Introduction
Causal AI represents a paradigm shift in artificial intelligence, moving beyond traditional correlation-based machine learning to models that explicitly understand cause-and-effect relationships. Unlike conventional AI, which excels at pattern recognition but struggles with explaining decisions, Causal AI leverages structured knowledge and statistical reasoning to uncover the underlying drivers of complex industrial processes.
This capability is particularly valuable in manufacturing, supply chain management, energy optimization and predictive maintenance, where understanding causality can improve efficiency, reduce downtime and mitigate risks. By integrating causal reasoning into decision-making, industries can create more robust automation systems, optimize resource allocation and enhance operational resilience.
Make Better Decisions with Geminos Causeway
Geminos Causeway is a state-of-the-art Causal AI Decision Intelligence platform designed to help organizations build, deploy and operationalize causal models for enhanced decision-making. Powered by Judea Pearl’s DoCalculus and decades of research in probabilistic and causal reasoning, Causeway ensures AI-driven insights are explainable, unbiased and actionable.
1. Causal Models
- Capture subject matter expertise as an intuitive model, supported by Causeway’s Co-Pilot and Causal Discovery suite.
- Use these models to quantify direct and indirect causal relationships affecting key outcomes, providing a much better understanding of the overall process.
2. Counterfactual Simulation Tools
- Test "what-if" scenarios for changes in process parameters, enabling decision-makers to predict outcomes of interventions such as adjustments in asset operating temperatures or input quality/quantity.
- Simulations will help evaluate trade-offs between key outcomes such as energy consumption and overall production output.
3. Insightful Reports and Recommendations
- Detailed documentation of findings, including key drivers of key outcomes and actionable recommendations to optimize production processes.
4. Interactive Visualizations
- Analysis results are easily presented in common dashboarding tools such as PowerBI and Tableau.
- Decision-makers have a straightforward path to making better and more data-driven decisions, backed up by the causal models.
Industry Use Cases
A large manufacturer of metal was interested in making better decisions with regards to their electrolysis processes. Anodes are a key component of electrolysis; their quality has an impact on the overall production cost, energy consumption and environmental emissions of the process. Therefore, the anodes themselves have desired characteristics such as high density, low porosity/cracks, low electrical resistivity and low air/CO2 reactivities. The resistivity of the carbon anodes is particularly important, as it is directly related to the amount of energy required to perform electrolysis.
The Geminos team helped the manufacturer to build a causal model of the anode production process, with resistivity as the key outcome. As part of the model, process parameters such as coke granulometry, pitch percentage and mixer speed were included. Following analysis of the causal model alongside historical production data, the manufacturer was able to make better decisions with regards to these parameters. This resulted in a substantial improvement to anode resistivity – the average resistivity was much lower and there were far fewer outliers than before.
Causal AI: Transforming the Mining Industry
The mining industry is undergoing a profound shift, driven by the need to optimize production, improve safety and meet sustainability goals. Mining companies face mounting pressure to balance profitability with environmental and social responsibility while ensuring operational efficiency. Achieving these goals requires smarter decision-making, increased process visibility and the ability to predict and prevent failures before they occur.
Mining operations are highly complex, involving extraction, material processing, transportation and waste management. Operational inefficiencies, unexpected equipment failures and resource variability can lead to costly disruptions. While traditional AI helps identify patterns, it lacks the ability to determine true cause-and-effect relationships. Causal AI fills this gap by offering deeper insights into system failures, maintenance needs and resource optimization.
Benefits
By leveraging Causal AI, mining companies can:
- Enhance Equipment Reliability: Predict and prevent equipment failures by identifying root causes of breakdowns, improving uptime and reducing maintenance costs.
- Optimize Ore Processing: Understand the relationships between variables like ore composition, grinding efficiency and reagent usage to maximize yield and minimize waste.
- Improve Energy Efficiency: Reduce energy consumption by identifying operational factors contributing to inefficiencies, optimizing haulage and refining processing methods.
- Increase Safety & Risk Management: Identify causal factors behind accidents, improve hazard detection and implement targeted safety interventions.
- Optimize Supply Chain Logistics: Predict disruptions due to weather, transport delays, or resource shortages, enabling proactive decision-making.
- Reduce Environmental Impact: Monitor and mitigate emissions, optimize water usage and minimize tailings dam risks through causal analysis, enabling the move towards Net Zero.