2. METHODOLOGICAL FRAMEWORK: PROCESS OPTIMISATION + LCA AS A META-ANALYSIS Process Optimisation (PO) and Life Cycle Assessment (LCA) draw on the same operational data but historically have been applied with different depth in mineral processing. PO is widely embedded in plant-level decision making, while LCA has often remained a reporting tool. The framework presented here reinterprets routine PO data under an environmental lens, producing a decision-oriented carbon metric that complements process KPIs without adding modelling. 2.1 Overview of Process Optimisation (PO) PO follows an iterative sequence that includes defining the decision context, collecting and validating operational data, calibrating models, simulating scenarios, and translating results into actionable operational guidance. This approach is unit operation agnostic and applies across comminution and flotation as long as activities remain traceable within defined plant battery limits. Typical PO datasets include throughput and mass balances, grind size, solids content, circulating loads, energy use by stage, reagent additions, grinding media and liner wear, runtime, availability, recovery, and product specifications. Scenario analysis usually compares changes in energy use, recovery, grade, throughput, and wear-related effects to evaluate trade-offs. 2.2 What LCA Adds in an Operational Context When integrated into concentrator optimisation, LCA reframes core operational data—particularly electricity use and key consumables—as sustainability indicators that inform operational decisions rather than broad corporate reporting. System boundaries A gate-to-gate boundary aligned with concentrator battery limits is adopted. Inputs such as electricity, fuels, grinding media, liners, and reagents are represented with cradleto-gate factors. This intermediate boundary is consistent with ISO 14040/14044 and can be extended upstream or downstream. Functional Unit (FU) The FU must align with the PO decision question to ensure results remain interpretable. Examples include kg CO₂e/t ore processed for throughput-focused evaluations, kg CO₂e/kg metal in product for recovery-driven questions, and kg CO₂e/t product for comparing concentrate specifications. Inventory LCA uses many of the same variables that PO already tracks, such as electricity use, reagent dosages, and operating hours, and introduces key parameters including fuels, grinding media, and liner wear to complete the inventory. These are mapped directly to cradle-to-gate environmental factors, enabling a shared dataset between PO and LCA. Thus, LCA does not change PO workflows; it simply reinterprets familiar variables using a functional unit aligned with the decision context.
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