Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes

Elveny, Marischa; Hosseini, Meysam; Chen, Tzu-Chia; Lawal, Adedoyin Isola; Alizadeh, Seyed Mehdi Seyed

doi:10.1155/2021/7332776

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…ELM is a type of machine learning model [ 30 ] that has several advantages over traditional machine learning models [ 30 ]. By randomly generating the weight and bias of the hidden layer [ 30 , 47 ], the ELM model exhibits excellent generalization and faster learning speed [ 33 ]. Moreover, the ELM model is easy to develop and achieves the smallest training error and smallest norm of weights [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study

Zhao

Zhang

2022

BMC Infect Dis

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Background Human brucellosis is a serious public health concern in China. The objective of this study is to develop a suitable model for forecasting human brucellosis cases in mainland China. Methods Data on monthly human brucellosis cases from January 2012 to December 2021 in 31 provinces and municipalities in mainland China were obtained from the National Health Commission of the People’s Republic of China website. The TBATS and ELM models were constructed. The MAE, MSE, MAPE, and RMSE were calculated to evaluate the prediction performance of the two models. Results The optimal TBATS model was TBATS (1, {0,0}, -, {< 12,4 >}) and the lowest AIC value was 1854.703. In the optimal TBATS model, {0,0} represents the ARIMA (0,0) model, {< 12,4 >} are the parameters of the seasonal periods and the corresponding number of Fourier terms, respectively, and the parameters of the Box-Cox transformation ω are 1. The optimal ELM model hidden layer number was 33 and the R-squared value was 0.89. The ELM model provided lower values of MAE, MSE, MAPE, and RMSE for both the fitting and forecasting performance. Conclusions The results suggest that the forecasting performance of ELM model outperforms the TBATS model in predicting human brucellosis between January 2012 and December 2021 in mainland China. Forecasts of the ELM model can help provide early warnings and more effective prevention and control measures for human brucellosis in mainland China.

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Section: Discussionmentioning

confidence: 99%

The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study

Zhao

Zhang

2022

BMC Infect Dis

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“…This dual-stage approach is developed with minimum cost with very less dynamic model constitution. The accurate biogas prediction was developed by De et al 13 that utilized the neural network model namely k-nearest neighbors (KNN) for the effective production of biofuel. Here the forecasting accuracy is very high with improved facility performances.…”

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confidence: 99%

“…Elveny et al 13 presented a novel Machine Learning (ML) technique dependent upon Extreme Learning Machine (ELM) for modelling this essential value. The real database involving 483 actual datasets has been related to the output forecasted with ELM technique.…”

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confidence: 99%

Design of optimal Elman Recurrent Neural Network based prediction approach for biofuel production

Kumar¹,

Vijayabaskar²,

Murali³

et al. 2023

Sci Rep

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Renewable sources like biofuels have gained significant attention to meet the rising demands of energy supply. Biofuels find useful in several domains of energy generation such as electricity, power, or transportation. Due to the environmental benefits of biofuel, it has gained significant attention in the automotive fuel market. Since the handiness of biofuels become essential, effective models are required to handle and predict the biofuel production in realtime. Deep learning techniques have become a significant technique to model and optimize bioprocesses. In this view, this study designs a new optimal Elman Recurrent Neural Network (OERNN) based prediction model for biofuel prediction, called OERNN-BPP. The OERNN-BPP technique pre-processes the raw data by the use of empirical mode decomposition and fine to coarse reconstruction model. In addition, ERNN model is applied to predict the productivity of biofuel. In order to improve the predictive performance of the ERNN model, a hyperparameter optimization process takes place using political optimizer (PO). The PO is used to optimally select the hyper parameters of the ERNN such as learning rate, batch size, momentum, and weight decay. On the benchmark dataset, a sizable number of simulations are run, and the outcomes are examined from several angles. The simulation results demonstrated the suggested model's advantage over more current methods for estimating the output of biofuels.

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Corrigendum to “Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes”

Elveny

Hosseini

Chen³

et al. 2022

BioMed Research International

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Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes

Cited by 3 publications

References 37 publications

The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study

The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study

Design of optimal Elman Recurrent Neural Network based prediction approach for biofuel production

Corrigendum to “Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes”

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