کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
685370 889039 2008 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Neural fuzzy model applied to ethylene-glycol pulping of non-wood raw materials
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی تکنولوژی و شیمی فرآیندی
پیش نمایش صفحه اول مقاله
Neural fuzzy model applied to ethylene-glycol pulping of non-wood raw materials
چکیده انگلیسی

We studied the influence of the operational variables (viz. ethylene-glycol concentrations of 50–70%, temperatures of 155–185 °C, times of 30–90 min and numbers of PFI beating revolutions of 500–1500) on pulp yield and various paper properties (breaking length, stretch, burst index, tear index and brightness) obtained in the ethylene-glycol pulping of vine shoots, cotton stalks, leucaena (Leucaena leucocephala) and tagasaste (Chamaecytisus proliferus).The fuzzy neural network models used reproduced the experimental results with errors less than 15% and smaller than those provided by second-order polynomial models in all cases.An ethylene-glycol concentration of 65% at 180 °C for 75 min and 1500 PFI beating revolutions were found to provide substantial savings in energy, chemicals and facility investments as a result of operating under milder conditions than the strongest ones studied in this work. Tagasaste was found to be the most suitable raw material among those tested as it provided the paper sheets with the highest breaking length (4644 m), stretch (2.87%), burst index (2.46 kN/g), tear index (0.33 m Nm2/g) and brightness (40.92%); its pulp yield was also high (62.88%), which reflects efficient use of this raw material.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Bioresource Technology - Volume 99, Issue 5, March 2008, Pages 965–974
نویسندگان
, , , , ,