Advances in Carbon Capture, Utilization & Storage (CCUS)
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 122
Special Issue Editors
Interests: CCUS; CO2/H2 transport modeling; energy transition; optimization; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; geological carbon storage; reservoir simulation; digital rock physics; lattice Boltzmann methods
Special Issue Information
Dear Colleagues,
Carbon capture, utilization, and storage (CCUS) provides a practical route to reducing greenhouse-gas emissions across the power, industrial, and energy-intensive sectors. Because CCUS spans an end-to-end value chain—from separating CO2 at the source to its transport, conversion, and secure geological storage—it calls for coordinated progress in science, engineering, and policy.
This Special Issue welcomes original research articles and authoritative reviews that advance research on any aspect of the CCUS value chain. We invite authors to submit papers spanning the entire chain—including experimental, modeling, and pilot studies; techno-economic assessments; field demonstrations; policy or regulatory analyses; and integrated process designs. Topics of interest include (but are not limited to) the following:
- CO2 capture: novel solvents, sorbents, membranes, and intensified processes;
- CO2 transport: network planning, pipeline integrity, and infrastructure optimization;
- CO2 utilization: chemical conversion, mineralization, and other value-adding pathways;
- CO2-based enhanced oil recovery: experiments on and simulations of CO2-EOR processes, filed implementation and monitoring, CO2-EOR, and carbon storage synergies;
- Subsurface storage: formation characterization, injection strategies, caprock integrity, and risk assessment;
- Simulation and optimization of CCUS hubs, clusters, and full-value-chain deployments;
- Machine learning and data analytics applications across carbon capture, transport, utilization, and storage;
- Risk, safety, and lifecycle assessments for CCUS projects;
- Techno-economic and policy frameworks enabling large-scale CCUS project implementation.
Our goal is to assemble a collection of papers presenting cutting-edge insights that will accelerate CCUS projects and broaden their contribution to global decarbonization. We look forward to receiving your submissions.
Dr. Martin Ma
Dr. Hongsheng Wang
Dr. Tao Bai
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- CCUS
- CO2 storage/sequestration
- CO2 transport
- CO2 capture
- modeling
- machine learning
- risk assessment
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