Modeling and Optimization for Multi-Scale Integration, 2nd Edition

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 427

Special Issue Editor


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Guest Editor
Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
Interests: modeling and simulation; system modeling; systems dynamics; energy management systems; applied optimization; sustainable processes
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Special Issue Information

Dear Colleagues,

This previous Special Issue, titled “Modeling and Optimization for Multi-Scale Integration”, focused on the forefront of chemical engineering, exploring the combination of multi-scale process modeling and optimization strategies. This Special Issue presented new modeling techniques that spanned molecular dynamics to macro systems, revealing complex interactions between different scales. These models enhanced our understanding and aided practical decision-making for process optimization, which was particularly important in the context of the ongoing shift toward cleaner energy sources.

Covering a range of topics from reaction engineering and fluid dynamics to heat transfer and materials synthesis, this Special Issue included a diverse array of subjects. Advanced computational methods, innovative data collection approaches, and optimization techniques played a central role. The primary goal was to accelerate the development of sustainable processes in sectors undergoing energy transition, such as renewable energy, energy storage, and emissions reduction.

Building on the strong interest and high-quality contributions received in the first edition, this second volume continued the exploration of modeling and optimization for multi-scale integration. Rapid advances in computational power, increased availability of high-resolution data, and closer coupling between physics-based and data-driven approaches are further expanding the relevance of multi-scale methodologies. These developments reinforce the need for a continued forum dedicated to integrating models across scales in support of sustainable and energy-efficient process design.

As Guest Editor, I welcome submissions of original research, informative reviews, and insightful case studies from both academia and industry. By combining pioneering ideas, we open up new pathways in multi-scale integration. This Special Issue serves as a platform for promoting optimized processes that align with environmental goals, reshaping the landscape of chemical engineering, and contributing to a more environmentally friendly future.

Dr. Marco Vaccari
Guest Editor

Manuscript Submission Information

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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. Processes 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 2400 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

  • modeling
  • optimization
  • multi-scale
  • digital twin
  • simulation
  • data-driven modeling
  • scale-up

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Published Papers (1 paper)

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Research

17 pages, 2551 KB  
Article
Bayesian Optimisation for Minimising Tritium Losses Within the Hydrogen Isotope Separation System of the Fusion Fuel Cycle
by Emma A. Barrow, Franjo Cecelja, Iryna Bennett, Megan Thompson, Eduardo Garciadiego-Ortega and Dimitrios Tsaoulidis
Processes 2026, 14(9), 1373; https://doi.org/10.3390/pr14091373 - 24 Apr 2026
Viewed by 268
Abstract
Tritium self-sufficiency is a fundamental design requirement of a fusion fuel cycle, necessitated by the limited global availability of tritium relative to the fuelling demands of a fusion reactor. Minimising tritium losses within a fuel cycle is therefore essential. The Hydrogen Isotope Separation [...] Read more.
Tritium self-sufficiency is a fundamental design requirement of a fusion fuel cycle, necessitated by the limited global availability of tritium relative to the fuelling demands of a fusion reactor. Minimising tritium losses within a fuel cycle is therefore essential. The Hydrogen Isotope Separation System (HISS) employs cryogenic distillation technology to remove excess protium and deuterium while rebalancing the deuterium–tritium (DT) mixture required for reactor operation. However, the HISS design involves a trade-off between reduced tritium emissions and increasing internal tritium inventory, both contributing to the overall tritium losses. In this work, a multi-objective Bayesian Optimisation (BO) framework based on an ε-constraint formulation is developed to construct Pareto-optimal solutions to compare alternative HISS architectures. Gaussian Process surrogate models derived from physics-based Aspen Plus simulations are used to resolve the non-linear relationships between design variables and performance metrics, including tritium inventory, tritium emission losses, and bottom-product purity. Application of the framework to representative case studies demonstrates that tritium emission losses significantly exceed tritium decay losses associated with internal inventory hold-ups across the investigated operating conditions. The proposed framework enables quantitative comparison of equilibrator integration strategies to compare HISS architectures and assess their impact on tritium losses within the fusion fuel cycle. Full article
(This article belongs to the Special Issue Modeling and Optimization for Multi-Scale Integration, 2nd Edition)
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