Conducting Project Feasibility Studies

Don’t make these common mistakes in techno-economic assessments (and avoid misleading conclusions.) TEA is a powerful tool to assess the feasibility of emerging technologies. But even small mistakes can lead to misleading conclusions and poor decisions. Here are 5 key mistakes I’ve seen repeatedly—and how to fix them: 1. Overestimating Technology Performance Challenge: Assuming ideal or lab-scale performance when scaling up. Real-world conditions often bring inefficiencies. Fix: Use conservative assumptions, validate with experimental data, and conduct sensitivity analysis. 2. Ignoring Uncertainty Problem: Treating input values (e.g., costs, energy efficiency) as fixed leads to rigid, unreliable results. Fix: Perform sensitivity and scenario analyses to identify critical variables and explore best/worst cases. 3. Using Outdated or Poor-Quality Data The Problem: Relying on old data or inconsistent sources reduces the credibility of your TEA. Fix: Source data from updated literature, validated models, or credible industry benchmarks, and clearly document assumptions. If data is missing for new technologies, use proxy technologies and check uncertainties. 4. Oversimplifying Economic Analysis Problem: Focusing only on capital costs (CAPEX) while ignoring operating costs (OPEX), maintenance, or financing impacts. Or focusing on single metrics, like NPV. Fix: Include all cost components—CAPEX, OPEX, and life-cycle costs—and calculate key metrics like NPV, IRR, and payback period. 5. Neglecting Policy and Market Factors Problem: Ignoring factors like carbon pricing, subsidies, or fluctuating raw material costs can skew results. Fix: Integrate policy scenarios, market trends, and potential incentives to build a more realistic TEA. Techno-economic analysis is only as good as its assumptions and methods. Avoiding these mistakes will help you deliver insights that are credible, actionable, and valuable for decision-making. We’re going to discuss all these challenges with TEA and more during my workshop in Q1 2025. What challenges have you faced when conducting TEA? I’d love to hear your thoughts in the comments! #Research #ChemicalEngineering #Economics #Energy #PhD #Scientist #Professor

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

One mining scandal wiped out $6 billion in investor money. Here's how: In 1997, Bre-X Minerals claimed to have found the "gold discovery of the century" in Indonesia. Their stock exploded from $0.50 to $286 in just 3 years. When independent verification finally happened, they found "insignificant amounts of gold." The gold didn't exist. Core samples had been "salted" with gold dust bought from local Indonesian panners. $6 billion vanished overnight. The smoking gun was hiding in plain sight – in technical reports most investors couldn't understand. This scandal forced Canada to create NI 43-101 – strict standards for mineral project disclosure. But the jargon in these reports can still make or break your investment. Here's what you need to know: 1. Understand mineral resource estimates These come in 3 categories of increasing confidence: • Inferred - Based on limited sampling, lowest confidence • Indicated - More exploration, reasonable confidence • Measured - Most reliable, based on detailed data Resources ≠ guaranteed money. 2. Know the difference between "reserves" and "resources" • Resources = What might be in the ground • Reserves = What's economically mineable Reserves have 2 critical categories: • Probable - Lower confidence • Proven - Highest confidence 3. Grade determines profitability It's the concentration of valuable mineral within the ore. Bre-X reported consistently high gold grades across large areas – which almost never happens naturally. Watch for these warning signs: • Lack of visible minerals despite high reported grades • Unrealistic (and increasing) projections • Suspicious consistency in samples • Unusually perfect conditions Before investing, ask: • Are resource estimates compliant with standards like NI 43-101? • What percentage is classified as Measured vs. Indicated vs. Inferred? • Has an independent qualified person verified the estimates? • Does management have a successful track record? At Power Metallic, transparency is our foundation. Our nickel reports use the NI 43-101 standards created post-Bre-X. For our Lion Zone copper discovery, we're publishing drill collars, assays, and geophysical data in real time – allowing independent analysts to model the deposit as it expands. A formal 43-101 for copper is coming in H2 2026. - Thanks for reading. I’ve spent decades in the trenches—building companies, making discoveries, and fighting for fairness in the markets. Follow me, Terry Lynch for straight talk on exploration, capital markets and creating real value in mining.

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

Ever wonder how companies actually decide whether an energy-saving project is worth the money? The golden standard is the Internal Rate of Return (IRR) – it’s the single most powerful metric in engineering economics. In simple terms:  The IRR tells you the annual return a project generates over its lifetime.  If the IRR is higher than your company’s Minimum Attractive Rate of Return (MARR – basically your cost of capital or hurdle rate), the project makes financial sense. The bigger the gap, the better the investment. Other key concepts in play:  - Net Present Value (NPV) = 0 at the IRR  - Uniform Annual Series (A) – same savings every year  - (P/A, i%, n) factor – converts annual amounts to today’s dollars  - MARR – the minimum return your company will accept (often 8–15% depending on risk) Now, let’s see this in action with a manufacturing example : Project: Energy Conservation Measure (ECM)  - Initial cost: $100,000  - Annual energy savings: $23,400  - Life: 12 years  - Company MARR: 12% Using the classic (P/A) factor method:  Required factor = 100,000 ÷ 23,400 = 4.2735  From interest tables → this falls between 20% and 21%  After interpolation (or Excel IRR function) → IRR = 20.7% That’s nearly 9 percentage points above MARR — an absolute no-brainer. Bottom line: This project doesn’t just pay back… it delivers outstanding returns with a huge safety margin. Check the infographic below for the full step-by-step calculation — perfect if you’re preparing for PE pr CEM exams. #EngineeringEconomy #IRR #EnergyEfficiency #CapitalProjects #Sustainability #Manufacturing #PEexam #EnergyManagement

