Explain Use Cases

Our project’s use cases highlight collaborative efforts and innovative solutions in the field of explainable artificial intelligence. Each case tackles specific challenges within this domain, aiming to enhance transparency and understanding in AI systems. Through partnerships and advanced technologies, we’re paving the way for practical and transparent AI solutions. Explore our use cases to see how we’re shaping the future of explainable artificial intelligence in industry

Use Case 1.1 Energy Price Prediction

Powerplant facilities are constantly faced with ramp up or ramp down decisions due to prioritized renewable energies. Future electricity prices must be available several days ahead, as coal power plants are inert. Electricity prices fluctuate due to geopolitics, unplanned outages and increasing share of renewables. Competitors offer just blank predictions without any model description or explaniability.


Use Case 1.2 Operator Support / Anomaly Detection

This use case aims providing more robust and comprehensive anomaly detection systems for plant operators, with the help of interactive XAI-based anomaly explainers.

Use Case 2.1 Maintenance Ticket Analysis

This use case focuses on operators who are responsible for searching and identifying Technical Reports from all parts of the metal manufacturing plants. A semantic analyzer engine allows “Technical Reports (TR)” to be analyzed for their meaning and not just for the presence of certain keywords. By using large language models, we can improve the accuracy of the results and provide more relevant information to Users.

Use Case 3.1 Use Life Estimation

To guarantee high quality products, electronics manufacturing contains many inspection steps like after PCB assembly (using machines from MEK) or after the final assembly of the product.

The relationship between the lifetime of electronics and the visual inspection data obtained during production is unknown and cannot be found using these existing methods.

Use Case 3.2 Visual Product Inspection on Electronics

To guarantee high quality products, electronics manufacturing contains many inspection steps like after PCB assembly (using machines from MEK) or after the final assembly of the product.The inspection types include checks for presence, alignment, or damage.

Some are performed manually or by algorithms that do not use deep learning, which results in slip of non-conforming products, high false positive rates, and lowered traceability of defects.

Use Case 4.1 Flotation

During the flotation process, the mined ore is separated into different minerals using a series of flotation cells and chemical detergents. Enhanced and interpretable forecasting enables operators to proactively control the process and improve the production of valuable minerals, leading to a direct economic benefit.

Use Case 5.1 Vibration Monitoring

Södra currently has hundreds of vibration sensors deployed. There is potential value in predicting failures or irregularity based on the vibration monitoring data (especially given Viking’s expertise in the domain). In addition to a simple prediction, understanding the correlation between various factors could enable maintenance professional to identify patterns and foresee potential challenges.  

Use Case 5.2 Kappa Sensor

The Kappa value is a crucial quality indicator of the pulping process and greatly influences the subsequent paper quality. Improved and explainable forecasting enables operators to predict deviations in advance and make proactive adjustments to maintain a stable high quality of Kappa.

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