HyProcess: A Smart Process System for Optimized Green Hydrogen Production, Storage and Transportation
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| _version_ | 1866901952086933504 |
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| author | C Hari Kiran Dhanush Shetty Goutham R |
| author_facet | C Hari Kiran Dhanush Shetty Goutham R |
| contents | <p><em><span>In the global effort to achieve a net-zero future, moving from fossil fuels to renewable energy sources is essential. Green hydrogen, made through water electrolysis powered by renewable energy, has become a key solution for reducing carbon emissions in hard-to- decarbonize sectors like heavy industry, shipping, and chemical synthesis. However, the current state of green hydrogen production is marked by fragmentation, high costs, and major technical inefficiencies. Production, storage, and transportation processes are often done manually, in isolation, and lack clarity. This results in misalignments between the intermittent supply of renewable energy and the operational needs of electrolyzers. Additionally, the absence of clear certification processes and the oversight of social equity issues, particularly water justice, create significant obstacles to widespread adoption.</span></em></p> <p><em><span>This paper presents HyProcess, a smart digital platform that combines AI for process matching, blockchain for transparency, fairness dashboards, and predictive maintenance using feedback loops. The system covers the entire hydrogen value chain, utilizing the Industrial Internet of Things (IIoT) for real-time data collection, Artificial Intelligence (AI) for balancing loads dynamically, and Digital Twin technology for simulation and optimization. The aim is to improve the efficiency of the production allocation process, ensure fair distribution of hydrogen across different regions and communities, and provide tailored operational support to underperforming assets. We discuss the terminology used in the background, review existing literature, examine current trends and challenges, propose an architecture and evaluation metrics, and provide use cases along with an implementation plan. HyProcess aligns with national and international objectives of digital governance, an inclusive energy transition, and improved industrial capability. </span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18874474 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | HyProcess: A Smart Process System for Optimized Green Hydrogen Production, Storage and Transportation C Hari Kiran Dhanush Shetty Goutham R Green Hydrogen, Smart Process System, AI Matching, Blockchain Logging, Fairness Dashboard, Predictive Maintenance, Digital Governance, Industrial IoT, Electrolysis Optimization. <p><em><span>In the global effort to achieve a net-zero future, moving from fossil fuels to renewable energy sources is essential. Green hydrogen, made through water electrolysis powered by renewable energy, has become a key solution for reducing carbon emissions in hard-to- decarbonize sectors like heavy industry, shipping, and chemical synthesis. However, the current state of green hydrogen production is marked by fragmentation, high costs, and major technical inefficiencies. Production, storage, and transportation processes are often done manually, in isolation, and lack clarity. This results in misalignments between the intermittent supply of renewable energy and the operational needs of electrolyzers. Additionally, the absence of clear certification processes and the oversight of social equity issues, particularly water justice, create significant obstacles to widespread adoption.</span></em></p> <p><em><span>This paper presents HyProcess, a smart digital platform that combines AI for process matching, blockchain for transparency, fairness dashboards, and predictive maintenance using feedback loops. The system covers the entire hydrogen value chain, utilizing the Industrial Internet of Things (IIoT) for real-time data collection, Artificial Intelligence (AI) for balancing loads dynamically, and Digital Twin technology for simulation and optimization. The aim is to improve the efficiency of the production allocation process, ensure fair distribution of hydrogen across different regions and communities, and provide tailored operational support to underperforming assets. We discuss the terminology used in the background, review existing literature, examine current trends and challenges, propose an architecture and evaluation metrics, and provide use cases along with an implementation plan. HyProcess aligns with national and international objectives of digital governance, an inclusive energy transition, and improved industrial capability. </span></em></p> |
| title | HyProcess: A Smart Process System for Optimized Green Hydrogen Production, Storage and Transportation |
| topic | Green Hydrogen, Smart Process System, AI Matching, Blockchain Logging, Fairness Dashboard, Predictive Maintenance, Digital Governance, Industrial IoT, Electrolysis Optimization. |
| url | https://doi.org/10.5281/zenodo.18874474 |