Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.8
Journals
Processes
Special Issues
Modeling and Optimization for Green Energy Materials: Machine Learning, Conventional,...
share announcement

Modeling and Optimization for Green Energy Materials: Machine Learning, Conventional, and Hybrid Approaches

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

Deadline for manuscript submissions: 20 May 2026 | Viewed by 3506

Special Issue Editors

Dr. Anibal Alviz-Meza

E-Mail Website
Guest Editor
Chemical Engineering Department, Nanomaterials and Computer Aided Process Engineering Research Group (NIPAC), Universidad de Cartagena, Cartagena 130014, Colombia
Interests: machine learning; process modeling and optimization; energy systems; green materials; coatings and films
Prof. Dr. Viatcheslav Kafarov

E-Mail Website
Guest Editor
Chemical Engineering Department, Universidad Industrial de Santander, Bucaramanga 680002, Colombia
Interests: process modeling and simulation; energy transition; environmental sustainability; green materials

Special Issue Information

Dear Colleagues,

Developing green energy materials for generation and storage is crucial to advancing sustainable and resilient energy systems. In recent years, the convergence of traditional process modeling and artificial intelligence tools has created new opportunities to design, optimize, and scale these materials more efficiently and with a lower environmental impact.

This Special Issue, "Modeling and Optimization for Green Energy Materials: Machine Learning, Conventional, and Hybrid Approaches", aims to compile research that integrates mechanistic simulation methods, machine learning, and hybrid strategies to study energy materials. Topics of interest include, but are not limited to, the following:

  • Modeling, process simulation, and optimization to produce green energy materials using conventional or hybrid approaches.
  • Applications of machine learning and artificial intelligence in the design, prediction, and control of sustainable materials and processes.
  • The integration of digital tools and application of process simulators and data-driven models to enhance energy efficiency and material performance.
  • Techno-economic, environmental, and life-cycle assessments supported by simulation and intelligent modeling techniques.

We welcome original contributions, both experimental and computational, that advance the state of the art of materials science, process engineering, and data science regarding the energy transition.

We look forward to receiving your contribution and working together to establish a Special Issue with a significant scientific and technological impact.

Sincerely,

Dr. Anibal Alviz-Meza
Prof. Dr. Viatcheslav Kafarov
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 250 words) can be sent to the Editorial Office for assessment.

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

  • green energy materials
  • machine learning
  • modeling and simulation of processes
  • sustainable process engineering
  • data-driven process engineering
  • digital twins

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 2580 KB  
Article
From Viability to Resilience: Technical–Economic Insights into Palm Oil Production Using a FP2O Approach
by Sofía García-Maza, Segundo Rojas-Flores and Ángel Darío González-Delgado
Processes 2025, 13(12), 4056; https://doi.org/10.3390/pr13124056 - 15 Dec 2025
Viewed by 388
Abstract
This work presents an assessment focused on technical, economic, and resilience-related aspects applied to a crude palm oil production process using the FP2O methodology, considering a capacity of 30 tons of fresh fruit bunches (FFB) per hour and an annual production [...] Read more.
This work presents an assessment focused on technical, economic, and resilience-related aspects applied to a crude palm oil production process using the FP2O methodology, considering a capacity of 30 tons of fresh fruit bunches (FFB) per hour and an annual production of 54,056 tons of oil per year. Operating parameters, capital and input costs, as well as the total investment, which amounts to approximately US$43 million, distributed between fixed capital, working capital, and start-up costs, were established. The analysis identified annual operating costs of US$24.7 million, with a majority share of raw materials. Economic and financial indicators showed positive values, higher than previous studies, highlighting a gross profit of over US$23 million, an after-tax profitability of US$13.7 MM, and an internal rate of return of 25.29%, which demonstrates the economic viability of the process. A simple payback period of 1.62 years and a discounted payback period of 4.88 years were determined, in addition to a positive net present value of $58.74 million, confirming the project’s profitability over a 15-year horizon. Using the FP2O methodology, the technical and economic resilience of the process to variations in product price, raw material costs, processing capacity, and normalized operating costs was evaluated. The results showed sensitivity to reductions in the oil sales price, while also demonstrating high resilience to increases in palm bunch costs and decreases in processing capacity. Furthermore, the break-even analysis revealed that the plant can operate 36.59% below its maximum capacity and maintain positive margins, requiring a minimum of 87,825 tons of raw material per year and a sales price of $482.35 per ton to avoid losses. This research highlights the applicability of the FP2O methodology as a strategic tool for scaling up crude palm oil production processes, guiding investment decisions, and supporting policies that promote more resilient and sustainable agro-industrial systems. Full article
(This article belongs to the Special Issue Modeling and Optimization for Green Energy Materials: Machine Learning, Conventional, and Hybrid Approaches)
►▼ Show Figures

Figure 1

21 pages, 2250 KB  
Article
Analysis of Inherent Chemical and Process Safety for Biohydrogen Production from African Palm Rachis via Direct Gasification and Selexol Purification
by Lina Mejía-González, Antonio Mendivil-Arrieta and Ángel Darío González-Delgado
Processes 2025, 13(12), 4052; https://doi.org/10.3390/pr13124052 - 15 Dec 2025
Viewed by 325
Abstract
Biofuels, such as biodiesel, bioethanol, and biohydrogen produced from organic waste, constitute a sustainable alternative to non-renewable fuels and drive the energy transition. In this work, the inherent safety methodology was implemented to quantify and evaluate the intrinsic risks of obtaining hydrogen from [...] Read more.
Biofuels, such as biodiesel, bioethanol, and biohydrogen produced from organic waste, constitute a sustainable alternative to non-renewable fuels and drive the energy transition. In this work, the inherent safety methodology was implemented to quantify and evaluate the intrinsic risks of obtaining hydrogen from African palm stems through direct gasification with Selexol. The methodology indicators were calculated with reference to databases, the literature, and safety data sheets, considering critical stages of the process. The total inherent safety index (ISI) was 38, classifying the process as intrinsically unsafe, with the chemical component scoring 21 points, with hydrogen being the main chemical risk (8 points), along with hazards generated by chemical reactions. Likewise, high temperature and pressure conditions indicate the presence of potentially unsafe equipment such as reactors and heat exchangers, giving the process index 17 points. Based on the results, it is recommended to reduce raw material inventories and optimize operating conditions to reduce the potential for hazardous events and improve overall inherent safety. Full article
(This article belongs to the Special Issue Modeling and Optimization for Green Energy Materials: Machine Learning, Conventional, and Hybrid Approaches)
►▼ Show Figures

