Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network

Kargbo, Hannah O.; Zhang, Jie; Phan, Anh N.

doi:10.1016/j.apenergy.2021.117567

Cited by 60 publications

(12 citation statements)

References 54 publications

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“…The output of the ANN models was found to agree with experimental data with absolute fractions of variance (R 2 ) more than 0.99 for CH 4 and CO models and greater than 0.98 for CO 2 and H 2 models. An ANN model was created by Kargbo et al 321 utilizing experimental data to forecast the gasification process. The study's objective was to use AI-based model prediction, resulting in saving time and money in the development and testing process.…”

Section: Solar Energy Systemsmentioning

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

222

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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: Solar Energy Systemsmentioning

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

222

View full text Add to dashboard Cite

show abstract

“…Of the reviewed studies that applied data-driven methods to model RRCC technologies, 20% (36% statistical and 64% ML methods) represented models that predicted syngas yield through the gasification of various feedstocks (Figure a and Table S9). Gasification was most frequently modeled by applying MPR ,− using primary data and ANN ,− using both primary and secondary data. These MPR and ANN models respectively, comprised 33% and 36% of the data-driven gasification models (Figure a and Table S9).…”

Section: Applications Of Data Science In Rrcc From Organic Waste Streamsmentioning

confidence: 99%

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

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Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002−2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to costeffectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

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“…The evaluation of algorithms. There are generally three kinds of ML -supervised learning, unsupervised learning, and reinforcement learning (Kargbo et al, 2021). Supervised learning refers to a ML scenario where both the input descriptors and output values in the training set are given, and the purpose of learning is correlating the input and output data with relatively complex laws (Tabor et al, 2018).…”

Section: Technical Backgroundsmentioning

confidence: 99%

A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture

et al. 2023

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Effective carbon dioxide (CO2) capture plays indispensable roles in closing the global carbon cycle, serving the sustainable production of energy, and achieving the grand 1.5 °C goal by 2050. Considering the diversity and complexity of CO2 capture materials, machine learning has stepped into this field years ago and become a powerful tool that promotes the screening and design of involving parameters. From these perspectives, this critical review firstly summarizes the technical backgrounds for the applications of ML-based methods in CO2 capture. Then, through categorizing the materials into two major groups, that is, adsorbents (containing metal organic frameworks, carbonaceous materials, polymers, and zeolites) and absorbents (involving ionic liquids, amine-based absorbents, and deep eutectic solvents), the applications of this effective tool in relevant areas are scrutinized. The major concerns remain to be further addressed are derived based on the above discussions, namely 1) the development of consistent and integrated databases, 2) the wise digitalization of inherent properties of materials, and 3) the validation of the accuracy of ML-derived results under practical scenarios. The main purpose of this critical review is bridging the previous achievements and further developments of ML-assisted design of CO2 capture techniques.

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Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network

Cited by 60 publications

References 54 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

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture

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