Conducting Project Feasibility Studies

What Comes After “How Long Will Your Mine Last?” Imagine this: Your team has just cracked the numbers — 150 million tons of ore, a 15-year mine life, 10 million tons per year, and a 3-shift system to keep things moving. The board nods in approval. But before anyone celebrates, a new question fills the room: “What kind of plant are we building?” The real work is just beginning. This is where strategy takes over. Before a single machine is purchased or a foundation poured, you need a clear, proven process to design a plant that delivers gold — efficiently, reliably, and profitably. Here’s the strategic path mining professionals follow: -Metallurgical Test Work – Understand the Ore -Every orebody is different. You begin with lab testing to reveal the ore’s secrets: -How hard is it to crush and grind? (Bond Work Index, SAG testing) -Is there free gold recoverable by gravity? -What’s the best gold recovery method — CIL, CIP, or heap leach? -How does the ore behave in tanks and tailings ponds? These tests guide every decision that follows. Flowsheet Development – Draw the Recovery Path -Based on test results, you create the flow sheet: a diagram showing how the ore travels from rock to refined gold. Typical stages: -Crushing -Grinding -Gravity Recovery (if useful) -Leaching (CIL/CIP) -Elution + Electrowinning -Smelting -Tailings Disposal Each piece of equipment depends on how your specific ore behaves. Throughput & Mass Balance – Set the Scale. We already know: 10 million tons/year ~28,570 tons/day ~1,190 tons/hour Now we size each unit (crushers, mills, tanks, etc.) to handle the flow — with a safety margin. Trade-Off Studies – Pick the Smartest Option You now evaluate options to balance cost, performance, and future plans: -Gravity + CIL vs. direct CIL? -Modular plant or custom build? -Start small and expand or build full capacity now? -What’s cheaper long-term? These trade-offs prevent costly mistakes and guide smart investment. Preliminary Engineering – Turn Plans into Reality. You finalize the design: -Equipment specifications. -Layout drawings. -Power, water, and reagent systems. -Tailings and environmental plans. -Automation, safety, and control systems. This is the blueprint for building a plant that works in the real world. What’s Next? we’ll walk through a sample flowsheet for a mid-size gold operation and show how professionals select and size each piece of equipment to match their throughput and recovery targets. You’ll see how test results, tonnage plans, and flow-sheets come together — one machine at a time. Stay tuned. The plant is coming to life.

Many companies are diving into AI agents without a clear framework for when they are appropriate or how to assess their effectiveness. Several recent benchmarks offer a more structured view of where LLM agents are effective and where they are not. LLM agents consistently perform well in short, structured tasks involving tool use. A March 2025 survey on evaluation methods highlights their ability to decompose problems into tool calls, maintain state across multiple steps, and apply reflection to self-correct. Architectures like PLAN-and-ACT and AgentGen, which incorporate Monte Carlo Tree Search, improve task completion rates by 8 to 15 percent across domains such as information retrieval, scripting, and constrained planning. Structured hybrid pipelines are another area where agents perform reliably. Benchmarks like ThinkGeo and ToolQA show that when paired with stable interfaces and clearly defined tool actions, LLMs can handle classification, data extraction, and logic operations at production-grade accuracy. The performance drops sharply in more complex settings. In Vending-Bench, agents tasked with managing a vending operation over extended interactions failed after roughly 20 million tokens. They lost track of inventory, misordered events, or repeated actions indefinitely. These breakdowns occurred even when the full context was available, pointing to fundamental limitations in long-horizon planning and execution logic. SOP-Bench further illustrates this boundary. Across 1,800 real-world industrial procedures, Function-Calling agents completed only 27 percent of tasks. When exposed to larger tool registries, performance degraded significantly. Agents frequently selected incorrect tools, despite having structured metadata and step-by-step guidance. These findings suggest that LLM agents work best when the task is tightly scoped, repeatable, and structured around deterministic APIs. They consistently underperform when the workflow requires extended decision-making, coordination, or procedural nuance. To formalize this distinction, I use the SMART framework to assess agent fit: • 𝗦𝗰𝗼𝗽𝗲 & 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 – Is the process linear and clearly defined? • 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 & 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 – Is there sufficient volume and quantifiable ROI? • 𝗔𝗰𝗰𝗲𝘀𝘀 & 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Are tools and APIs integrated and callable? • 𝗥𝗶𝘀𝗸 & 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Can failures be logged, audited, and contained? • 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗹 𝗟𝗲𝗻𝗴𝘁𝗵 – Is the task short, self-contained, and episodic? When all five criteria are met, agentic automation is likely to succeed. When even one is missing, the use case may require redesign before introducing LLM agents. The strongest agent implementations I’ve seen start with ruthless scoping, not ambitious scale. What filters do you use before greenlighting an AI agent?

