳t̳l̳y̳ ̳a̳n̳d̳ ̳e̳n̳v̳i̳r̳o̳n̳m̳e̳n̳t̳a̳l̳l̳y̳ ̳c̳h̳a̳l̳l̳e̳n̳g̳i̳n̳g̳.̳ We have been evaluating how 𝐞𝐥𝐞𝐜𝐭𝐫𝐢𝐜 𝐯𝐞𝐡𝐢𝐜𝐥𝐞𝐬 (𝐄𝐕𝐬) can transform our operations. The benefits are clear, but the key question remains: 👉 How do we sustainably charge these EVs? Charging through diesel or HFO generators is not a real solution. Instead, we are exploring a complete ecosystem: ·       𝐇𝐲𝐛𝐫𝐢𝐝 𝐫𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞 𝐜𝐡𝐚𝐫𝐠𝐢𝐧𝐠 (solar + wind integration at site) ·       𝐁𝐚𝐭𝐭𝐞𝐫𝐲-𝐬𝐰𝐚𝐩 𝐬𝐲𝐬𝐭𝐞𝐦𝐬(already proven in large mining operations worldwide) ·       𝐇𝐢𝐠𝐡-𝐜𝐚𝐩𝐚𝐜𝐢𝐭𝐲 𝐛𝐚𝐭𝐭𝐞𝐫𝐢𝐞𝐬 balanced against financial feasibility Our fleet operate 20-𝐡𝐨𝐮𝐫 𝐝𝐚𝐢𝐥𝐲 𝐬𝐡𝐢𝐟𝐭𝐬 (10 + 10) making 𝐜𝐡𝐚𝐫𝐠𝐢𝐧𝐠 𝐭𝐢𝐦𝐞 𝐚𝐧𝐝 𝐫𝐚𝐧𝐠𝐞 𝐩𝐞𝐫 𝐜𝐲𝐜𝐥𝐞 critical. While a 60-minute charge could fit into break time, if one cycle cannot cover haulage of 8-9 trips/dump truck, we face operational constraints. This opens a larger discussion: ·       𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐜𝐡𝐚𝐫𝐠𝐢𝐧𝐠 via 𝐫𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞 𝐞𝐧𝐞𝐫𝐠𝐲 𝐬𝐨𝐮𝐫𝐜𝐞 vs 𝐫𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 within 𝐣𝐨𝐛 𝐬𝐢𝐭𝐞? ·       Is 𝐛𝐚𝐭𝐭𝐞𝐫𝐲 𝐬𝐰𝐚𝐩𝐩𝐢𝐧𝐠 the viable path forward for continuous operations? ·       How do we balance 𝐡𝐢𝐠𝐡𝐞𝐫 𝐛𝐚𝐭𝐭𝐞𝐫𝐲 𝐜𝐚𝐩𝐚𝐜𝐢𝐭𝐲 𝐯𝐬 𝐜𝐨𝐬𝐭 𝐟𝐞𝐚𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲? ·       What 𝐥𝐞𝐬𝐬𝐨𝐧𝐬 can 𝐭𝐮𝐧𝐧𝐞𝐥𝐢𝐧𝐠, 𝐦𝐢𝐧𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐦𝐞𝐠𝐚 𝐝𝐚𝐦 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 learn from each other on 𝐄𝐕 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧?

