integrating carbon metrics does not require additional modelling layers. Instead, it reveals how existing decisions shift emissions between channels when evaluated against a consistent functional unit. 3.4 Illustrative Operational Examples of Carbon Responses The following simplified examples illustrate how common operating decisions affect both power demand and the distribution of carbon emissions across channels. Finer P80 targeting increases specific energy consumption and may also increase grinding media and liner consumption due to higher grinding duty. Aalthough the magnitude depends on mill type and operating conditions, these consumable-related emissions can become significant when evaluated alongside energy effects. Liner selection and maintenance also influence carbon response: liner material or profile and relining strategy alter charge trajectory, shifting throughput and specific energy across the liner life as documented in liner design references16. Embedded emissions from liner materials and relining frequency are rarely surfaced, but linking liner mass to cradle-to-gate factors makes them visible. Similarly, improved grind–flotation coupling—such as a moderately coarser primary grind combined with improved flotation kinetics—can reduce regrind demand and total steel consumption, lowering overall kg CO₂e per unit of metal produced despite similar kWh/t values, consistent with integrated grind–float studies.¹⁷ These examples are illustrative rather than site-specific, but they reflect mechanisms well established in mineral processing practice and reported in the literature. 3.5 Implications for Process Optimisation Practice By linking carbon emissions to engineering objects and operational variables, the carbon footprint becomes interpretable within standard process optimisation workflows. Instead of serving only as a reporting output, kg CO₂e can be evaluated alongside kWh/t, recovery, and cost using the same scenarios, datasets, and decision logic. This perspective allows practitioners to identify not only energy-efficient options but also configurations that minimise total carbon exposure as emissions shift toward consumables and services. The case study in Section 4 shows how these redistributions become visible when the integrated framework is applied to routine optimisation scenarios. This approach distinguishes the framework by treating carbon footprint as an operational response variable rather than a post hoc sustainability indicator. 4. ILLUSTRATIVE USE OF CARBON AS AN OPTIMISATION KPI, WITH INTEGRATION OF CONSUMABLES In this approach, the aim is not to provide a fully developed life cycle model, but to demonstrate how consumables can be incorporated into optimisation results and influence carbon-based decision signals. The analysis uses a generalised and anonymised dataset from an industrial grinding circuit optimisation study. Twelve operating scenarios were simulated, varying throughput and power draw while maintaining the same grinding media specification, liner configuration, and operating philosophy. Electricity consumption is taken directly from
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