The report titled "𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗳 𝗔𝗴𝗿𝗶-𝗣𝗵𝗼𝘁𝗼𝘃𝗼𝗹𝘁𝗮𝗶𝗰𝘀 (𝗔𝗴𝗿𝗶𝗣𝗩) 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮𝗻 𝗔𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗲" evaluates the 𝘃𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗳 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝘀𝗼𝗹𝗮𝗿 𝗽𝗵𝗼𝘁𝗼𝘃𝗼𝗹𝘁𝗮𝗶𝗰 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘄𝗶𝘁𝗵 𝗳𝗮𝗿𝗺𝗶𝗻𝗴 𝗶𝗻 𝗠𝗮𝗵𝗮𝗿𝗮𝘀𝗵𝘁𝗿𝗮 (𝗽𝗼𝘁𝗮𝘁𝗼𝗲𝘀) 𝗮𝗻𝗱 𝗛𝗶𝗺𝗮𝗰𝗵𝗮𝗹 𝗣𝗿𝗮𝗱𝗲𝘀𝗵 (𝗮𝗽𝗽𝗹𝗲𝘀). Here's a concise summary of its key findings and recommendations: 🔍𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 #AgriPV allows 𝗱𝘂𝗮𝗹 𝗹𝗮𝗻𝗱 𝘂𝘀𝗲—simultaneous solar power generation and crop cultivation. It has potential to improve land-use efficiency, boost renewable energy, and support climate goals in India. 🌿 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 & 𝗕𝗶𝗼𝗱𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗜𝗺𝗽𝗮𝗰𝘁 No mandatory ESIA (Environmental & Social Impact Assessment) currently exists for AgriPV in India, raising concerns in ecologically sensitive areas. 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: Shading effects that reduce crop yields. Risks to soil quality, biodiversity, and stormwater management. Possible chemical contamination from panel maintenance. Positive outcomes may include erosion control, reduced irrigation needs, and cooler microclimates for crops. 💰 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗠𝗼𝗱𝗲𝗹𝘀 & 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝘍𝘰𝘶𝘳 𝘮𝘰𝘥𝘦𝘭𝘴 𝘸𝘦𝘳𝘦 𝘢𝘴𝘴𝘦𝘴𝘴𝘦𝘥: 𝗙𝗮𝗿𝗺𝗲𝗿 𝗢𝘄𝗻𝗲𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗢𝘄𝗻𝗲𝗱 𝗝𝗼𝗶𝗻𝘁 𝗩𝗲𝗻𝘁𝘂𝗿𝗲 𝗕𝗲𝗵𝗶𝗻𝗱-𝘁𝗵𝗲-𝗠𝗲𝘁𝗲𝗿 (𝗕𝘁𝗠) – 𝘧𝘰𝘶𝘯𝘥 𝘵𝘰 𝘣𝘦 𝘮𝘰𝘴𝘵 𝘷𝘪𝘢𝘣𝘭𝘦 𝘧𝘰𝘳 𝘭𝘢𝘳𝘨𝘦𝘳 𝘰𝘱𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘴 𝘭𝘪𝘬𝘦 𝘧𝘰𝘰𝘥 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 𝘶𝘯𝘪𝘵𝘴. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: High capital costs Low solar feed-in tariffs Grid interconnection limits for small farms (min 500 kW capacity) Limited access to affordable finance 📊 Multidimensional Feasibility Assessment Mixed expert views on technological and policy feasibility Low awareness among farmers limits social feasibility Ecological feasibility is possible but requires careful planning Financial and economic feasibility hinges on improved tariffs, incentives, and innovative revenue sources (e.g., D-RECs, carbon credits) ✅ Recommendations Policy Reforms: Introduce 𝗺𝗮𝗻𝗱𝗮𝘁𝗼𝗿𝘆 𝗘𝗦𝗜𝗔 Zoning regulations to protect ecosystems Grid reforms to include smallholder farmers Financial Support: Higher solar feed-in tariffs Incentives like D-RECs and low-interest loans Farmer Education: Programs to raise awareness and promote AgriPV benefits Showcase BtM model as an example for income diversification Research & Design: Identify shade-tolerant crops Optimize panel design to minimize crop yield loss 🧩 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 #AgriPV holds transformative potential for sustainable farming and energy production in India. However, regulatory support, targeted investments, and farmer-centric programs are crucial for widespread adoption, especially for small and marginal farmers. #AgriPV #SolarFarming #RenewableEnergy #SustainableAgriculture #GreenEnergyIndia #DualUseLand

Many farmers ask "Mere area mein polyhouse banana sahi rahega kya?" Continuation from my last post - https://lnkd.in/gRSdSTbw Let's simplify it Step 1: Location & GPS Marking - Note the village name, taluka, district. - Mark GPS coordinates (for sunlight angle, solar radiation planning, subsidy application, etc.). Step 2: Study the Climate Collect temperature, rainfall, humidity, and wind data for the area (preferably last 1–2 years). The goal is to understand: - Is the summer too hot? (Above 40–45°C needs cooling measures) - Is the winter too cold? (Below 7–8°C may need heating or protection) - Is the humidity very high or low? - Are there frequent storms or strong winds? Step 3: Test the Water Quality Take a sample of the available borewell, canal, or tank water and test it. - Check