Development and application of ANN model for property prediction of supercritical kerosene

Li, Bo; Lee, Yachao; Yao, Wei; Lu, Yang; Fan, Xuejun

doi:10.1016/j.compfluid.2020.104665

Cited by 24 publications

(5 citation statements)

References 44 publications

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“…Weights and biases are the adjustable parameters of the model and control the inuence of the processing in the form of signals. 29,30 The idea of articial intelligence that formulates the relationship between the human brain and the nervous system was used to develop the ANN model. 31,32 The following function (wX + b) is transferred to the activation function in the hidden layers where non-linearity is introduced.…”

Section: Design Of Experiments Using Response Surface Methodologymentioning

confidence: 99%

Optimizing methanol synthesis from CO₂ using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Kumar,

Nayak,

Chowdhury

et al. 2024

RSC Adv.

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Section: Design Of Experiments Using Response Surface Methodologymentioning

confidence: 99%

Optimizing methanol synthesis from CO₂ using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Kumar,

Nayak,

Chowdhury

et al. 2024

RSC Adv.

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“…Machine learning algorithms are more widely used in state prediction, including neural networks, support vector machines, and decision forests. Among them, an artificial neural network has better durability and timeliness [29]. It has a high degree of self-learning, adaptability, and error-proofing ability [30].…”

Section: Digital Twin Framework For Building Operationsmentioning

confidence: 99%

Construction Theory for a Building Intelligent Operation and Maintenance System Based on Digital Twins and Machine Learning

et al. 2022

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The operation and maintenance (O&M) of buildings plays an important role in ensuring that the buildings work normally, as well as reducing the damage caused by functional errors. There are obvious problems in the traditional O&M modality, and an effective way to solve them is to make the model smarter. In this paper, a digital twin framework for building operation is proposed, which consists of two key components: a digital twin O&M model and a machine learning algorithm. The process of establishing the digital twin model is introduced in detail, and the method is explained according to the structure, equipment, and energy consumption characteristics of the model. A mechanism of fusing the digital twin and machine learning algorithm is proposed and the prediction process based on an artificial neural network (ANN) is shown. Finally, based on a systematic summary of the modeling process and fusion mechanism, the development path and overall structure of the intelligent O&M system utilizing digital twins is proposed.

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“…Artificial Neural Network (ANN) is an algorithm that has the ability to study a set of input-output the subject, then predicts the output for the new sample set at high speed and with a reasonable degree of accuracy [19]. In the ANN classification using multi-layer perceptrons, multi-layer perceptrons are often used because of their flexibility and ability to adapt various non-linear with a high degree of accuracy [20].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Performance Analysis of Air Pollution Classification Prediction Map with Decision Tree and ANN

Ramadhani

Prasetiyowati

Sibaroni

2022

JoSYC

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Jakarta is a city in Indonesia that has a high population density that must pay attention to its health condition. Good air quality provides positive benefits to support public health so that they can be more productive at work and create fresh and healthy air. This study uses Machine Learning to classify air based on certain attributes. Then, the development of a prediction model based on time data is designed to produce a predictive map of air pollution in Jakarta area for the next 3 years. The methods applied are Decision Tree and Artificial Neural Networks. As a result, the Decision Tree and Artificial Neural Network models show very good accuracy for predictions from 2024 to 2026. The Decision Tree and Artificial Neural Network models get an accuracy of 98% and 94%. In 2025 the Decision Tree and Artificial Neural Network models get 99% and 93% accuracy. In 2026 the Decision Tree and Artificial Neural Network models get an accuracy of 94% and 93% which can be seen from the Decision Tree model which is superior to the Artificial Neural Network with a difference of 1 - 6%.

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Development and application of ANN model for property prediction of supercritical kerosene

Cited by 24 publications

References 44 publications

Optimizing methanol synthesis from CO₂ using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Optimizing methanol synthesis from CO₂ using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Construction Theory for a Building Intelligent Operation and Maintenance System Based on Digital Twins and Machine Learning

Performance Analysis of Air Pollution Classification Prediction Map with Decision Tree and ANN

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Development and application of ANN model for property prediction of supercritical kerosene

Cited by 24 publications

References 44 publications

Optimizing methanol synthesis from CO2 using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Optimizing methanol synthesis from CO2 using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Construction Theory for a Building Intelligent Operation and Maintenance System Based on Digital Twins and Machine Learning

Performance Analysis of Air Pollution Classification Prediction Map with Decision Tree and ANN

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Product

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Optimizing methanol synthesis from CO₂ using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Optimizing methanol synthesis from CO₂ using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability