Autoclave reactions are sensitive to several parameters, particularly temperature, overpressure, and reactant concentrations. This talk will cover how to quantify autoclave reaction tradeoffs by controlling these parameters. The talk will explore how to use mass and energy balance analysis to determine how sensitive autoclave yields are. Even small process variations can have big impacts on reaction yield.

This talk looks at provides an environmental evaluation of making copper.

This paper presents a computational fluid dynamics (CFD) based model for predicting slurry flow-induced erosion, a common issue in mineral processing operations. Unlike traditional methods that require tracking individual solid particles, this model adopts a continuum approach, significantly enhancing computational efficiency. The key advantages of the model are accurately predicts erosion patterns in good agreement with experimental data from previous CSIRO studies. In addition, enabling rapid erosion prediction—up to 100 times faster than conventional particle-tracking methods and simple to integrate into existing CFD software frameworks will be presented.

The electrolytic refining of raw copper allows large-scale access to high-purity copper, which is required in the electrical and electronics industry. During this process, bismuth and antimony dissolve in the electrolyte and are efficiently removed by Lewatit MDS TP 260, an ion exchange resin from the German specialty chemicals group, Lanxess. Lewatit MDS TP 260 has proven itself in this application on an industrial scale, not only in Australia, but also already in Japan and Spain. Even in the presence of more than a hundredfold excess copper, large quantities of bismuth and antimony can be reliably separated from the electrolyte.

A bespoke high-fidelity model was developed of the sulphurous acid and precipitation circuits used for selenium removal from copper solution at a base metal refinery. The purpose of the developed transient model was to mimic the behaviour and variable performance of the existing plant, and to support its upcoming upgrade. A hybrid modelling approach, integrating first-principles and data-driven methods, was used to accurately capture Se/Te and Cu/Ni behaviour. These calibrated algorithms were embedded in an internally consistent manner in the proposed upgraded plant configuration. The resulting model was used to validate the new design, evaluate detailed “what-if” scenarios, and provide a data-driven basis for de-risking the investment.

Australia is rich in critical minerals but high economic costs challenge domestic processing. Strategic investment in R&D offers a pathway to overcome these barriers by delivering innovative, cost-effective, and sustainable processing technologies that can transform Australia’s position in global supply chains. A critical element of these activities is close collaboration with commercial partners to ensure R&D efforts are directed toward processes that are economically viable and commercially implementable. This presentation outlines several hydrometallurgy-based processing technologies currently under development, including unlocking copper from chalcopyrite, production of battery-grade manganese sulphate and recovery of vanadium and titanium from vanadium titanomagnetites.

The design of transfer chutes in bulk material handling, especially for iron ore, is crucial for operational efficiency, equipment reliability, and safety. Poor designs cause spillage, excessive wear, blockages, reduced throughput, increased costs, and delays. Computational tools such as Discrete Element Method (DEM) modelling, supported by flow property testing, have proven valuable in mitigating various issues related to complex material handling systems. However, DEM can be computationally demanding, particularly when simulating large volumes of particles or complex transfer chute geometries at high throughputs typical of iron ore systems. When simulations are complex or need external validation, physical modelling offers a complementary approach. Full-scale physical testing with real ore is often impractical due to cost and handling issues. Scaled models using surrogate materials with similar flow behaviours are commonly used to bridge virtual design and real-world performance. In this study, Physical Scale Modelling (PSM) uses a 1/10th-scale model and surrogate material, calibrated by matching the Froude number for dynamic similarity. The focus is on how Froude-based calibration affects flow behaviour and material surcharge profiles on conveyors, ensuring the model replicates real flow patterns and trajectories. Flow property tests on both surrogate and actual iron ore determine key parameters like cohesion, friction, and flowability, supporting calibration validation. The PSM results with the surrogate are compared to DEM outcomes with calibrated iron ore properties, providing insights into calibration accuracy.

A proprietary process produces hydrogen sulfide (H2S) with over 99.9 wt% purity and sodium hydrosulfide (NaSH) using hydrogen and sulfur with a durable, high-performance catalyst. Its simple and reliable design minimizes impurities and eliminates sulfur residue, ensuring stable production, with capacity ranging from 500 TPA to 15,000 TPA. Applications include engineering plastics, DL-methionine synthesis, and hydrometallurgical refining of non-ferrous metals, especially for recycling valuable metals from used lithium-ion batteries. To meet growing global demand for nickel and cobalt recovery from used lithium-ion batteries, we offer modular H2S/NaSH units with capacity of 500 TPA, enabling cost savings and shorter delivery times.

This presentation explores the new principles governing what is possible through gravity separation, including the potential to produce ultra-high grade iron ore in a single stage. Ores that fail to liberate in full still invite significant mass rejection, reducing the scale of the next phase of processing. Then the key to the next phase is comminution, supported by highly efficient classification with no by-pass, to achieve full liberation. The GradePROTM delivers efficient classification, overcoming the inefficiencies of cyclones in comminution circuits. The pre-concentration, and narrower size range, permits low processing rates in a second gravity stage. The GradePROTM then delivers further significant mass rejection, and even higher mineral upgrades and recoveries in the final phase.

The Ramu nickel project commenced construction in 2008—commissioning in 2012—and has been in operation for over a decade with much of that time at levels above design nameplate. The project has consistently operated at the lower end of the cost curve for HPAL operations and provided positive cashflows to its owners as well as substantial benefits to other stakeholders. The paper will provide an overview of the unique features of the Ramu Nickel project, achievements of the first decade of operations, recent improvements the project has made, and what can be expected for the next decade.

If sea nodules are to become a major source of nickel, they will also produce enough manganese to disrupt the global manganese market, assuming the manganese is not discarded. In HPAL and RKEF processing of sea nodules the manganese becomes an oxide or slag that is said to be suitable for making FeMn or SiMn. The oxide/slag would then compete with established land based Mn ores such as pyrolusite. This paper presents a route for recovering nickel and cobalt as MSP and manganese metal from sea nodules. The Mn metal would go directly to the Mn steel alloy industry.

The production of battery-grade nickel sulphate involves key trade-offs between Capital Expenditure (CapEx), Operational Expenditure (OpEx), and operational flexibility, primarily influenced by the chosen process technology and purity requirements. Crystallisation is the most important final purification step in producing the high purity battery grade products. This paper describes crystalliser design features to produce battery grade nickel sulphate hexahydrate product, and will compare the operating conditions, crystalliser types, preferred energy source, control of product purity, and trade-off between capital and operating costs.

Generative artificial intelligence (AI) is transforming how metallurgists analyse data, design process models, and communicate complex ideas in copper operations. By combining metallurgical expertise with generative tools, engineers can rapidly prototype dynamic models, automate repetitive analytical tasks, and communicate technical insights more effectively. Applications include dynamic modelling of tanks in series, where AI-generated simulations allow faster testing of process behaviour and control strategies; AI-assisted scripting for mass balances, which enables quick creation and adjustment of process models to evaluate circuit changes and design options; and AI-enhanced communication, where complex metallurgical data are translated into clear visual and written formats for plant teams and decision-makers. Together, these approaches show how AI can act as a collaborator—accelerating technical work, supporting creative problem-solving, and improving knowledge sharing across disciplines, while maintaining data integrity and professional judgement at the centre of metallurgical practice.