pH (ideal is near 7), EC (electrical conductivity), TDS, sodium, and chloride levels. - If water is too salty or hard, growing crops like capsicum or gerbera will be risky unless water is treated. Step 4: Test the Soil Collect soil from the top 15 cm and get it tested for: - pH level (avoid soils that are too alkaline) - Organic carbon content - Nutrients like nitrogen, phosphorus, potassium, and micronutrients - Texture (loamy or sandy loam is good) - Check if the land has waterlogging problems. Poor drainage can kill plants in polyhouse. Step 5: Check Sunlight Availability Visit the site during different times of day and check for shadows. - Make sure there are no big trees, buildings, or hills blocking sunlight. - The polyhouse should get at least 5–6 hours of direct sunlight daily. It can vary depending on crop. Step 6: Evaluate Market Access - How far is the nearest city, mandi, hotel, or bulk buyer? - Is there transport available for quick delivery? - Will you be able to sell exotic vegetables, flowers, or herbs easily? Step 7: Check Infrastructure and Labour - Is electricity available nearby for running fans, foggers, or drip system? - Are there roads for trucks to come and go? - Is there skilled or semi-skilled labour available locally for polyhouse work? Step 8: Check Subsidy and Support Availability - Visit your district horticulture department or Krishi Vigyan Kendra. - Find out if your area is eligible for NHM, MIDH, or state polyhouse subsidies. - Check if banks nearby offer agri-loans or linked schemes. Step 9: Identify Risk Factors - Is the area prone to floods or heavy winds? - Are monkeys, wild boars, or other animals a threat? - Is there a risk of theft or vandalism? - Is the location safe for long-term investment? And finally talk to an expert to get realistic views. #PolyhouseFarming #GreenhouseFarmingIndia #ProtectedCultivation #AgriConsultant #FarmersOfIndia #KarnatakaFarming #AgriBusiness #SubsidyAwareness #AgriSupport

Here's a step-by-step guide with examples on how to conduct a requirement feasibility analysis: Step 1: Gather Requirements Example: Let's assume you are working on a software development project to build a new mobile application. Some of the requirements might include features like user registration, real-time chat functionality, location-based services, and compatibility with iOS and Android devices. Step 2: Identify Feasibility Criteria Determine the feasibility criteria that will be used to evaluate each requirement. Example: For the mobile application project, the feasibility criteria might include: Technical Feasibility: Can the required features be implemented with the available technology and resources? Economic Feasibility: Is the project financially viable within the allocated budget? Legal and Regulatory Compliance: Does the app comply with privacy laws and other relevant regulations? Operational Feasibility: Will the app be usable and manageable by the target users and administrators? Scheduling Feasibility: Can the project be completed within the desired timeframe? Step 3: Evaluate Feasibility for Each Requirement Assess each requirement against the identified feasibility criteria. You can use a scale (e.g., high, medium, low) or a numeric value (e.g., 1 to 5) to indicate the feasibility level. Example: Requirement: Real-time chat functionality Technical Feasibility: High (The technology for real-time communication is readily available). Economic Feasibility: Medium (Additional server infrastructure may be required for scalability). Legal and Regulatory Compliance: Medium (Ensure data privacy regulations are followed). Operational Feasibility: High (The chat feature is familiar and intuitive for users). Scheduling Feasibility: Medium (The chat functionality may require extra development time). Step 4: Analyze Feasibility Results Review the feasibility assessments for each requirement. Identify potential risks and constraints that might impact the project's success. Example: From the analysis, it becomes evident that most requirements have a high feasibility rating, but the economic feasibility for additional server infrastructure could be a concern. Additionally, the scheduling feasibility for implementing the chat functionality may require adjustments to the project timeline. Step 5: Make Recommendations Based on the feasibility analysis, make informed recommendations to the project stakeholders. Example: Recommendations: 1. Proceed with the development of the mobile application as most requirements are highly feasible. 