Applying feature selection and machine learning techniques to estimate the biomass higher heating value

Abdollahi, Seyyed Amirreza; Ranjbar, Seyyed Faramarz; Razeghi Jahromi, Dorsa

doi:10.1038/s41598-023-43496-x

Cited by 7 publications

(6 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section compares the BBO-ANN accuracy toward the WOB prediction with MLR 37 , SVR 38 , ANFIS 39 , and GMDH 40 . Equation ( 14 ) introduces the linear correlation between WOB and all four input variables.…”

Section: Resultsmentioning

confidence: 99%

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

Dowlatabadi,

Azizi,

Dehbashi

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Dowlatabadi,

Azizi,

Dehbashi

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Models such as ANNs often achieve high R 2 values (>0.90) and lower RMSE and MAE values, demonstrating their ability to estimate by managing the data [46,47]. On the other hand, it should be noted that although high accuracy and lower modeling errors are achieved, longer runtimes are often required due to higher learning complexity [48], which can be a limiting factor when computational resources are limited. On the other hand, other ML models that have simpler structures provide faster results due to their (relative) simplicity, although they are not as accurate in estimation [48].…”

Section: Discussionmentioning

confidence: 99%

Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models

Brandić,

Pezo,

Voća

et al. 2024

Energies

View full text Add to dashboard Cite

The research conducted focused on the capabilities of various non-linear and machine learning (ML) models in estimating the higher heating value (HHV) of biomass using proximate analysis data as inputs. The research was carried out to identify the most appropriate model for the estimation of HHV, which was determined by a statistical analysis of the modeling error. In this sense, artificial neural networks (ANNs), support vector machine (SVM), random forest regression (RFR), and higher-degree polynomial models were compared. After statistical analysis of the modeling error, the ANN model was found to be the most suitable for estimating the HHV biomass and showed the highest specific regression coefficient, with an R2 of 0.92. SVM (R2 = 0.81), RFR, and polynomial models (R2 = 0.84), on the other hand, also exhibit a high degree of estimation, albeit with somewhat larger modelling errors. The study conducted suggests that ANN models are best suited for the non-linear modeling of HHV of biomass, as they can generalize and search for links between input and output data that are more robust but also more complex in structure.

show abstract

“…[ 152 ] ML is increasingly being utilized in the assessment of gas separation membrane performance according to several recent articles. [ 153–155 ] However, perhaps the most studied NM‐based separation application using ML in recent times is filtration—namely ultrafiltration, nanofiltration, and reverse osmosis.…”

Section: From Predictive Power To Automated Analysis: the Role Of Mac...mentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

show abstract

Applying feature selection and machine learning techniques to estimate the biomass higher heating value

Cited by 7 publications

References 60 publications

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

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

Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Contact Info

Product

Resources

About