Study on prediction fly ash generation using statistical method

Zahari, Nazirul Mubin; Mohamad, Daud; Arenandan, Vanisha; Beddu, Salmia; Sadon, Siti Nabihah; Syamsir, Agusril; Kamal, Nur Liyana Mohd; Zainoodin, Mahyun; Nadhirah, Afiqah

doi:10.1063/1.5066994

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…225 It was predicted by Zahari et al that the fly ash generation in Malaysia would reach 0.309 MT in 2021, making it a potentially valuable source of TES materials. 227 Next, steel slags produced during the iron ore melting process can be a potential TES material. About 10% to 20% of slags are generated per ton of steel production.…”

Section: Life Cycle Phasesmentioning

confidence: 99%

A Review on CSP and CPVT Technologies: Energy, Economic Analysis and Recycling towards Circular Economy

Wong

Walvekar²,

Vaka³

et al. 2023

ECS J. Solid State Sci. Technol.

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Recently, there has been a shift towards renewable energy sources for electricity generation due to rapidly depleting non-renewable energy sources. Solar energy is generally the most promising renewable energy source to be harvested by concentrated solar power (CSP) and solar photovoltaic (PV) technologies. CSP technology can be further classified into a few categories, such as parabolic trough collector (PTC), solar power tower, linear Fresnel reflector, and solar parabolic dish. In contrast, solar PV can be further developed into concentrated photovoltaic and concentrated photovoltaic thermal (CPVT) systems. The modifications (e.g. optical and thermal modifications) done on PTC and parabolic trough based CPVT systems on enhancing system performance are discussed. Next, the economic analyses conducted for both systems are discussed to compare the economic feasibility of the technologies employed in different countries. The appropriate recycling and CE approaches applied for PTC and PV technologies are reviewed and classified based on the key material elements within this industry. Overall, this paper compares PTC and parabolic trough-based CPVT systems from the technical, economic, and environmental aspects to provide insight for the solar energy harvesting field researchers.

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Section: Life Cycle Phasesmentioning

confidence: 99%

A Review on CSP and CPVT Technologies: Energy, Economic Analysis and Recycling towards Circular Economy

Wong

Walvekar²,

Vaka³

et al. 2023

ECS J. Solid State Sci. Technol.

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show abstract

“…However, the amount of generation of CFA is variable, so predicting the generation of CFA in advance is the basis for ensuring its effective disposal and rational utilization. Some scholars used neural networks in MATLAB and linear regression statistical analysis in IBM SPSS to predict the generation of CFA in power plants in five or ten years [15]. However, the description of this method is too simple and general, and the accuracy of the prediction has not been verified.…”

Section: Introductionmentioning

confidence: 99%

Comparison and Determination of Optimal Machine Learning Model for Predicting Generation of Coal Fly Ash

Lü

et al. 2022

Crystals

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The rapid development of industry keeps increasing the demand for energy. Coal, as the main energy source, has a huge level of consumption, resulting in the continuous generation of its combustion byproduct coal fly ash (CFA). The accumulated CFA will occupy a large amount of land, but also cause serious environmental pollution and personal injury, which makes the resource utilization of CFA gradually to be attached importance. However, given the variability of the amount of CFA generation, predicting it in advance is the basis to ensure effective disposal and rational utilization. In this study, CFA generation was taken as the target variable, three machine learning (ML) algorithms were used to construct the model, and four evaluation indices were used to evaluate its performance. The results showed that the DNN model with the R = 0.89, R2 = 0.77 on the testing set performed better than the traditional multiple linear regression equation and other ML algorithms, and the feasibility of DNN as the optimal model framework was demonstrated. Applying this model framework to the engineering field enables managers to identify the next step of the disposal method in advance, so as to rationally allocate ways of recycling and utilization to maximize the use and sales benefits of CFA while minimizing its disposal costs. In addition, sensitivity analysis further explains ML’s internal decisions and verifies that coal consumption is more important than installed capacity, which provides a certain reference for ensuring the rational utilization of CFA.

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Physicochemical and Engineering Properties of Coal Bottom and Fly Ash Mixtures Modified with Poly-Vinyl Alcohol and Sodium Lauryl Sulfate

Ayob,

Robert,

Mokhtar

et al. 2024

Lecture Notes in Civil Engineering

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Study on prediction fly ash generation using statistical method

Cited by 7 publications

References 5 publications

A Review on CSP and CPVT Technologies: Energy, Economic Analysis and Recycling towards Circular Economy

A Review on CSP and CPVT Technologies: Energy, Economic Analysis and Recycling towards Circular Economy

Comparison and Determination of Optimal Machine Learning Model for Predicting Generation of Coal Fly Ash

Physicochemical and Engineering Properties of Coal Bottom and Fly Ash Mixtures Modified with Poly-Vinyl Alcohol and Sodium Lauryl Sulfate

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