2. Conduct a detailed cost-benefit analysis to evaluate the economic impact of the additional server infrastructure required for real-time chat. 3. Work closely with legal experts to ensure compliance with data privacy regulations for the chat feature. BA Helpline #businessanalysis #businessanalyst #businessanalysts #requirements #feasibility #ba

The aviation industry faces the challenge of lowering emissions while meeting growing demand. Sustainable Aviation Fuel (SAF) and e-SAF play a critical role in solving this challenge. However, despite their potential, policy and market barriers are slowing their deployment. As part of the Project SkyPower initiative, we at Topsoe are working alongside industry leaders to fast-track e-SAF production. A recent analysis highlights five key policy areas that must be addressed to unlock scale-up in Europe. 1️⃣ Current uncertainty around policy incentives and market demand prevents investors from committing capital. Stable, long-term policies—such as binding e-SAF mandates—are needed to de-risk projects and enable financing. 2️⃣ Today, e-SAF remains 3–5 times more expensive than fossil jet fuel, making policy-driven price stabilization essential. Aligning energy and aviation policies will create viable business models and enable competitive scaling. 3️⃣ Complex approval processes for renewable energy, green hydrogen and CO₂ utilization projects are delaying deployment. Clear, standardized certification pathways can accelerate time-to-market for e-SAF technologies. 4️⃣ Europe needs significant expansion in electrolyzer capacity and CO₂ capture to support e-SAF production. Policies should prioritize access to affordable, additional renewable electricity for power-to-liquid fuel synthesis. 5️⃣ A fragmented regulatory landscape across EU member states risks creating inefficiencies and uncertainty. A unified EU-wide sustainability framework for e-SAF will ensure consistent incentives and facilitate trade. With the right policies, a strong position can be built within e-SAF, strengthening diversification of energy sources, dependencies, industrial competitiveness and energy security. At Topsoe, we are committed to developing the technologies and solutions that will propel large-scale SAF and e-SAF production. Read more here: https://lnkd.in/dB2--dsu

#Feasibility_Study Steps: From Concept to Implementation 1. Initiating the Feasibility Study Determine the need and scope of the study to establish clear objectives. 2. Research and Concept Development Conduct background research by reviewing previous studies and projects. Develop initial concepts, parameters, and study frameworks. 3. Team Formation and Public Engagement Form an internal Project Development Team (PDT) and an External Advisory Group (EAG) to oversee the study. Hold a public meeting to gather input on proposed concepts and ideas. 4. Engineering Analysis and Report Drafting Perform an in-depth engineering analysis, including model development. Document findings in a draft report. 5. Review and Public Consultation The PDT and EAG review the draft report. A second public meeting is held to gather additional feedback. 6. Refinement and Finalization Refine concepts based on feedback. Prepare a final report and have it reviewed by the PDT and EAG before official presentation. 