Catalytic Upgrading of Pyrolysis Vapors from Scenedesmus sp. Microalgae towards Renewable Hydrocarbons using a Low-Cost Zeolite Synthesized from Rice Husk Ash and Diatomite Residue DOI

Júlio de Andrade Oliveira Marques,

José Luiz Francisco Alves, Karine Fonseca Soares de Oliveira

и другие.

BioEnergy Research, Год журнала: 2024, Номер 17(3), С. 1794 - 1804

Опубликована: Фев. 19, 2024

Язык: Английский

A review of hydrogen production and storage materials for efficient integrated hydrogen energy systems DOI Creative Commons
Feras Alasali, Mohammed I. Abuashour, Waleed Hammad

и другие.

Energy Science & Engineering, Год журнала: 2024, Номер 12(5), С. 1934 - 1968

Опубликована: Март 6, 2024

Abstract The rapidly growing global need for environmentally friendly energy solutions has inspired extensive research and development efforts aimed at harnessing the potential of hydrogen energy. Hydrogen, with its diverse applications relatively straightforward acquisition, is viewed as a promising carrier capable tackling pressing issues, such carbon emissions reduction storage. This study conducts preliminary investigation into effective generation storage systems, encompassing methods like water electrolysis, biomass reforming, solar‐driven processes. Specifically, focuses on assessing nanostructured catalysts innovative materials to enhance productivity versatility systems. Additionally, utilization novel not only improves capacity safety but also opens up possibilities inventive applications, including on‐demand release efficient transportation. Furthermore, critical factors catalyst design, material engineering, system integration, technoeconomic viability are examined identify challenges chart paths future advancements. emphasizes importance fostering interdisciplinary collaborations advance technologies contribute sustainable future.

Язык: Английский

Процитировано

22

Neural network-based surrogate modeling and optimization of a multigeneration system DOI Creative Commons
Parviz Ghafariasl, Alireza Mahmoudan, Mahmoud Mohammadi

и другие.

Applied Energy, Год журнала: 2024, Номер 364, С. 123130 - 123130

Опубликована: Апрель 5, 2024

Multi-Objective Optimization (MOO) poses a computational challenge, particularly when applied to physics-based models. As result, only up three objectives are typically involved in simulation-based optimization. To go beyond this number, Surrogate Models (SMs) need replace such high-fidelity In exploratory study, the perform comprehensive regression surrogate modeling and conduct MOO for Multi-Generation System (MGS). The most suitable SM was chosen among four neural-network models: Artificial Neural Network (ANN), Convolutional (CNN), Long-Short Term Memory (LSTM), an ensemble model developed through brute-force search using aforementioned final found be superior others, achieving R2 values ranging from 0.9830 0.9999. Next, optimization problem with six conflicting defined performed at distinct of Direct Normal Irradiation (DNI), time-dependent feature. This aimed provide multi-criteria decision-making information based on atmospheric transparency. new understandings were gained: (I) exergy efficiency, production cost, freshwater rate highly influenced by DNI, (II) critical range operation observed within DNI interval 100 400 W/m2. Furthermore, we compared result six-objective that bi-objective obtained our study all showed improvements 1.9% 12.7%. Finally, findings present some practical recommendations put forward applying proposed methodology similar MGSs.

Язык: Английский

Процитировано

21

Prediction of hydrogen−brine interfacial tension at subsurface conditions: Implications for hydrogen geo-storage DOI Creative Commons