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

Not every C&I solar project is viable, I learnt this the hard way. It’s easy to jump at the show of a new C&I lead. Many developers and EPCs assume that every working factory, mart, farm, or hospital is a viable solar candidate. You scan industrial rooftops, chase meetings, and finally get invited to perform site assessments and energy audits. Excitement builds. You involve the engineering team, you design diligently, you push hard through your process. But then, weeks or months in, you hit a roadblock: the economics don’t stack, the client can’t commit, or the financier isn’t convinced. C&I projects aren’t about panels and batteries. They’re about business cases. And business cases need to make sense to two groups: The Offtakers → clients who must see real savings and operational value. The Financiers → investors who must see risk-adjusted returns. If you can’t defend both sides, then what you have is not a project, it’s just a lead. So, how do you qualify early? Start with three fundamental filters: 1️⃣ Load Profile: Does the client’s consumption pattern align with solar generation? A factory running 8 am–6 pm is viable. A hotel with peak load at midnight may not be, unless they’re ready to pay for storage. 2️⃣ Tariff Environment: What benchmark are you competing against? If grid tariffs are cheap and reliable, solar won’t make economic sense. But if diesel costs are spiraling, solar PPAs suddenly become compelling. 3️⃣ Client’s Energy Spend & Financial Strength: Is power a material cost for the business (e.g., power costs 20% of OPEX in agro-processing = urgent). And beyond these, you must run feasibility studies. They’re not paperwork. They’re the due diligence backbone: Technical → can the system physically work? Financial → do the numbers hold under stress tests? Legal/regulatory → are there barriers to connect or operate? Operational → will the client maintain and honor commitments? 🚩 Red flags you must not ignore: → Night-heavy loads with no storage appetite. → Clients with poor creditworthiness. → Subsidized tariff environments where solar can’t compete. → Weak roof structures or no space for panels. → Clients treating energy as a “nice to have” rather than a strategic priority. #SolarEnergy #RenewableEnergy #CISolar #EnergyTransition #PPAs #SolarProjects #EnergyFinance #CommercialSolar #IndustrialSolar #ProjectFinance #EnergyManagement #SolarDevelopment

“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

⛔ $67,000,000,000 was lost… ⛔ . . . To unpredictability.   According to Accenture, mining companies missed production targets by 2.6% per year on average over a five-year period, amounting to a mind-blowing $67B in lost revenue.   And this doesn't seem to be a one-time miss. The data shows it’s systematic.   🔍 Take a look at production variances from 2019 to 2023.   The pattern is clear across commodities (iron ore, gold, copper, zinc, nickel, coal):   Forecasting errors are NOT exceptions. They’re the norm.   Here is the real eye-opener: this analysis is based on annual forecasts—just one year ahead, using all the data we already have to predict 365 days later.   Now imagine the level of uncertainty in long-term production forecasts stretching 20, 30, 40 years into the future till the end of life of mine.   What’s a key factor contributing to this gap?   ➡️ Geological variability.   Even with skilled geology teams, detailed models, rigorous data collection, and flawless QA/QC, the subsurface remains inherently unpredictable.   It’s not the geologists’ fault. They’re not magicians. What lies underground is, by nature, uncertain. Geologists make informed interpretations based on the best available data. But they’re still just that: "interpretations".   And when mine plans rely on a single representation of the deposit, any deviation (grade, tonnage, recovery, etc.) translates into revenue volatility.   But this isn’t just a geology thing. It’s a (over)confidence problem!   🛑 (over)confidence in forecasts 🛑 (over)confidence in decisions 🛑 (over)confidence in capital allocation   We know the forecast is not reliable, but we still decide to rely on it and make decisions based on that…   ➡️ The alternative?   ✅ Taking that uncertainty into account when generating production forecasts to improve predictability. ✅ Using probabilistic forecasting to see a range of possible outcomes, assess risk, and support better-informed decisions.     Pretending we know exactly what’s underground doesn’t make it be there.   We can plan for what might happen to build stronger and more robust projects.   In the end, acknowledging variability isn’t a weakness. It’s a strategic advantage. 📊 The data is from a report published by Accenture: https://lnkd.in/ejmm5kpg A huge thanks to Bernd Elser for articulately explaining the problem. #Mining #Geology #MinePlanning #RiskManagement