7. Implementation and Execution Once funding sources are secured, the proposed improvements are designed and constructed, marking the completion of the feasibility study process. ⸻ Why Is This Process Important? A structured feasibility study ensures that decisions are based on thorough research, expert analysis, and public input. It helps organizations: 🔷 Minimize risks 🔷 Allocate resources efficiently 🔷 Improve project success rates Key Tips for a Successful Feasibility Study 1- Clearly define objectives and scope from the start 2- Conduct comprehensive research and gather accurate data 3- Engage key stakeholders and encourage public participation 4- Regularly review and refine concepts based on feedback 5- Ensure financial viability and explore funding opportunities 6- Use the final report as a roadmap for implementation

🤔 As a generative AI practitioner, I spend a good chunk of time developing task-specific metrics for various tasks/domains and use-cases. Microsoft's AgentEval seems like a promising tool to assist with this! ❗ Traditional evaluation methods focus on generic and end-to-end success metrics, which don't always capture the nuanced performance needed for complex or domain specific tasks. This creates a gap in understanding how well these applications meet user needs and developer requirements. 💡 AgentEval provides a structured approach to evaluate the utility of LLM-powered applications through three key agents: 🤖 CriticAgent: Proposes a list of evaluation criteria based on the task description and pairs of successful and failed solutions. Example: For math problems, criteria might include efficiency and clarity of the solution. 🤖 QuantifierAgent: Quantifies how well a solution meets each criterion and returns a utility score. Example: For clarity in math problems, the quantification might range from "not clear" to "very clear." 🤖 VerifierAgent: Ensures the quality and robustness of the assessment criteria, verifying that they are essential, informative, and have high discriminative power. Turns out that AgentEval demonstrates robustness and effectiveness in two applications: math problem-solving and household tasks and it outperforms traditional methods by providing a comprehensive multi-dimensional assessment. I want to try this out soon, let me know if you've already used it and have some insights! #genai #llms

From Discovery to Mine Operation The journey from mineral discovery to the commissioning of a new mine is a multi-disciplinary and highly interconnected process. It requires technical precision, robust economic evaluation, and sustainable practices. 1. Mineral Discovery Regional geological mapping, geochemical surveys, and advanced geophysical techniques identify promising mineralized zones Structural geology and lithological interpretations refine target prioritization, leveraging modern data integration tools like ArcGIS and Leapfrog 2. Systematic Exploration Preliminary Studies: Surface sampling, trenching, and reconnaissance geophysics validate target potential. Drilling Programs: Core drilling delineates ore body geometry, grade distribution, and mineralogical associations. Comprehensive logging (lithological, geotechnical, and alteration) is vital for resource modeling Mineral Resource Estimation: Sophisticated 3D modeling and geostatistical analyses conform to industry standards (e.g., JORC, NI 43-101) to classify resources 3. Resource Evaluation and Feasibility Studies Technical Feasibility: Optimal mining methods (open-pit or underground) are selected based on ore body morphology, geotechnical stability, and hydrogeological conditions Metallurgical Test Work: Process optimization ensures efficient recovery of valuable minerals, addressing challenges like refractory ores or impurities. Economic Feasibility: Rigorous financial models incorporating CAPEX, OPEX, NPV, IRR, and sensitivity analyses guide investment decisions 4. Detailed Mine Design and Planning Geotechnical Engineering: Pit slope design, stope layouts, and ground support systems ensure operational safety and efficiency Mine Layout Optimization: Strategic placement of waste dumps, haul roads, and stockpiles minimizes costs and environmental impact Production Scheduling: Dynamic mine planning aligns resource