Figure 1

29 pages, 3310 KB  
Article
Impact of Mass Integration on the Technoeconomic Performance of the Gas Oil Hydrocracking Process in Latin America
by Sofía García-Maza, Segundo Rojas-Flores and Ángel Darío González-Delgado
Processes 2025, 13(11), 3681; https://doi.org/10.3390/pr13113681 - 14 Nov 2025
Cited by 1 | Viewed by 532
Abstract
The gas oil hydrocracking process is a cornerstone of modern refining, enabling the conversion of heavy fractions into high-value fuels such as diesel, kerosene, LPG, and naphtha. However, despite its economic significance, its considerable water requirements for cooling, washing, and steam generation lead [...] Read more.
The gas oil hydrocracking process is a cornerstone of modern refining, enabling the conversion of heavy fractions into high-value fuels such as diesel, kerosene, LPG, and naphtha. However, despite its economic significance, its considerable water requirements for cooling, washing, and steam generation lead to high utility costs, which may undermine profitability, representing the problem of the study. This study addresses the issue through a techno-economic assessment and resilience analysis of an industrial-scale, mass and energy-integrated gas oil hydrocracking process, utilizing the novel FP2O methodology. The process was modeled in Aspen HYSYS® V14.0 with a capacity of 1.94 Mt/year, assuming a feedstock cost of USD 350/t and a primary product (diesel) price of USD 1539/t. The total capital investment (TCI) was estimated at USD 175.68 million, while utility expenses reached USD 1312.18 million/year, representing nearly half of the total product cost (TPC) of USD 2692.20 million/year. A set of twelve techno-economic and three financial indicators was determined, yielding a gross profit (GP) of USD 97.69 million, profitability after tax (PAT) of USD 64.96 million, and a net present value (NPV) of USD 229.62 million. The payback period (PBP) was 1.41 years, with a depreciable payback period (DPBP) of 2.99 years. The return on investment (ROI) was 36.97%, and the internal rate of return (IRR) reached 44.81%, evidencing strong profitability relative to comparable petrochemical operations. Resilience analysis highlighted sensitivities to fluctuations in product prices, feedstock costs, and normalized variable operating costs (NVOC), identifying a critical NVOC of USD 1435/t against the current operation at USD 1384.74/t, which suggests a narrow buffer before profitability deteriorates. Overall, the findings confirm that mass and energy integration enhances resource efficiency but does not fully mitigate exposure to feedstock and utility price volatility. This work constitutes the first application of FP2O to a mass and energy-integrated gas oil hydrocracking facility, establishing a benchmark for holistic techno-economic and resilience assessments in complex petrochemical systems. Full article
(This article belongs to the Special Issue Modeling and Optimization for Green Energy Materials: Machine Learning, Conventional, and Hybrid Approaches)
►▼ Show Figures

Figure 1

22 pages, 2085 KB  
Article
Trends in Using Microalgae as a Green Energy Source: Conventional, Machine Learning, and Hybrid Modeling Methods
by Ángel Darío González-Delgado, Segundo Rojas-Flores and Anibal Alviz-Meza
Processes 2025, 13(10), 3134; https://doi.org/10.3390/pr13103134 - 29 Sep 2025
Viewed by 1635
Abstract
This study analyzes, quantifies, and maps, from a bibliometric perspective, scientific research on microalgae energy production. It includes traditional simulation, machine learning, and hybrid approaches, covering 500 original articles from 2005 to 2024 in Scopus. We used Biblioshiny 4.1.2 software in RStudio 4.3.0 [...] Read more.
This study analyzes, quantifies, and maps, from a bibliometric perspective, scientific research on microalgae energy production. It includes traditional simulation, machine learning, and hybrid approaches, covering 500 original articles from 2005 to 2024 in Scopus. We used Biblioshiny 4.1.2 software in RStudio 4.3.0 to categorize and evaluate the contributions of authors and journals. The studied field underwent an exponential growth in publications from 2004 to 2022, with an average annual increase of approximately 21%. Moreover, recent research focuses on photobioreactors, computational fluid dynamics, carbon dioxide capture, bio-oils, biodiesel, and hydrothermal liquefaction, increasingly integrating machine learning algorithms and hybrid methods. Since 2020, we have identified a clear trend toward combining modeling approaches to predict and improve energy efficiency, particularly for biodiesel, bio-derived hydrogen, and crude bio-oil produced via pyrolysis or hydrothermal liquefaction, which is often influenced by factors such as light, carbon dioxide, nutrients, and blending operations. Finally, recent advancements involve combining physical models with data to enable real-time optimization and control, supporting microalgae-based circular biorefining strategies. This review serves as a guide for future research in green energy materials and process modeling, inspiring colleagues to explore new ways for microalgae energy production and modeling. Full article
(This article belongs to the Special Issue Modeling and Optimization for Green Energy Materials: Machine Learning, Conventional, and Hybrid Approaches)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop