Data-driven modelling techniques for earth-air heat exchangers to reduce energy consumption in buildings: a review

Ahmed, Shams Forruque; Saha, Suvash C.; Debnath, J C; Liu, G.; Mofijur, M.; Baniyounes, Ali M.; Chowdhury, Shyamapriya; Vo, Dai‐Viet N.

doi:10.1007/s10311-021-01288-7

Cited by 4 publications

(3 citation statements)

References 139 publications

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“…The heat exchanger is essential equipment in the renewable and sustainable energy sectors. The hydrodynamic and heat transport properties of heat exchangers significantly impact the performance of renewable and sustainable energy systems. , There are various methods for increasing a heat exchanger’s efficiency. The use of working fluids with improved thermal characteristics would be the most efficient of these strategies .…”

Section: Machine Learning For Rementioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

208

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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Section: Machine Learning For Rementioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

208

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“…Renewable sources of energy have a great prospect in this scenario 7,8 . Renewable energy sources like biomass, solar, wind, hydel, and geothermal are green resources for renewable energy production 9 . Both ecological protection and energy security concerns can be attended to by utilizing renewable energy sources 10,11 .…”

Section: Introductionmentioning

confidence: 99%

“…7,8 Renewable energy sources like biomass, solar, wind, hydel, and geothermal are green resources for renewable energy production. 9 Both ecological protection and energy security concerns can be attended to by utilizing renewable energy sources. 10,11 Biomass has great potential in this context, as it is renewable and works on the carbon-fixation process with low emissions.…”

Section: Introductionmentioning

confidence: 99%

Sustainability of biodiesel production using immobilized enzymes: A strategy to meet future bio‐economy challenges

Raza

Ali

Inayat

et al. 2022

Intl J of Energy Research

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Summary Biodiesel is an evolving energy substitute for petroleum‐based fuel products. In terms of antropogenic impacts, it is an environmentally friendly and sustainable energy source. Biodiesel is produced from a wide variety of biomass materials and its production costs are mainly related to the feedstock and production process. Transesterification is the most commonly used method for biodiesel production. Enzymatic transesterification using lipase‐enzyme has certain advantages over acid‐alkaline transesterification. The issues relating to enzyme stability, separation, and recyclability in enzymatic transesterification are further improved using immobilized enzymes. Here, we review the potential of immobilized‐enzyme technology in biodiesel production as a safer strategy to meet the needs of a sustainable future economy and will conduct future research studies. We critically analyze the state‐of‐the‐art and latest research on the production of biodiesel using immobilized lipase enzymes. Results indicate that liquid lipase's immobilization increases the stability, reactivity, and reusability and significantly decreases biodiesel production costs

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Investigation of Various Data-Driven Modeling Techniques for an Industrial Heat Exchanger

Resma Madhu,

Subbaiah

2024

Data Management, Analytics and Innovation

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Data-driven modelling techniques for earth-air heat exchangers to reduce energy consumption in buildings: a review

Cited by 4 publications

References 139 publications

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sustainability of biodiesel production using immobilized enzymes: A strategy to meet future bio‐economy challenges

Investigation of Various Data-Driven Modeling Techniques for an Industrial Heat Exchanger

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