PRODUCTION: THE CHOICE OF KEY INDICATORS AND THE KENOWLEDGE OF THE - - PowerPoint PPT Presentation

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.

Steam Power Bleached Pulp Bleaching Effluents

Very simplified…

The start... ...the goal! ...the way...

Electricity

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.

Dens., kg /m3 Extractives , % Lignin, % EA, % Yield.,% HexA, mMol /kg O2 Efficiency, % ClO2, kg/ tad *BPP, t/ ha.year A1 507.8e 1.23b 29.0bc 14.3bc 51.5b 44.4cde 48.0bc 9.2bc 10.0bc A2 459.0a 0.91a 29.5c 14.1bc 51.3ab 47.0e 45.7b 7.5a 9.0a B1 484.5bc 1.19b 28.2ab 13.5a 53.8cd 39.1a 49.7c 10.6de 10.5d B2 511.0e 2.31e 29.9c 15.4de 51.4ab 55.2f 41.6a 10.7de 10.6d C 493.0cd 1.93d 32.0d 15.7e 50.1a 53.4f 43.3a 9.2bc 9.6b D 510.5e 1.62c 29.6c 14.1bc 52.2b 40.8ab 48.2c 8.9b 9.7b E1 483.0bc 1.21b 29.4c 14.0b 52.6bc 40.3ab 53.5d 9.8bcd 10.2cd E2 538.3f 1.27b 29.4c 14.5c 51.9b 42.7bc 53.0d 10.2cd 11.2e F 474.0b 2.05de 29.7c 14.5c 54.3e 44.0cd 53.5d 11.5f 10.0bc G 505.0de 2.18de 28.9abc 15.2d 52.1b 45.7de 53.1d 10.8de 10.3cd H 471.6ab 1.25b 28.0a 13.5a 52.2b 38.7a 54.2d 9.3bc 9.6b

After evaluation of eight clones from different places

EA and Yield for kappa number 17.0

Extractives, % Lignin, % AE, % Yield.,% HexA, mMol/kg ClO2, kg/adt Dens., kg/m3

0.27 0.13 0.37

• 0.22

0.16 0.25

Extractives, %

0.42 0.76

• 0.05

0.58 0.57

Lignin, %

0.71

• 0.51

0.67

• 0.11

EA, %

• 0.52

0.85 0.23

Yield,%

• 0.55

0.49

HexA, mMol/kg

0.01

high content of extractives and lignin low alkaline load For #k:17.0

74.24% of the sample variance

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”

The neutralized and dried filtrate from Klason lignin; Is not possible conclude about the intense presence of aromatic groups FTIR spectrum

Compound % Phenol 3,48 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

PY-GC/MS analysis of dried extract from Klason lignin

Lignin related aromatics represent 3,39% of sample;

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.

~500 kg/m3 Chips Density 184,9 kg/m3 #kappa: 17,0 yield: 52,4% Rejects: 0,40% AEr: 9,29 g/L Pentosans: 14,5 % Solids: 100,5 t/h HV: 3390 Gcal/t Production: 80 t/h Wood feed 826,2 m3/h 152,7 t/h Chips Density

• 10%

166,4 kg/m3 Wood feed 826,2 m3/h 137,5 t/h #kappa: 15,9 Yeild: 51,6% Rejects: 0,29% AEr: 11,58 g/L Pentosans: 13,3 % Solids: 91,6 t/h PCS: 3210 Gcal/t Production: 71,0 t/h

S/G = S/G %lig = %lig %ext = %ext

A lab scale experiment

• 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.

Thank you!