Bioenergy prediction using computer vision and machine intelligence: modeling and optimization of bioenergy production DOI

Ruchita Shrivastava,

Raju Rajak,

Akash

и другие.

Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 141 - 162

Опубликована: Окт. 1, 2024

Язык: Английский

Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach DOI
Hung Vo Thanh, Mohammad Rahimi, Suparit Tangparitkul

и другие.

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 83, С. 1 - 12

Опубликована: Авг. 8, 2024

Язык: Английский

Процитировано

10
Sustainable Freshwater/Energy Supply through Geothermal-Centered Layout Tailored with Humidification-Dehumidification Desalination Unit; Optimized by Regression Machine Learning Techniques DOI
Shuguang Li, Yuchi Leng, Rishabh Chaturvedi

и другие.

Energy, Год журнала: 2024, Номер 303, С. 131919 - 131919

Опубликована: Июнь 3, 2024

Язык: Английский

Процитировано

5
Development of the CO2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms DOI
Hossein Mashhadimoslem, Mohammad Ali Abdol,

Kourosh Zanganeh

и другие.

ACS Applied Energy Materials, Год журнала: 2024, Номер unknown

Опубликована: Сен. 19, 2024

Язык: Английский

Процитировано

5
Harnessing Biomass Energy: Advancements through Machine Learning and AI Applications for Sustainability and Efficiency DOI
B. Deepanraj, Prabhakar Sharma, Bhaskor Jyoti Bora

и другие.

Process Safety and Environmental Protection, Год журнала: 2024, Номер 191, С. 193 - 205

Опубликована: Авг. 24, 2024

Язык: Английский

Процитировано

4
Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques DOI
Mohammad Rahimi,

Amir Mehrpanah,

Parastoo Mouchani

и другие.

Industrial & Engineering Chemistry Research, Год журнала: 2024, Номер 63(44), С. 18940 - 18956

Опубликована: Окт. 25, 2024

Carbon materials possess active sites and functionalities on the surface that can attract prominent interest as solid adsorbents for diverse gas adsorption. This study aimed to predict optimized methane uptake, adsorption energy (Ead), adsorbent rediscovery through multitechniques of neural, regression, classifier ML-DFT, Uniform Manifold Approximation Projection (UMAP). Nitrogen oxygen (N/O) graphene, graphene oxide (GO), N-doped GO were applied storage medium. Multi-ML algorithms employed CH4 uptake (i) N/O such pyridinic (N-py), carboxyl (O–II), oxidized (N-x), hydroxyl (O-h), Nitroso (N-ni), Amine (primary, secondary, tertiary). (ii) The surfaces are decorated with heteroatoms construct (GO) GO. DFT calculations by PW91 Dmol3 package. N/O-functionalities in distance ∼2.0 3.1 Å groups obtained Ead approximately −2.0 −4 eV. Further, ML models accomplished forthcoming physisorption using multiadsorptive features an R2 0.99. ML-derived sensitivity analysis approach was specifications deformation energy, functionality type, structure. indicate levels −0.03 0.02 synergetic DFT/ML approaches distinguished modeled rediscovered phases functional structures. UMAP is a new screening play complementary role modeling process.

Язык: Английский

Процитировано

3
Optimizing hydrogen-rich gas production by steam gasification with integrated CaO-based adsorbent materials for CO2 capture: Machine learning approach DOI Creative Commons
Mohammad Rahimi, Shakirudeen A. Salaudeen

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 95, С. 695 - 709

Опубликована: Ноя. 21, 2024

Язык: Английский

Процитировано

3
Experimental simulation and analysis of Acacia Nilotica biomass gasification with XGBoost and SHapley Additive Explanations to determine the importance of key features DOI
Prabhu Paramasivam, Mansoor Alruqi, Ümit Ağbulut

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 136291 - 136291

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Role of Machine Learning and Artificial Intelligence in Biofuel/Bioenergy Productions DOI
Saira Mansab, Saima Nasreen,

Kousar Parveen

и другие.

Clean Energy Production Technologies, Год журнала: 2025, Номер unknown, С. 375 - 398

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Machine Learning Predicted Crop Yield and Soil Organic Carbon Variation After Biochar Application DOI
Zhichao Xu, Mingwei Li, Run Zhou

и другие.

Soil Use and Management, Год журнала: 2025, Номер 41(2)

Опубликована: Апрель 1, 2025

ABSTRACT The prediction of crop yield is significant interest for growers utilising biochar on land, while managers prioritise the enhancement soil organic carbon (SOC). However, it remains unclear to what extent machine learning can accurately predict or SOC when applied soil. In this study, Random Forest (RF) and Multilayer Perceptron Neural Network (MLP‐NN) models were employed with 297 paired data from field trials. results indicated that RF model (test R 2 = 0.83) did not differ significantly MLP‐NN 0.84) in predicting yield. 0.87) performs better than 0.53) SOC. most influential features found be application rate (15%), initial (13%), pH (10%), TP (10%). contrast, variation was primarily influenced by latitude (26%), (22%), (13%). Furthermore, both multiple factors, solely one, their impacts necessarily linear. This study suggests optimization phosphorus content, along regulation its sandy clay‐rich soils, simultaneously enhance future, we hope develop a decision support system prediction, different scenarios, consultation capabilities based geospatial location.

Язык: Английский

Процитировано

0
From waste to worth: advances in energy recovery technologies for solid waste management DOI Creative Commons

Tarek Abedin,

Jagadeesh Pasupuleti, Johnny Koh Siaw Paw

и другие.

Clean Technologies and Environmental Policy, Год журнала: 2025, Номер unknown

Опубликована: Май 28, 2025

Язык: Английский

Процитировано

0