Mostafa Hosseini,

Yuri Leonenko

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 58, С. 485 - 494

Опубликована: Янв. 25, 2024

Underground hydrogen storage (UHS) offers a promising approach for the of significant volumes gas (H2) within deep geological formations, which can later be utilized energy generation when necessary. Interfacial tension (IFT) between H2 and formation brine plays vital role in influencing distribution at pore scale and, ultimately, capacity. In this research, we developed four intelligent models: Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP). These models were designed to predict IFT utilizing pressure, temperature, molality. Additionally, fine-tuned three explicit correlations previously our research. To assess influence each parameter on IFT, conducted comprehensive analysis raw data exclude doubtful samples. This was followed by rigorous model development, including hyperparameter tuning, finally, an examination using testing data. The results clearly demonstrate superiority RF model, achieving high accuracy reliability with coefficients determination (R2), root mean square error (RMSE), average absolute relative deviation (AARD) values 0.96, 1.50, 1.84 %, respectively. exemplary performance attributed its inherent characteristics. ensemble excels capturing complex relationships, thereby enhancing predictive solidifying over other study. Furthermore, feature importance revealed that temperature has most influence, molality pressure. Moreover, assessed these through external not used initial training stages. Our study highlights exceptional power emphasizing practical selecting enhanced reliability. proposed method shows potential industrial applications, especially optimizing underground storage.

Язык: Английский

Процитировано

18

Synergizing food waste management and microalgae biorefinery for bioenergy production: recent advance on direct and indirect conversion pathway DOI
Adityas Agung Ramandani, Sze Ying Lee, Anet ­Režek ­Jambrak

и другие.

Process Biochemistry, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2

Deep learning–assisted phase equilibrium analysis for producing natural hydrogen DOI
Tao Zhang, Yanhui Zhang, Klemens Katterbauer

и другие.

International Journal of Hydrogen Energy, Год журнала: 2023, Номер 50, С. 473 - 486

Опубликована: Сен. 26, 2023

Язык: Английский

Процитировано

37

Towards industrial biological hydrogen production: a review DOI Creative Commons
George M. Teke, Bovinille Anye Cho, Catharine Elizabeth Bosman

и другие.

World Journal of Microbiology and Biotechnology, Год журнала: 2023, Номер 40(1)

Опубликована: Дек. 7, 2023

Abstract Increased production of renewable energy sources is becoming increasingly needed. Amidst other strategies, one promising technology that could help achieve this goal biological hydrogen production. This uses micro-organisms to convert organic matter into gas, a clean and versatile fuel can be used in wide range applications. While biohydrogen its early stages, several challenges must addressed for become viable commercial solution. From an experimental perspective, the need improve efficiency production, optimization strategy microbial consortia, reduction costs associated with process still required. scale-up novel strategies (such as modelling validation) discussed facilitate process. Hence, review considers not within framework particular method or technique, but rather outlines work (bioreactor modes configurations, modelling, techno-economic life cycle assessment) has been done field whole. type analysis allows abstraction industrially, giving insights applications, cross-pollination separate lines inquiry, reference point researchers industrial developers

Язык: Английский

Процитировано

25

Biohydrogen from waste feedstocks: An energy opportunity for decarbonization in developing countries DOI

Nitesh Premchand Machhirake,

Kumar Raja Vanapalli, Sunil Kumar

и другие.

Environmental Research, Год журнала: 2024, Номер 252, С. 119028 - 119028

Опубликована: Апрель 27, 2024

Язык: Английский

Процитировано

10

Biohythane production techniques and recent advances for green environment – A comprehensive review DOI

M. Aashabharathi,

Sourav Kumar, Shobana Sampath

и другие.

Process Safety and Environmental Protection, Год журнала: 2024, Номер 184, С. 400 - 410

Опубликована: Фев. 1, 2024

Язык: Английский

Процитировано

9

Investigation of Wettability and IFT Alteration during Hydrogen Storage Using Machine Learning DOI Creative Commons

Mehdi Maleki,

Mohammad Rasool Dehghani,

Ali Akbari

и другие.

Heliyon, Год журнала: 2024, Номер 10(19), С. e38679 - e38679

Опубликована: Сен. 30, 2024

Язык: Английский

Процитировано

9

Role of hydrogen-enrichment for in-direct diesel engine behaviours fuelled with the diesel-waste biodiesel blends DOI
Necdet Alçelik, Suat Sarıdemir, Fikret Polat

и другие.

Energy, Год журнала: 2024, Номер 302, С. 131680 - 131680

Опубликована: Май 17, 2024

Язык: Английский

Процитировано

8