Prediction of pellet quality through machine learning techniques and near-infrared spectroscopy

Mancini, Manuela; Mircoli, Alex; Potena, Domenico; Diamantini, Claudia; Duca, Daniele; Toscano, Giuseppe

doi:10.1016/j.cie.2020.106566

Cited by 29 publications

(9 citation statements)

References 35 publications

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“…The possibility to trace pellet quality is important, since fraud behaviors could impact consumers’ health. Usual pellet analyses are costly and time-consuming Mancini and coworkers defined a handheld-NIRs approach to propose a fast and automatic classification of pellet ( Mancini et al, 2020 ).…”

Section: Other Applicationsmentioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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Section: Other Applicationsmentioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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“…A screw press pelletizing machine was manufactured for waste biomass materials like oil palm wastes to create pellets from a powder or molten mixture [8]. A method was provided for quick and inexpensive evaluation of pellet quality that can be applied throughout the supply chain [9]. A machine for making cassava mash pellets at a cottage level was designed, fabricated and tested [10].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Design and Analysis of Various Components of LEPTOCHLOA FUSCA Pelletizing Machine

Sunny Kumar,

Pathan,

Jamali

et al. 2022

JAET

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This paper places a strong emphasis on the development of agriculture-based technology, which will motivate farmers and owners of subpar property to raise cattle. The paper discusses the material safety factor and fatigue life for manufacturing of pelletizing machine for leptochloa fusca. The emphasis of this technology is to pelletize the leptochloa fusca (Kallar Grass). The initiative focuses on the advancement of agriculturally based technology. The fatigue life and factor of safety of the main components of the pelletizing machine were examined in ANSYS software by using a sample of SolidWorks. Analysis of factor of safety and fatigue life represents that material is safe and can undergo 1×106 cyclic load, hence material is safe for the manufacturing of pelletizing machine for leptochloa fusca.

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“…Francik et al (2020) [18] developed a decision support system using artificial neural networks (ANNs) to indicate the optimal parameters for the total energy consumption required for the production process of Miscanthus and willow briquettes. Mancini et al (2020) [19] studied a methodology based on machine learning-based classification of pellet spectra. Mungale et al (2016) [20] examined the total briquette weight following mixing to provide a model (ANN simulation) formulation for the briquette-making procedure.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

Kabas,

Ercan,

Dinca

2024

Applied Sciences

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Briquetting is a compaction technology that has been used for many years to produce raw materials that are uniform in size and moisture content and are easy to process, transport and store. The physical and chemical properties of the raw material and the briquetting conditions also affect the density and strength of the briquettes. Nonetheless, assessing the quality of briquettes is challenging and extremely expensive, and necessitates lengthy laboratory investigations. In this study, a fast, cost-effective, and simple method using machine learning was used to evaluate the quality characteristics of briquette samples. The deformation energy, one of the most important briquette quality parameters, was predicted by machine learning methods, considering specific compression force, moisture content, compression resistance, briquette density, tumbler index, water resistance, shatter index and compression stress. For this purpose, Random Forest, Extreme Gradient Boosting, and CatBoost methods, which are among the ensemble learning methods, were used. The RMSE, MAE, MAPE, and R2 metrics were used to evaluate the models. With respect to the training data, the model created using the Extreme Gradient Boosting method was successful on all the metrics. However, for test data, the best RMSE (15.69), MAPE (0.0146), and R2 (0.9715) were obtained from the model established with the CatBoost method. The best MAE (10.63) was obtained from the model established with the Random Forest method. The metric results and the graphs obtained from the prediction values of the models revealed that machine learning methods were successfully able to predict briquette deformation energy.

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Prediction of pellet quality through machine learning techniques and near-infrared spectroscopy

Cited by 29 publications

References 35 publications

Portable NIR spectroscopy: the route to green analytical chemistry

Portable NIR spectroscopy: the route to green analytical chemistry

Design and Analysis of Various Components of LEPTOCHLOA FUSCA Pelletizing Machine

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

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