extraction with processing capacity and market demand 5. Environmental, Social, and Governance (ESG) Considerations EIA: Biodiversity management, water conservation, and tailings storage design are integral to sustainable operations. Community Relations: Transparent stakeholder engagement fosters trust and ensures alignment with local socio-economic goals Regulatory Compliance: Adherence to international environmental and safety standards ensures project longevity 6. Mine Development and Construction Infrastructure development includes road networks, power supply, water management systems, and processing plants Pre-production trials optimize mining and processing workflows to achieve steady-state operations. 7. Operational Readiness Initial production phases focus on achieving design throughput and maintaining grade control. 8. Risk Management and Future-Proofing Comprehensive risk assessments address geological uncertainties, operational disruptions, and price volatility. #Geology #MineralExploration #FeasibilityStudies #MineDevelopment #Mining

The gold deportment test is a sophisticated mineralogical and metallurgical tool used to determine the most efficient extraction process for gold ores, particularly as mining projects transition from exploration to the design and construction phase of a gold processing plant. This test provides comprehensive data on the distribution, mode of occurrence, and liberation of gold within the ore matrix, including whether it is free-milling, refractory, or associated with base metals or gangue minerals. Understanding gold deportment is essential for designing a tailored process flow sheet that optimizes gold recovery and operational efficiency. The deportment test involves a series of detailed investigations, including quantitative mineralogical analysis using automated SEM-EDS systems (such as QEMSCAN or MLA), which identifies gold associations with specific minerals, grain size distribution, and texture. Grindability tests, combined with gravity separation and cyanide leaching assays, help establish the ore’s amenability to gravity recovery and cyanidation. For ores exhibiting refractory behavior, where gold is locked within sulfide minerals (e.g., pyrite, arsenopyrite), further diagnostic leaching and pre-treatment tests—such as pressure oxidation (POX), bio-oxidation (BIOX), or roasting—are necessary to liberate encapsulated gold. Gold deportment test work plays a critical role in determining the most appropriate processing method for each ore body, providing the data needed to design circuits such as carbon-in-leach (CIL), carbon-in-pulp (CIP), or heap leaching. For operations advancing from the exploration phase to plant construction, this test is vital to minimize operational risks by identifying the most efficient metallurgical process before committing to full-scale plant construction. Once a plant is operational, gold deportment testing continues to be valuable for process optimization. Continuous ore characterization allows operators to adjust plant parameters in real-time, responding to variations in ore feed characteristics, including mineralogical complexity, grade, and sulfide content. For polymetallic or complex ores, where gold may be associated with copper or other metals, deportment testing informs necessary modifications in the flotation and leaching circuits. In conclusion, the gold deportment test is indispensable for metallurgical optimization. It provides the scientific foundation for decision-making during the design, construction, and operational phases of a gold processing plant. By understanding how gold behaves in different mineral matrices, mining companies can fine-tune plant operations, ensuring maximum recovery and long-term profitability. #GoldDeportment #MetallurgicalTestwork #RefractoryGold #CIL #CIP #POX #BIOX #HeapLeaching #GoldProcessing #MineralProcessing #ProcessOptimization #DiagnosticLeaching #Flotation #MiningTechnology #GoldRecovery #PlantDesign #SustainableMining #MiningExploration #MineralAnalysis