Optimal techno-economic and thermo-electrical design for a phase change material enhanced renewable energy driven polygeneration unit using a machine learning assisted lattice Boltzmann method

Sohani, Ali; Dehbashi, Mohsen; Delfani, Fatemeh; Hoseinzadeh, Siamak

doi:10.1016/j.enganabound.2023.04.027

Cited by 5 publications

(1 citation statement)

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“…The ANN algorithm can be used to solve different non-linear engineering problems 22 . McCulloch and Pitts 23 suggested the idea of this algorithm, which mimics the biological neural procedures.…”

Section: Methodsmentioning

confidence: 99%

A novel neural-evolutionary framework for predicting weight on the bit in drilling operations

Dowlatabadi,

Azizi,

Dehbashi

et al. 2023

Sci Rep

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This study compares the performance of artificial neural networks (ANN) trained by grey wolf optimization (GWO), biogeography-based optimization (BBO), and Levenberg–Marquardt (LM) to estimate the weight on bit (WOB). To this end, a dataset consisting of drilling depth, drill string rotational speed, rate of penetration, and volumetric flow rate as input variables and the WOB as a response is used to develop and validate the intelligent tools. The relevance test is applied to sort the strength of WOB dependency on the considered features. It was observed that the WOB has the highest linear correlation with the drilling depth and drill string rotational speed. After dividing the databank into the training and testing (4:1) parts, the proposed LM-ANN, GWO-ANN, and BBO-ANN ensembles are constructed. A sensitivity analysis is then carried out to find the most powerful structure of the models. Each model performs to reveal the relationship between the WOB and the mentioned independent factors. The performance of the models is finally evaluated by mean square error (MSE) and mean absolute error criteria. The results showed that both GWO and BBO algorithms effectively help the ANN to achieve a more accurate prediction of the WOB. Accordingly, the training MSEs decreased by 14.62% and 24.90%, respectively, by applying the GWO and BBO evolutionary algorithms. Meanwhile, these values were obtained as around 9.86% and 9.41% for the prediction error of the ANN in the testing phase. It was also deduced that the BBO performs more efficiently than the other technique. The effect of input variables dimension on the accuracy and training time of the BBO-ANN clarified that the most accurate WOB predictions are achieved when the model constructs with all four input variables instead of utilizing either three or two of them with the highest linear correlation. It was also observed that the training stage of the BBO-ANN model with four input variables needs a little more computational time than its training with either two or three variables. Finally, the accuracy of the BBO-ANN model for the WOB prediction has been compared with the multiple linear regression, support vector regression, adaptive neuro-fuzzy inference systems, and group method of data handling. The statistical accuracy analysis confirmed that the BBO-ANN is more accurate than the other checked techniques.

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Section: Methodsmentioning

confidence: 99%