This project develops an integrated framework for evaluating Singapore-linked cross-border CO₂ maritime transport and storage pathways. The research combines process modeling, techno-economic analysis, uncertainty quantification, risk analysis, and machine learning to assess uncertainties across liquefaction, buffer storage, port operations, shipping, receiving terminals, and storage interfaces.

The project aims to support more robust infrastructure planning by identifying the technical, economic, and operational factors that most strongly influence project cost, risk, and long-term viability.

Funder: Ministry of Education Academic Research Fund (MOE AcRF) Tier 1
Role: Principal Investigator
Funding: S$150,000

Research themes: CCUS · Maritime CO₂ Transport · Techno-Economic Analysis · Uncertainty · Risk-Informed Decision-Making

This project develops AI-enabled and data-driven technologies for end-to-end verification, traceability, and visibility across the rebar supply chain.

The research integrates digital records, automated verification, data analytics, and intelligent decision-support methods across manufacturing, logistics, delivery, and construction operations. The goal is to improve traceability, reduce manual verification effort, strengthen quality assurance, and support more efficient project delivery.

Funder: Industry Alignment Fund – Industry Collaboration Project
Role: Project Co-Lead
Funding: S$1.4316 million

Research themes: Artificial Intelligence · Digital Traceability · Construction Technology · Data Analytics · Industry 4.0

A collaborative research and workforce-development initiative focused on carbon capture, transport, storage, and regional CCUS deployment.

Dr. Ma contributes research in CO₂ transportation, infrastructure modeling, techno-economic analysis, storage feasibility, and decision support, together with professional training and knowledge exchange.

Funder: U.S. Department of Energy, Office of Fossil Energy and Carbon Management
Role: Co-PI
Funding: US$1.4 million

Research themes: CCUS · CO₂ Transport · Geological Storage · Techno-Economic Analysis · Workforce Development

Development and application of SimCCS, an integrated open-source platform for designing and evaluating CO₂ capture, transport, and storage infrastructure.

Dr. Ma served as Co-PI and lead developer of SimCCS 3.0, contributing to model development, large-scale network optimization, transport infrastructure analysis, storage integration, and decision-support applications.

Funder: U.S. Department of Energy
Role: Co-PI

Development of large-scale modeling frameworks for national CO₂ transport infrastructure planning in the United States.

The project combined geospatial analysis, optimization, storage-resource assessment, and infrastructure modeling to evaluate regional and national-scale CCS deployment pathways.

Funder: U.S. Department of Energy
Role: Co-PI

This project applied SimCCS to evaluate CO₂ transport infrastructure associated with the U.S. power sector, supporting strategic analysis of potential carbon-management deployment pathways.

Funder: U.S. Department of Energy, Office of Policy
Role: Co-PI

The SMART project developed science-informed machine-learning approaches to accelerate real-time decision-making in subsurface applications.

The research integrated physics-based simulation, machine learning, data assimilation, and uncertainty-aware modeling to improve computational efficiency and support faster, more informed subsurface decisions.

Funder: U.S. Department of Energy
Role: Co-Investigator

CATALOG was a large-scale research consortium focused on developing advanced methods for identifying, characterizing, and assessing undocumented and orphaned oil and gas wells.

The project combined artificial intelligence, multimodal data analysis, geospatial methods, environmental assessment, and field information to improve understanding of legacy well infrastructure and associated environmental risks.

Funder: U.S. Department of Energy
Role: Work Package Lead

Development of machine-learning-based surrogate and proxy models to accelerate computationally intensive subsurface production and optimization problems.

Funders: Stanford Smart Fields Consortium and NSERC
Role: Co-Investigator