Structure Optimization and Transport Characteristics of Porous Media
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Materials Processes".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 13
Special Issue Editors
Interests: artificial intelligence; carbon molecular sieves; methane gas separation; carbon dioxide capture
Special Issue Information
Dear Colleagues,
Porous media are central to environmental sustainability and energy utilization. They enable targeted pollutant capture and CO₂ sequestration through structural optimization, while driving efficient operations (e.g., hydrocarbon recovery, geothermal energy) via advanced transport engineering. Achieving these objectives requires an integrated framework uniting materials science, fluid dynamics, and AI. This Special Issue spotlights research on the co-optimization of pore-scale design and system-scale transport control across diverse applications, inviting innovations that leverage AI to decode complex phenomena and deliver scalable solutions.
This Special Issue seeks high-quality research on the integrated design and analysis of porous media, emphasizing structural optimization for environmental applications (e.g., pollutant capture, CO₂ sequestration) and transport engineering for energy (e.g., hydrocarbon recovery, geothermal energy). We highlight advancements in artificial intelligence (AI) that bridge material design, transport physics, and industrial scalability. Topics include, but are not limited to, the following:
- AI-optimized porous materials (MOFs, biochar, hybrid composites) for the high-efficiency adsorption of pollutants, CO₂, or heavy metals;
- Multiphase transport dynamics in hydrocarbon reservoirs, including pore-scale modeling of oil–gas–water interactions and permeability enhancement strategies;
- Novel fabrication processes (3D printing, freeze-casting, templated synthesis) for hierarchical porous structures with tailored adsorption and flow properties;
- Intelligent transport control using machine learning (e.g., reinforcement learning for adaptive reservoir management, physics-informed neural networks for adsorption kinetics prediction);
- Industrial integration of porous media in carbon capture systems, hydraulic fracturing operations, and scalable environmental remediation technologies.
Dr. Zhenlong Song
Prof. Dr. Tingting Wang
Guest Editors
Manuscript Submission Information
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Keywords
- multiphase flow in reservoirs
- AI-optimized porous adsorbents
- hybrid physics–AI frameworks
- sorption–separation integrated pore architectures
- AI for reservoir management
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