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Increasing productivity in heavy industry processing plants with advanced analytics

Processing plants are complex, with intricate, non-linear interactions across thousands of variables and multiple trade-offs to solve for. Operating decisions are typically made through process control tools solving for a local optimization, or are based on an individual operator’s experience, which can vary significantly. Amidst this complexity, technology generates enormous amounts of data across the value chain. When used to its full potential, data can drive productivity improvement and help plants achieve optimal results.
Capturing value from advanced analytics requires multiple skillsets, such as data science, process control and metallurgy, IT, change management, and capability building. OptimusAI leverages a full portfolio of digital solutions that combines science, data, and experts to help processing plants in heavy industries make data-driven decisions to improve profit per hour.

Video

Revolutionizing processing plant optimization

What if artificial intelligence could take data from processing plants and use it to optimize the plant in real-time?

4-5%

EBITDA uplift by improving productivity of industrial processing plants

>$4B

impact created for OptimusAI clients

Our approach

Our tried and tested approach to advanced analytics brings together a robust delivery process with a collaborative team of specialists—both supported by proprietary technology and products.

People

We bring a mix of data science, process control, operations, and design expertise, with hands-on experience in applying advanced analytics to improve processing.

Process

With our focus on processing, we have developed a standard recipe for deploying high-impact solutions efficiently and sustainably. We help organizations move quickly to capture impact while building capabilities for long-term success.

Technology

Our proprietary capability develops control room advisor systems to improve processing performance. OptimusAI uses state-of-the-art data science to optimize performance in the context of thousands of variables, and includes an easy-to-use interface to process recommendations.

Examples of our work

Leveraging AI to improve productivity of a copper concentration plant

A major copper concentrate producer was struggling to run grinding mills at its ideal capacity and to achieve top-decile recovery performance. The challenges were driven by the plant’s complex operations, where thousands of sensors were fed to a control system to solve for non-linear functions and trade-offs. We created a custom-built artificial intelligence model–loaded with three years of operating data–to identify operational tweaks that would boost copper production at consistent quality. As a result, the plant improved throughput by a range of 10 to 15 percent, and copper recovery in a range of two to four percentage points.

Launching a tech-enabled manufacturing transformation at a biochemicals plant network

A leader in biochemicals aspired to embed digital and analytics in its way of working. Together, we launched a tech-enabled manufacturing transformation, initiated with the deployment of five global centers of excellence and sustained by training the majority of the practitioners. Through a series of agile sprints, we developed advanced analytics models to identify the most important drivers for process variability. Automated data pipelines and interactive dashboards enabled embedding this data-driven decision making into daily operations. As a result, operators were able to reduce process variability and to increase throughput by 10 to 15 percent in bottlenecked sites.

Optimizing advanced process controls to improve throughput and yield at a pulp and paper mill

A large North American pulp mill operation was only achieving 60 percent of its target production at sub-optimal quality in final product. We leveraged AI to review the design of existing control strategy, including advanced process control (APC) loops, at key unit operations in the mill. It helped identify the gaps in the control design and operating philosophy. AI also helped in identifying the desired operating ranges—including set points and upper or lower tolerances—for key process variables in the APC in close relationship with subject matter experts. This resulted in a 15 percent increase in throughput and four percent increase in yield.

Leveraging machine learning to optimize performance for a refinery in Europe, the Middle East, and Africa

A leading European refining operations company was aspiring to improve one of its top-performing assets using state-of-the art analytics tools. The asset already had industry leading analytical tools in place such as unit level APCs, kinetic models, and site-level linear program optimizer.

We partnered with a global cloud computing leader to develop custom-built machine learning models and tools to improve prediction of quality specs, process indicators, constraints, and unit performance. These models served to create a global optimizer to minimize quality giveaway while maximizing throughput. It helped achieve a $0.3/bbl margin improvement (equivalent to five percent variable operating margin).

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