“𝑯𝒐𝒘 𝒎𝒖𝒄𝒉 𝒊𝒔 𝒓𝒆𝒂𝒍𝒍𝒚 𝒊𝒏 𝒕𝒉𝒆 𝒈𝒓𝒐𝒖𝒏𝒅?” This is the single most important question in mining. To answer this, mining engineers and geologists use different resource estimation methods. Each method has its own accuracy, data requirements, and ideal use case. 1. Polygonal / Triangular Methods (Classical) Draw polygons (or triangles) around sample points (e.g., drill holes). Assign the grade of the sample to the whole polygon or use the average of three samples for a triangle. Used in : Early exploration, very sparse data, quick first-look estimates. 2. Inverse Distance Weighting (IDW) Estimate the grade at an unsampled point by averaging nearby samples. Closer samples have more weight (weight decreases with distance, often by distance²). Used in : Moderate drill density, mid-stage projects needing a straightforward interpolator. 3. Ordinary Kriging (OK) Use a semivariogram to model how grades correlate with distance and direction. Calculate optimized weights from that model to produce an unbiased estimate and error measure. Used in: Advanced exploration, feasibility studies, and formal resource reporting (JORC/NI 43-101). 4. Indicator Kriging (IK) Convert grades into indicators (e.g., above/below a cutoff). Krige those indicators to estimate probabilities that blocks exceed cutoffs; combine probabilities to infer grade classes. Used in : Highly variable deposits, modelling cutoffs for ore/waste, probabilistic resource classification. 5. Sequential Gaussian Simulation (SGS) / Multiple Simulations Generate multiple equally-probable realizations of the grade distribution that honour data and spatial continuity. Use the ensemble of realizations to assess uncertainty and preserve local variability. Used in : Uncertainty / risk analysis, complex or highly heterogeneous ore bodies, mine planning with scenario testing. 6. Machine Learning (ML)–Based Estimation Use supervised learning algorithms (e.g., random forests, gradient boosting, neural networks) to predict grades or classes from many inputs: drill data, geology logs, geophysics, remote sensing, structural interpretations, and derived features. ML models learn non-linear relationships and can incorporate large multi-source datasets. Often used together with spatial methods (e.g., ML predictions as inputs to kriging or as features in simulations). Used in : Complex datasets with many predictors, integrating geophysics/chemistry/structural data, rapid scenario testing, and when non-linear patterns are suspected. Increasingly used for feature engineering, anomaly detection, and to augment traditional geostatistics. #mining #geology #resources #resourceestimation #geostatistics #Kriging #IDW

Feasibility of a utility-scale BESS project: 1. Site Selection Location Suitability: Evaluate the site for physical space, accessibility, and proximity to the grid connection point. Consider factors like land ownership, zoning regulations, potential for expansion. 2. Grid Connection and Integration Interconnection Requirements: Analyze the technical requirements for connecting the BESS to the grid, including voltage levels, power capacity, and grid stability. Grid Compatibility: Ensure the BESS can handle grid dynamics, such as fluctuations in voltage and frequency, and assess the system’s ability to provide ancillary services like frequency regulation or reactive power support. 3. Battery Technology Selection Technology Suitability: Compare different battery technologies (e.g., lithium-ion, flow batteries, solid-state) based on energy density, cycle life, efficiency, and response time to ensure the project’s needs. Thermal Management: Consider the thermal management requirements of the selected battery technology, including cooling systems and potential for thermal runaway. 4. System Sizing & Scalability Energy & Power Requirements: Determine the optimal size of the BESS based on the project's storage and power output. This includes peak load demands, duration of energy discharge, and frequency of cycling. Scalability: Assess the potential for future expansion and whether the system design can be scaled up to accommodate increased demand or additional storage capacity. 5. Performance and Reliability Cycle Life & Degradation: Evaluate the expected cycle life of the batteries and their degradation rate over time, considering the impact on performance and maintenance costs. System Reliability: Analyze the reliability of the entire system, including power conversion systems, inverters, and control systems. Ensure redundancy and fail-safes are in place to maintain continuous operation. 6. Control & Communication Systems EMS: Evaluate the control systems responsible for managing the charge/discharge cycles, ensuring optimal performance, and integrating with the broader energy management strategy. Communication Protocols: Ensure compatibility with existing grid communication protocols and consider the need for secure, real-time data exchange between the BESS and grid operators. 7. Energy Efficiency & Losses Round-Trip Efficiency: Calculate the round-trip efficiency of the BESS, considering losses during charging, discharging, and energy conversion. This impacts the overall economic feasibility of the project. Self-Discharge Rate: Evaluate the self-discharge rate of the batteries and how it affects long-term storage efficiency, especially for applications requiring extended storage. 8. Integration with Renewables Renewable Energy Compatibility: If the BESS is intended to integrate with renewable energy sources (e.g., solar, wind), assess the compatibility of the system in terms of variability in generation and storage. #BESS #Powersystem #renewable