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Climate Solve AI

Industrial Decarbonization

AI cuts emissions from heavy industry by optimizing processes, electrifying heat, integrating hydrogen and CCS, and reducing waste and leaks across complex plants and supply chains.

The Challenge

Industry (steel, cement, chemicals, refining, pulp & paper) contributes a large share of global emissions. Processes are capital‑intensive, heat‑hungry, and hard to electrify. Operators need actionable insights to lower energy intensity, switch to low‑carbon feedstocks and fuels, and capture remaining CO₂ — without sacrificing uptime or product quality.

Where AI Makes a Difference

  • Advanced process control: Reinforcement learning and MPC tune kilns, furnaces, crackers, and reactors for minimal energy and emissions.
  • Quality prediction & yield optimization: Models forecast spec deviations and adjust setpoints to reduce scrap and rework.
  • Heat integration & electrification: AI designs heat‑recovery networks and schedules electric boilers/HPs against price and carbon signals.
  • Hydrogen & CCS integration: Planning tools optimize H₂ blending, storage, and CO₂ capture/transport/storage economics.
  • Fugitive emissions & leak detection: CV + IoT spot methane/volatile leaks for rapid repair.
  • Supply chain decarbonization: Scope 3 modeling guides material choices, routing, and vendor engagement.

Example Applications

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Kiln & Furnace Optimization

AI tunes air‑fuel ratios, residence times, and heat recovery to lower energy intensity.

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Predictive Quality & Yield

Multivariate models anticipate off‑spec and adjust setpoints to reduce scrap.

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Leak Detection & Repair

Computer vision and sensors identify methane/volatile leaks to cut fugitives fast.

Key Metrics

10–20%

Energy‑intensity reduction via advanced control & heat integration

5–15%

Yield/throughput improvement from predictive quality control

30–60%

Faster leak detection & repair cycles using CV + IoT analytics