Modeling and prediction of flotation performance using support vector regression

Despotović, Vladimir; Trumić, Maja S.; Trumić, Milan

doi:10.5937/ror1701031d

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…• Shabahzi et al [2] investigated the effect of process variables on the flotation rate and recovery using Random Forest; • Nakhaei and Irannajad [3] used various neural networks architectures in order to predict the metallurgical performance of the flotation column in a copper plant; • Despotoviç [4] built a Support Vector Regression model to estimate deinking flotation performance in the paper recycling process. Nevertheless, the majority of the previous works applied just train, validation and test steps in order to prove the concept and they did not report deploying them nor achieving online phase.…”

Section: Introductionmentioning

confidence: 99%

Soft Sensor: Machine Learning Tradicional Ou Deep Learning?

Drumond¹,

Kappes²,

Moraes³

et al. 2018

ABM Proceedings

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Resumo Para atingir a qualidade desejada no concentrado da flotação, numa usina de mineração de ferro, o operador tomava decisões com latência mínima de 2 horas, o equivalente ao tempo para recebimento de resultados da análise laboratorial. Um tempo morto tão elevado, tornava difícil colocar a planta em regime quando ocorria uma variação na alimentação da flotação ou na qualidade desejada. Dessa forma, o objetivo do presente trabalho foi construir um sensor virtual capaz de estimar o teor SiO2 e permitir a atuação do operador em um período mais curto. A alta variabilidade na alimentação, adicionada às dificuldades inerentes da predição envolvendo séries temporais tornaram o projeto singular. Foram aplicadas técnicas de mineração de dados e seleção de features em uma base de 03 meses abrangendo variáveis da Flotação e também da Deslamagem, e obtiveram-se entradas para elaboração de 03 modelos de Machine Learning: Random Forest, GradientBoostedTrees e MultiLayerPerceptron. O Soft Sensor baseado em redes neurais artificiais se mostrou estatisticamente mais eficiente na etapa de teste. Na versão online, o baixo erro médio absoluto obtido comprovou a robustez do modelo, entregou agilidade para a operação e certificou o poder dessa abordagem em processos industriais com alta latência de resultados laboratoriais.

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Section: Introductionmentioning

confidence: 99%

Soft Sensor: Machine Learning Tradicional Ou Deep Learning?

Drumond¹,

Kappes²,

Moraes³

et al. 2018

ABM Proceedings

View full text Add to dashboard Cite

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Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques

Gavrilović,

Despotović,

Zot

et al. 2024

Applied Sciences

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Flotation deinking is one of the most widely used techniques for the separation of ink particles from cellulose fibers during the process of paper recycling. It is a complex process influenced by a variety of factors, and is difficult to represent and usually results in models that are inconvenient to implement and/or interpret. In this paper, a comprehensive study of several machine learning methods for the prediction of flotation deinking performance is carried out, including support vector regression, regression tree ensembles (random forests and boosting) and Gaussian process regression. The prediction relies on the development of a limited dataset that assumes representative data samples obtained under a variety of laboratory conditions, including different reagents, pH values and flotation residence times. The results obtained in this paper confirm that the machine learning methods enable the accurate prediction of flotation deinking performance even when the dataset used for training the model is limited, thus enabling the determination of optimal conditions for the paper recycling process, with only minimal costs and effort. Considering the low complexity of the Gaussian process regression compared to the aforementioned ensemble models, it should be emphasized that the Gaussian process regression gave the best performance in estimating fiber recovery (R2 = 97.77%) and a reasonable performance in estimating the toner recovery (R2 = 86.31%).

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Modeling and prediction of flotation performance using support vector regression

Cited by 2 publications

References 5 publications

Soft Sensor: Machine Learning Tradicional Ou Deep Learning?

Soft Sensor: Machine Learning Tradicional Ou Deep Learning?

Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques

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