Calculate Embedded Emissions for Unwrought Aluminium (HS7601)

Enter your input

Payable emissions (tCO₂e/t)

Total CBAM Cost (€)

Notes:
There may be a difference when calculating the price with respect to import volume, carbon price, and benchmark emissions, as the embedded formula may result in minor variations due to decimal rounding. Therefore, the actual value may vary.

CBAM is applicable to trade volumes starting from 50 metric tonnes. For trade volumes below 50 metric tonnes, CBAM does not apply.

Usage Procedure – How to use the CBAM Calculator Sheet

Enter or update values only in the INPUT
PARAMETERS section (Highlighted in blue) , including
the carbon price, benchmark emissions, CBAM
chargeable percentage (as per the phase-in year),
and imported quantity.

The system will automatically calculate the payable
emissions and the total CBAM cost (€) based on the
inputs provided.

Notes:
• Change any input value to automatically update CBAM cost.
• Formula used: Carbon price × payable emissions × quantity.
• Model aligned with CBAM supplier-side illustrative methodology.

04 AUGUST 2026 AL CIRCLE

Alba’s AI leap: New turbine monitoring system to enhance plant efficiency

EDITED BY : NILANJANA BANERJEE 2MINS READ

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The image has been obtained from https://www.albasmelter.com/en/category/media-kit

Aluminium Bahrain (Alba) has collaborated with the Nasser Centre for Science and Technology (NCST) to deploy an artificial intelligence (AI)- based monitoring system for gas turbines at Power Station 4. The initiative aims to improve predictive maintenance capabilities as well as operational efficiency.

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Developed by NCST’s AI research and development team, the platform would be continuously monitoring the GT-51, GT-52, GT-61 and GT-62 gas turbines, offering advanced analytics to help minimise the risk of unexpected equipment failures while backing faster, data-based maintenance choices.

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The AI system evaluates critical operating parameters such as compressor pressure and turbine blade cooling efficiency. Thus, engineers are able to identify early signs of compressor fouling or cooling port blockages before they impact performance. It also compares real-time operating data with AI-generated predictive models to detect deviations at an early stage.

The system features an automated alert mechanism to notify engineers whenever operating parameters exceed approved limits. The alerts provide details of the affected turbine, expected and actual readings, deviation percentages and severity levels. It enables quicker intervention, reinforcing continuity in performance optimisation.

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Moreover, combining real-time operational data, historical performance records, AI analytics and interactive data visualisation, the platform builds a secure monitoring environment.

This paves the way for continuous assessment of turbine health, trend analysis, comparison between actual and expected performance, and identification of thermal imbalances or other operational discrepancies.

The project highlights the collaborative efforts between NCST and Alba to boost digital transformation in Bahrain’s aluminium industry through AI and such innovative initiatives with technological advancements.

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