Forge AI - Autonomous Cement Operations

ForgeAI — Intelligent Cement Operations

Inspiration

India’s cement industry faces increasing pressure to operate efficiently while meeting legally mandated sustainability goals, especially with the introduction of the Greenhouse Gas Emission Intensity (GEI) Target Rules, 2025. Unlike earlier voluntary schemes such as PAT, GEI requires measurable reductions in emissions intensity.

At the same time, cement plants generate massive real-time data across raw materials, grinding, kilns, fuels, and utilities—but much of it remains siloed and underutilized.
ForgeAI was inspired by the need to bridge operational intelligence with regulatory compliance, enabling cement plants to autonomously optimize energy, quality, and emissions in real time.

What It Does

ForgeAI is a Generative AI–driven autonomous operations platform that:

Optimizes energy consumption across cement processes
Stabilizes product quality despite raw material variability
Reduces greenhouse gas emissions intensity
Supports compliance with GEI Target Rules, 2025
Enables real-time, cross-process decision-making

How We Built It

ForgeAI is built on a real-time, event-driven architecture:

Confluent Kafka serves as the backbone, streaming high-frequency data from raw materials, grinding mills, kilns, alternative fuels, utilities, and material handling systems.
Confluent connectors integrate plant control systems and historians into a unified data stream, breaking traditional process silos.
Generative AI models analyze streaming data to predict variability, recommend optimal control actions, and generate intelligent insights.
A feedback loop enables continuous learning, allowing the system to adapt to changing operating conditions.

This architecture enables real-time optimization aligned with energy efficiency, quality stability, and emissions-intensity reduction.

Challenges We Ran Into

Managing high-volume, real-time industrial data from heterogeneous sources
Breaking down siloed operations to enable cross-process optimization
Mapping operational decisions directly to emissions intensity outcomes
Ensuring AI recommendations are explainable, safe, and trusted by operators
Balancing real-time responsiveness with model accuracy

Confluent was critical in overcoming these challenges by providing a scalable, low-latency streaming platform that supports reliable, real-time intelligence.

Accomplishments We’re Proud Of

Designed an emissions-aware autonomous optimization platform
Unified end-to-end cement operations through real-time data streaming
Enabled alternative fuel optimization to improve thermal substitution rates
Aligned operational intelligence with regulatory GEI compliance
Built a scalable foundation ready for multi-plant deployment

What We Learned

Energy efficiency alone is not enough—emissions intensity must be actively managed
Real-time streaming data is essential for industrial AI success
Generative AI is most powerful when paired with event-driven architectures
Sustainability, quality, and productivity must be optimized together, not separately
Confluent’s streaming platform is a key enabler for cross-process industrial intelligence