Introduction to

AI revolution in Hydrogen Value chain.

PRESENTED BY

MR. SANJAY KAUL

2025 OCTOBER 31

Table of Contents

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Understanding of AI

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8

Relevance AI in Hydrogen Value Chain

13

Compliance with Hydrogen value chain stages

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Integrating AI with O&M of

hydrogen value chain

15

Impact of AI in regulatory and Commercialisation

17

Talent

18

AI in hydrogen value chain stages

Keywords

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‘Digital Intelligence for a Decarbonized Future.’

‘Ab-inetio Advantages of Hydrogen Value Chain

Understanding of A.I.

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AI works by simulating human intelligence through computer programs, enabling machines to see, understand language, analyze data, make recommendations, and act autonomously. Technologies that support AI include:​

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• Machine Learning (ML): Algorithms that allow systems to learn and improve from data without being explicitly programmed.​

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• Deep Learning: Subset of ML using neural networks with multiple layers to identify patterns.

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• Natural Language Processing (NLP): Enables understanding and generation of human language, like chatbots and translators.​

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• Expert Systems: Mimic the decision-making abilities of human experts using predefined rules.

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*References : Coursera, IBM, Geeksforgeeks, Britannica

When all what Machines can do in Layered to do plus that

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Ammonia

Production

Fuel

cells

Energy

Storage

Synthetic

fuels

Energy

Generation

Relevance of A.I. in Hydrogen value chain.

‘Where Intelligence Meets Energy.’

Refining

Combustion Engine

Manufacturing & Mining, Glass , Steel

AI Can manage both the Application & Hydrogen Value Chain

Relevance of A.I. in Hydrogen value chain.

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AI can be used even to work out the both technical & Financial Feasibility

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Relevance of A.I. in Hydrogen value chain in recent times

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A.I. In Hydrogen value chain stages

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• Hydrogen Supply Chain Modeling: Hierarchical deep multi-agent reinforcement learning enables comprehensive design and management of hydrogen supply chains, integrating policy planning, market analysis, power systems, and distribution.​
• Plant Layout and Configurator Tools: Generative AI chatbots, such as Siemens' Hydrogen Plant Configurator, assist stakeholders in creating plant designs for entire hydrogen systems, making the process more flexible and data-driven.​
• Renewable Integration and Feasibility Simulation: AI balances renewable generation (wind, solar) with electrolyzer and auxiliary systems, predicting output fluctuations and simulating multiple design scenarios for robust feasibility analysis.
• Smart Grid and System Integration: Automated decision-making ensures seamless integration of hydrogen assets with the broader energy grid, using predictive algorithms for load balancing and supply-demand forecasting.

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DESIGN

By embedding intelligence at every phase — from concept to commissioning — AI ensures that hydrogen moves from promise to performance. The result: smarter decisions, optimized assets, and a resilient pathway to a net-zero future.

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AI can help customise already established platforms & Licensed technologies

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AI analyzes large datasets including topography, weather patterns, renewable energy availability, and infrastructure constraints to identify optimal locations for hydrogen production plants, storage facilities, and distribution networks.

Planning and Design Optimization

AI tools improve coordination and management by predicting delays, optimizing resource allocation, and enhancing communication among stakeholders. AI-driven predictive analytics forewarn of equipment failures or safety hazards during construction, enabling proactive mitigation and improved site safety.

Construction Project Management

AI-powered digital twin technology creates virtual models of hydrogen plants and infrastructure, enabling simulations of various construction scenarios, performance predictions, and risk assessments. This supports decision-making and optimizes project design and construction schedules, reducing time and cost overruns.

Digital Twin and Simulation

AI-guided integration planning ensures hydrogen plants can efficiently couple with renewable energy sources, optimizing operational parameters from construction through commissioning. This makes the infrastructure more adaptable to variable energy supply and demand.

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Integration with Renewable Energy and Operations

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A.I. In Hydrogen value chain stages

CONSTRUCTION

AI thus revolutionizes hydrogen infrastructure construction — driving precision, efficiency, and sustainability from planning to operation.

Strength of AI lies in leap Frogging in Project Management

Integration with Renewable Energy

A.I. In Hydrogen value chain stages

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1

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Catalyst Design and Material Innovation

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Fault Diagnosis & Detection

PRODUCTION

*References : Sciencedirect, Hartek

By combining real-time process control, advanced materials discovery, renewable energy integration, and predictive fault diagnosis, AI ensures green hydrogen production is efficient, resilient, and scalable.

A.I. In Hydrogen value chain stages

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STORAGE

By combining proactive safety monitoring, dynamic storage optimization, and advanced material development, AI transforms hydrogen storage into a reliable, high-performance component of the energy value chain.

AI Can Dynamically simulate storage solutions

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A.I. In Hydrogen value chain stages

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1

Supply Chain Optimization

2

Network Planning and Flexibility

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Demand Forecasting

4

Integration with Smart Energy Systems

DISTRIBUTION

*References : Precedenceresearch, marketsandmarkets

From route optimization to smart grid integration, AI ensures hydrogen delivery is cost-effective, reliable, and responsive to dynamic energy and market needs.

AI can Customise & Plug into already Existing Platforms

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Integrating AI with O&M of hydrogen value chain

OPERATIONS & MAINTAINANCE

Predictive

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Maintenance

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Intervention

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Operation

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Parameters

Inspection/

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Detection

A.I. compliances with Hydrogen Value Chain stages

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• Production Credit

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• Storage and Transportation

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• Distribution & End-Use

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• Transparency & Traceability

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• Controls, Auditing & Reporting

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AI ensures transparency, traceability, and regulatory compliance across the hydrogen value chain — from production and storage to distribution and end-use — by automating monitoring, reporting, and auditing processes.

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AI tools monitor operations in real time, automatically auditing data against local and international hydrogen regulations such as carbon intensity targets and safety protocols.

Regulatory Compliance Automation

AI enhances safety compliance through continuous monitoring of pressure, temperature, and potential hazard indicators in storage and transport infrastructure. Predictive models allow early intervention to prevent regulatory breaches.

Risk & Safety Governance:

AI analyzes lifecycle emissions and verifies provenance of hydrogen, distinguishing between green, blue, and grey hydrogen. This transparency helps certify “low-emission hydrogen” for export and trade, a prerequisite for meeting ESG standards and carbon pricing mechanisms in importing regions.

Carbon Traceability & Certification:

Digital certification powered by AI provides traceable “green credentials,” enabling producers to attract sustainability-focused buyers and participate in regulated green energy markets.

Product Traceability for Market Differentiation

Impact of A.I. In Hydrogen value chain regulatory & commercialisation

REGULATORY

*References : ceda, irena

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1

Policy Simulation & Impact Analysis

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Investment Optimization

3

Integrated Data Management

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Transaction Automation

*References : aast.edu

Impact of A.I. In Hydrogen value chain regulatory & commercialisation

COMMERCIAL

AI powers smarter policies, optimized investments, integrated operations, and automated transactions — de-risking and accelerating the hydrogen economy.

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Talent

Between March 2024 and March 2025, demand for AI and Data talent in India grew by an estimated 38% to 45%. But the growth has been uneven. GenAI-specific roles — Prompt Engineers, GenAI Researchers, LLMOps Specialists — surged by over 170%, reflecting the urgency to productionize AI capabilities. Data Engineering has become the foundation of scalable AI, while traditional BI roles are contracting, mirroring automation and toolchain consolidation.

Route Map

Step 1

Step 2

Step 3

Look at customizable Gap Through AI

Look at Customization of “ Off Shelf Offerings

Start infusing AI at designs stage & O& M

Thank you