Beyond Carbon Capture: Deloitte Middle East's AI Blueprint for Heavy Industry

How Deloitte Deploys AI for Industrial Carbon Capture

Daniel Gribbin, Director of Sustainability at Deloitte Middle East, works with steel, cement and oil and gas clients to cut emissions without sacrificing uptime or safety. The approach goes beyond installing capture units. AI sits across process control, maintenance and supply chains to remove waste, reduce energy intensity and uncover cost savings that stick.

The goal is what Gribbin calls "scalable and sustainable decarbonisation." For operations leaders, that means treating carbon reduction as a system-level optimisation problem, not a single project.

What this looks like on the plant floor

• Process efficiency: ML models tune setpoints in real time to smooth load, stabilise quality and lower energy use across kilns, furnaces and compressors.
• Predictive maintenance: Failure models forecast bearing wear, fouling and vibration anomalies so you schedule outages on your terms and keep efficiency curves tight.
• Supply chain improvements: Better demand forecasts and logistics routing cut idle time, inventory buffers and transport emissions.

These are practical levers that move both OPEX and emissions. They also build the data baseline needed for capture projects to perform to spec.

Technology stack Deloitte brings

• Digital twins and advanced analytics: A live model of the line to test scenarios, tune controls and benchmark shifts, products and sites.
• AI-enabled controls: Models embedded into DCS and MES for continuous optimisation.
• Blockchain traceability: Verifies origin and attributes in carbon markets and renewable energy trading, improving auditability.

This isn't technology for its own sake. It's a way to measure progress precisely, meet national targets and give investors confidence in reported results.

ESG that drives the P&L

Deloitte helps clients make ESG part of core planning and capital decisions. That includes reporting that fits international standards, access to green financing and meeting supplier expectations in global value chains.

Deloitte set near-term science-based targets in 2020 and added long-term targets in 2024. Its net zero by 2040 target has been validated by the Science Based Targets initiative. Learn more about SBTi's criteria at sciencebasedtargets.org.

AI for carbon capture-and the bigger emissions picture

Hard-to-abate sectors need capture, but performance depends on what happens upstream. AI reduces energy waste before flue gas reaches the capture unit, stabilises process conditions and lowers solvent degradation risks.

Deloitte also supports the business case: incentives, policy engagement, renewable sourcing and investment options. The result is a portfolio of measures where capture is one part of a balanced plan.

Hydrogen in the UAE: from roadmap to industry

The UAE's Hydrogen Leadership Roadmap sets an ambition to become a hub for low-carbon hydrogen by 2031, serving domestic decarbonisation and exports. Deloitte advises on project economics, certification, supply chains and cross-border partnerships so pilots move to commercial scale.

For context on the roadmap, see the UAE Ministry of Energy and Infrastructure's materials on hydrogen policy and initiatives: moei.gov.ae.

Progress across the Middle East

Deloitte Middle East has worked with public and private organisations to map Scope 1-3 emissions, set science-based targets and build governance that keeps performance improving. Workstreams include renewables sourcing, circular economy models and ESG reporting that meets global investor expectations.

90-day action plan for operations leaders

• Week 1-2: Set the baseline. Consolidate energy, throughput, downtime and scrap data. Select 3-5 process KPIs tied to emissions intensity.
• Week 3-6: Pilot two AI use cases. 1) Predictive maintenance on a critical asset; 2) Soft-sensor or setpoint optimisation for a high-energy unit (kiln, furnace, reformer).
• Week 7-10: Tighten data and controls. Integrate pilot outputs into DCS/MES with clear operator playbooks. Add metering where gaps block decision quality.
• Week 11-12: Build the business case. Quantify OPEX, energy and emissions impacts. Map incentives and financing. Define a staged scale-up plan across lines and sites.
• In parallel: Governance and reporting. Match disclosures to accepted standards, document methods and prepare for third-party verification.

If your team needs a fast primer on practical AI skills for plant operations and reporting, explore current options at Complete AI Training.

The takeaway for operations: treat decarbonisation as continuous improvement. Put AI where it moves throughput, downtime and energy use today; let capture projects benefit from a cleaner, more stable process tomorrow.