Bakyrchik Mining – Polymetal wins “Best Project with use of AI” at Tech Garden Awards for their Flotation Project

IntelliSense.Lab is Delighted with Project and Customer Award Recognition


The project “Optimization of the flotation process using artificial intelligence technologies“, was conducted by “Bakyrchik Mining Enterprise LLP” owned by Polymetal,  and was initially awarded the nomination of “Best project with use of AI” by the Tech Garden Awards 2023.

The awards ceremony occurred at the Tech Garden Awards 2023 early this month during the Digital Almaty 2023 event with the competition comprising the major Kazakhstan mining and oil companies. We are delighted that the Bakyrchik Mining project was selected as the category winner. The award’s purpose is to promote successful examples of digital development of enterprises and to encourage the introduction of Industry 4.0 elements into production processes, under the appreciation of the Ministry of Digital Development, Innovation and Aerospace Industry of Kazakhstan.

The IntelliSense-LAB digital industry center is part of the Tech Garden hub for the development and implementation of Industry 4.0 technologies.  With the aim to be a technological platform for industrial automation and digitalization, is helping in the optimization of production processes by using Artificial Intelligence to reduce costs and increase productivity.

Flotation Process Challenges

In the mining industry, controlling flotation variables and stabilizing column cells have always been challenging aspects of plant operations, particularly for metal recovery. With the increasing pressure towards more efficient and sustainable processes (especially with water), and the constant increase in the cost of chemical inputs and energy, innovation in the field of real-time process control is under the spotlight.

In Flotation, achieving throughput, grade, and recovery targets are challenging as operators face a reactive and somewhat manual approach, often responding to the appearance of the froth, which is compounded by constant changes in feed properties.

In addition, the industrial flotation process is much more complex with a high degree of uncertainty requiring more than laboratory work to predict its behavior. Even modeling on longer timescales for design purposes is challenging while predicting the real-time operational performance of flotation is more difficult still.

At, we have developed world-class modeling approaches to alleviate mining process bottlenecks such as flotation control.

Using Artificial Intelligence for Flotation

The limited real-time visibility on circuits and low feedback on how to set control setpoints for optimal recovery threaten flotation performance. Another barrier to data-driven discovery is that existing machine learning methods often do not satisfy the needs of scientific models, since these models must also respect or incorporate the laws of physics as well as process and operation constraints.

In this way, analyze flotation behavior within a columnar cell using a method based on neural networks with: 

1) data measured directly
2) other data calculated with the process of hydrodynamics
3) physics equations phenomena, all embedded in the primary neural model. 

This modeling approach can be applied to analyze flotation columnar cell behavior and make predictions of grades or other variables in real-time. Our Application uses a method based on a machine learning (ML) process model combined with an explainable and real-time configurable optimization engine that amplifies metallurgists/operators’ process visibility also giving guidance on how to best operate the circuit.

Learn more about Flotation Optimization Application.

It’s Time to Digitalize

Projects like the ones being implemented at Polymetal can enable a higher metal recovery (1.5-3%) by increasing visibility, better predicting the material inputs (ore mineralogy and PSD) and operator guidance, helping in delivering OPEX savings, and reducing localized events by highlighting underperforming cells/columns. Some of the largest mines on the planet, across multiple commodities, have turned to to assist with their recovery optimization.

Stimulating digitization to improve business performance, and sustainability and reduce risk is part of’s core values. Through the use of Scientific AI*, we have built digital applications to facilitate decision-making on processes in one of the harshest environments. By doing so, our apps can drive process improvements, identify and remove bottlenecks, increase asset availability, reliability, and utilization for a single operating unit (like flotation, thickeners, stockpiles, grinding, etc) or work in tandem to increase overall mine-to-market efficiency.

*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

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