The Battle to Maintain Operational Margins with Recession Looming

Recent Slowdown in Global Commodity Markets Demands Attention 

The global mining industry has a history of facing significant challenges year after year, among them are falling commodity prices, declining global demand and currently an increase in downside risks, with, as an example, the worth of iron ore contracts in Singapore having fallen by more than 46 per cent from its most up-to-date peak in March’22.

A recent warning about the slowdown in global commodity markets demands attention as the threat of a recession in Europe and the US, along with a property crisis in China, threatens  the demand for iron ore. Rio Tinto said in its update last Tuesday that commodity prices are likely to continue falling as “downside risks to demand” arise.

Moreover, many mining C-level Executives are concerned about the operational costs incurred by their mineral processing plants. Maintaining profitable margins while the running of processes such as grinding circuits, thickeners, heap leaching, and flotation profitably are becoming more and more challenging with the recent reagent and energy cost escalations, not to mention the industry-wide drive towards sustainability – which requires even more CAPEX investment.

Mining companies are forced to regulate spending and to maintain strict levels of operational discipline to remain profitable. To stay in the game, mining companies need to become leaner, stronger, and more innovative. Technological advances like Artificial Intelligence (AI) and Machine Learning (ML) will now become crucial to drive operational efficiencies and offer insights that can help identify bottlenecks and inefficiencies.

Arnot Coal Powerstation

Remaining Efficient and Protecting Margins

AI and cloud solutions now allow the capture and use of huge volumes of process data. Simulation tools allow mining companies to analyze their processes well in advance in a virtual environment and to compare operational decisions using ‘what if’ scenarios, reducing the time and cost of prospecting operations, exploration and mining.

At it is our mission to enable the mining industry to make informed decisions based on individual requirements. Our AI-driven applications are not closed  ‘black-box’ solutions. They are able to offer great flexibility in choice of optimization, allowing youto introduce threshold points according to your desired target. Thus, you can choose to optimise in accordance with  whatever approach suits you at that point in time (and change it over time as your circumstances change).

Here are a few examples of outcomes that can be targeted.

Targeted Outcomes Based on Requirements:

  • If you choose to reduce energy costs in grinding, you can optimize for this by allowing more friable ores to enter the circuit.


  • If you choose to improve metal recovery, you can optimize to receive a blend with fewer contaminants.


  • You can opt to optimally reduce CO2 emissions or water usage.

Our Scientific AI* approach further helps you to enrich and incorporate your vast amounts of available data into the decision-making process. In this way, it is possible to use historical (and current) process data to give subsidies for ML models to find and learn missing behaviors, providing important inputs to the physical models themselves.

In a manner similar to how operational personnel make decisions that span process units, our Optimization Applications draw on data from the mine through to the final product and enable proper value chain optimization. It “learns” and can predict what influence the feed material properties have on each process step, which means that it can proactively provide the automated control systems of each subsequent process (e.g. grinding and flotation) with the setpoints and limits it would need to achieve optimal operational efficiency.

In challenging times such as these where we face a global recession,  it is essential to have access to  process optimisation solutions that are fast to implement and that relieve the pain quickly and with low risk. process optimization apps are ‘out-of -the-box’ products that can be up and running in weeks avoiding the need for lengthy and risky in-house software builds.


*Scientific AI is the fusion of physics models with machine learning techniques. It was developed by to further increase net metal production through the use of our AI solutions.


Digitalization is key in achieving operational excellence, especially in challenging markets such as mining. Modern AI-based applications can successively support the decision-making process, in a fast, informed and fact-based manner, and create entirely new opportunities for continuous optimization of mining operations/plants throughout their life cycle. 

The ability to have control over your operations, whether to increase or decrease parameters for optimization, has become an imperative business procedure to reduce operating cost and protect expected margins. By doing so, miners can make decisions that make the most sense for their business based on the current economic climate.


The Application Portfolio

Mine to Market: Value Chain Optimization

The Stockpile & Inventory Optimization Application is one of a suite of real-time decision-making applications that uses AI to optimize each process; from mine to market.

Our Material Handling model connects these applications to drive even greater efficiency.

Our process optimization apps can be deployed on a specific process bottleneck or expanded across the entire value chain. They are powered by our Industrial AI Decision Intelligence Platform,

Stockpile and Inventory Optimization

Stockpile & Inventory Optimization

Grinding Optimization App

Grinding Optimization

Thickener Circuit Optimization

Thickener Optimization

Flotation Circuit

Flotation Optimization

Solvent Extraction Optimization App

Solvent Extraction (SX) Optimization

Leaching Optimization App

Leaching Optimization