EUCALYPTUS WOOD EVALUATION FOR PULP PRODUCTION: THE CHOICE OF KEY INDICATORS AND THE KENOWLEDGE OF THE VARIABLES ROLE ON THE PROCESSES AS A TOOL FOR RAISING THE PRODUCTIVITY
Autors Leonardo S. Caux Leandro C. Dalvi Jorge L. Colodette All CENIBRA Research team, the industrial and forestry group.
Very simplified… ...the way... ...the goal! The start... Bleaching Bleached Pulp Effluents Power Electricity Steam
Some topics • How to translate the wood information to the industry ? • Which The key wood information? • To maximize the industrial an forestry productivity. • What about the information quality? • Variability; • Frequency.
Some topics The wood storage: Cost/time/quality; Density Variability: Wood density against chips density The volumetric feed of digesters; Technological aspects of wood: The wood chemistry; The industrial wood performance; The relationship between variables.
Some topics Thinking about those three topics CENIBRA have been studying about: Wood storage to find the equilibrium between the quality and cost of wood for transportation logistic and industrial performance; Stability of density and faster analytical methods; Better (and fast…) evaluation of wood chemistry; Extractives; Lignin; Carbohydrates; Fiber quality.
After evaluation of eight clones from different places Dens., kg Extractives Yield.,% HexA, mMol O 2 Efficiency, ClO 2 , kg/ *BPP, t/ Lignin, % EA, % /m 3 , % /kg % tad ha.year A1 507.8 e 1.23 b 29.0 bc 14.3 bc 51.5 b 44.4 cde 48.0 bc 9.2 bc 10.0 bc A2 459.0 a 0.91 a 29.5 c 14.1 bc 51.3 ab 47.0 e 45.7 b 7.5 a 9.0 a B1 484.5 bc 1.19 b 28.2 ab 13.5 a 53.8 cd 39.1 a 49.7 c 10.6 de 10.5 d B2 511.0 e 2.31 e 29.9 c 15.4 de 51.4 ab 55.2 f 41.6 a 10.7 de 10.6 d C 493.0 cd 1.93 d 32.0 d 15.7 e 50.1 a 53.4 f 43.3 a 9.2 bc 9.6 b D 510.5 e 1.62 c 29.6 c 14.1 bc 52.2 b 40.8 ab 48.2 c 8.9 b 9.7 b E1 483.0 bc 1.21 b 29.4 c 14.0 b 52.6 bc 40.3 ab 53.5 d 9.8 bcd 10.2 cd E2 538.3 f 1.27 b 29.4 c 14.5 c 51.9 b 42.7 bc 53.0 d 10.2 cd 11.2 e F 474.0 b 2.05 de 29.7 c 14.5 c 54.3 e 44.0 cd 53.5 d 11.5 f 10.0 bc G 505.0 de 2.18 de 28.9 abc 15.2 d 52.1 b 45.7 de 53.1 d 10.8 de 10.3 cd H 471.6 ab 1.25 b 28.0 a 13.5 a 52.2 b 38.7 a 54.2 d 9.3 bc 9.6 b EA and Yield for kappa number 17.0
Extractives, HexA, ClO 2 , Lignin, % AE, % Yield.,% % mMol/kg kg/adt 0.27 0.13 0.37 -0.22 0.16 0.25 Dens., kg/m 3 Extractives, % 0.42 0.76 -0.05 0.58 0.57 0.71 -0.51 0.67 -0.11 Lignin, % -0.52 0.85 0.23 EA, % -0.55 0.49 Yield,% HexA, 0.01 mMol/kg
74.24% of the sample variance high content of extractives and lignin low alkaline load For #k:17.0
First conclusions The relationship of wood characteristics may be used to evaluate their potential; The wood chemistry showed strong effect on wood performance; Lignin content; Extractives content.
Another questions… However, what about the lignin chemistry and the analytical methods for hardwood lignin determination? What the connection with S/G ratio? How true is the information coming from the quantitative analysis of lignin? Acid insoluble lignin, the Klason method; And acid soluble lignin. 35 industrial sorted wood samples were selected to evaluate the lignin chemistry.
Syringyl (S) and guaiacyl (G) ratio was performed by pyrolysis coupled to the gas chromatography and mass spectrometry (PY-GC/MS)
In a statistical approach, PLS The S markers always presented positive correlation with soluble lignin; The G markers always presented positive correlation with insoluble lignin; Why? The complex lignin-carbohydrate (CLC) may be one of the causes…
The acid “soluble lignin” FTIR spectrum The neutralized and dried filtrate from Klason lignin; Is not possible conclude about the intense presence of aromatic groups
PY-GC/MS analysis of dried extract from Klason lignin Compound % Lignin related aromatics Phenol 3,48 represent 3,39% of sample; 3-Methyl-Phenol 1,75 2-Methyl-Phenol 2,18 Guaiacol (methoxi-Phenol) 0,66 3,4 Dimethyl-Phenol 0,37 1,2 Benzenodiol 2,1 3-Methyl-1,2-Benzenodiol 1,23 Siringol (2,6-Dimethoxi-Phenol) 2,73
After all… We can forget the density? The feeding of the process is volumetric; The mass and volume ratio keep essential for the digesters; The trouble is the relationship between wood density and chip density; The sampling is not sufficient and the process have a “blind time” ; In this cases wood chemistry is not responsible for fluctuations.
A lab scale experiment S/G = S/G %lig = %lig Wood feed Wood feed %ext = %ext 826,2 m 3 /h 826,2 m 3 /h 152,7 t/h 137,5 t/h ~500 kg/m 3 Chips Density #kappa: 17,0 #kappa: 15,9 -10% yield: 52,4% Yeild: 51,6% 166,4 kg/m 3 Rejects: 0,40% Rejects: 0,29% AEr: 9,29 g/L AEr: 11,58 g/L Pentosans: 14,5 % Pentosans: 13,3 % Chips Solids: 100,5 t/h Solids: 91,6 t/h Density HV: 3390 Gcal/t PCS: 3210 Gcal/t 184,9 kg/m 3 Production: 80 t/h Production: 71,0 t/h -11,3%
Conclusions The pre-evaluation of clones available for processing, associated to appropriate statistical techniques, may provide significant benefit to the mill; The results also showed that the density is not the best parameter to evaluate the wood quality; The wood chemistry is significant and has an important role in the mill result.
Conclusions The lignin content represent an important role for pulping; The extract of Klason lignin, historically called acid soluble lignin, present good relationship with syringyl units peaks from PY-GC/MS; However, this extract represented less then 4% of lignin structures; The “ Klason soluble lignin” is not suitable to relate the lignin content; Over estimated lignin.
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