Rapid Determination of Holocellulose and Lignin in Wood by Near Infrared Spectroscopy and Kernel Extreme Learning Machine

Yang, Hao; Liu, Yaoyao; Xiong, Zhixin; Liang, Long

doi:10.1080/00032719.2019.1700267

Cited by 16 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventionally, reliable measurements of chip moisture, lignin and carbohydrate contents, white liquor EA, sulfidity, residual alkali, pulp consistency and end-point Kappa number, have mostly been achieved by laboratory testing and rarely by in-situ testing. Nonetheless, advanced measurement techniques for reliable online measurements of important process parameters are emerging [119][120][121]. Till date, most of these advanced sensors have primarily been used for process monitoring rather than process control.…”

Section: Discussion and Future Research Directions Of Pulp Digesters mentioning

confidence: 99%

A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters

2020

View full text Add to dashboard Cite

Being at the heart of modern pulp mills, continuous pulp digesters have attracted much attention from the research community. In this article, a comprehensive review in the area of modeling, control and diagnostics of continuous pulp digesters is conducted. The evolution of research focus within these areas is followed and discussed. Particular effort has been devoted to identifying the state-of-the-art and the research gap in a summarized way. Finally, the current and future research directions in the areas have been analyzed and discussed. To date, digester modeling following the Purdue approach, Kappa number control using model predictive controllers and health index-based diagnostic approaches by utilizing different statistical methods have dominated the field. While the rising research interest within the field is evident, we anticipate further developments in advanced sensors and integration of these sensors for improving model prediction and controller performance; and the exploration of different AI-based approaches will be at the core of future research.

show abstract

Section: Discussion and Future Research Directions Of Pulp Digesters mentioning

confidence: 99%

A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters

2020

View full text Add to dashboard Cite

show abstract

“…This work adopts the radial basis function (RBF) in the kernel functions of K-ELM, SVR and SKR as (7) where is the kernel parameter. K-ELM has been widely used to analyse different types of spectral data due to its fast computational speed and good generalization ability (Yan et al, 2017;Yang, Liu, Xiong, & Liang, 2020).…”

Section: Kernel Extreme Learning Machine (K-elm)mentioning

confidence: 99%

Spectral knowledge-based regression for laser-induced breakdown spectroscopy quantitative analysis

Song

Afgan

Yun

et al. 2022

Expert Systems with Applications

View full text Add to dashboard Cite

“…Compared with the basic extreme learning machine (ELM) algorithm, it has a stronger ability to solve the regression pre-diction problem [20]. Compared with back propagation neural network (BPNN) and support vector machines (SVM), it has a faster calculation speed and greatly improves the generalization ability of the network when obtaining better or similar prediction accuracy [21]. The KELM algorithm has been proved to have excellent prediction performance in many fields [22], but its performance is vulnerable to the influence of penalty coefficient C and kernel parameter σ .…”

Section: Introductionmentioning

confidence: 99%

Sustainability Intelligent Evaluation of Regional Microgrid Interconnection System Based on Combination Entropy Weight Rank Order-TOPSIS and NILA-KELM

Wang¹,

Fan²,

Meng³

et al. 2022

Energy Engineering

View full text Add to dashboard Cite

Sustainability evaluation of regional microgrid interconnection system is conducive to a profound and comprehensive understanding of the impact of interconnection system projects. In order to realize the comprehensive and scientific intelligent evaluation of the system, this paper proposes an evaluation model based on combination entropy weight rank order-technique for order preference by similarity to an ideal solution (TOPSIS) and Niche Immune Lion Algorithm-Extreme Learning Machine with Kernel (NILA-KELM). Firstly, the sustainability evaluation indicator system of the regional microgrid interconnection system is constructed from four aspects of economic, environmental, social, and technical characteristics, and the evaluation indicators are explained. Then, the classical evaluation model based on TOPSIS is constructed, and the entropy weight method and rank order method (RO) are coupled to obtain the indicator weight. The niche immune algorithm is used to improve the lion algorithm, and the improved lion algorithm is used to optimize the parameters of KELM, and the intelligent evaluation model based on NILA-KELM is obtained to realize fast real-time calculation. Finally, the scientificity and accuracy of the model proposed in this paper are verified. The model proposed in this paper has the lowest RMSE, MAE and RE values, indicating that its intelligent evaluation results are the most accurate. This study is conducive to the horizontal comparison of the overall performance of regional microgrid interconnection system projects, helps investors to choose the most promising project scheme, and helps the government to find feasible project.

show abstract

Rapid Determination of Holocellulose and Lignin in Wood by Near Infrared Spectroscopy and Kernel Extreme Learning Machine

Cited by 16 publications

References 24 publications

A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters

A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters

Spectral knowledge-based regression for laser-induced breakdown spectroscopy quantitative analysis

Sustainability Intelligent Evaluation of Regional Microgrid Interconnection System Based on Combination Entropy Weight Rank Order-TOPSIS and NILA-KELM

Contact Info

Product

Resources

About