English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Refining is a critical step in the manufacturing of medium-density fiberboard (MDF). To ensure fiber quality and control of the energy consumption during refining, proper production parameters, such as feeding screw revolution speed (SR), accumulated chip height (CH), opening ratio of the discharge valve (OV), and content of Chinese poplar (CP), are vital. These parameters were monitored and recorded in an MDF mill to investigate the relationships between the parameters and the fiber quality and energy consumption. In this study, fuzzy models of the fiber quality and the energy consumption during refining were established based on subtractive clustering and an adaptive neuro-fuzzy inference system (ANFIS). The fiber quality and energy consumption models demonstrated high prediction accuracy because their predictive mean relative errors were as low as 4.14% and 6.72%, respectively. The errors of fiber quality were optimized using the simulated annealing method, and the input parameters were obtained. Based on the energy consumption model, the minimum energy consumption was 41.51 kWh/t, on the premise of the minimum requirement of fiber quality. This study can be a guideline for MDF production management to improve fiberboard quality and reduce energy consumption.

bioresources

Modeling and Optimization of Fiber Quality and Energy Consumption during Refining Based on Adaptive Neuro-fuzzy Inference System and Subtractive Clustering