kshama thesis1 (repaired)

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CHAPTER 1

INTRODUCTION

Black liquor is the spent liquid after the pulping process from the pulp mill. It is very complex,

contains high alkalinity and high dissolved solids such as lignin residues, degraded

carbohydrates and inorganic constituent. Black liquor is often called a waste although it contains

valuable energy and most importantly, converted expensive inorganic cooking chemicals. It is

conventionally burned in a large unit called Kraft recovery boiler. For the production of paper,

approximately 4 ton of wood, 200-250 kg chemicals & 8-10 ton of water are required which will

produce 1 ton of pulp and 10 ton of weak black liquor. If this liquor is treated it gives economic

and environmental benefits by way of:

Chemical recovery (caustic soda)

Energy recovery

Less pollution load

Unconventional energy production, from black liquor with surplus energy generation

Although the pulping chemical in the Kraft pulping process is only sodium hydroxide and

sodium carbonate, the discharging of this spent liquor/black liquor into the downstream without

proper treatment will definitely bring serious water pollution in term of biological oxygendemand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), colour unit, pH,

odour and etc [1]. In the US, 80 million tons of black liquor (calculated for 25% water content

liquor) is burnt in recovery boilers every year. That corresponds to over 1% of the US total heat

and power generation [2]. In Northern Europe, black liquor plays even a more important role. In

Finland and Sweden black liquor combustion covers 7 and 4.5% of the total heat and power

generation, respectively. This corresponds in both cases to roughly 30% of the heat and power

generated from biofuels [3].



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Recovery boiler have many problems including smelt-water explosions, unsteady smelt run off,

poor smelt reduction, low steam production, fouling of heat transfer tubes and plugging of flue

gas passages. So it is required to have a prior knowledge of kinetics of combustion of black

liquor when it is fired into the recovery boiler. Devolatilization is an important step in the

combustion of black liquor. Devolatilization refers to the rapid decomposition of organic

material into gaseous compounds and char. The devolatilization reactions affect the amount and

composition of black liquor char and the gas composition in the lower furnace.

Kinetic knowledge is of great importance in achieving good control of the combustion process

and in optimizing system design. Combustion model requires the proper understanding of

physical and chemical processes taking place in the particle during combustion. Kinetic model

should be developed that can predict the experimentally observed combustion behavior and can

be able to transfer experimental observations to furnace environment.

1.1.Outline of the Dissertation

Chapter 1 includes the brief introduction about the importance of Black liquor, how is it

generated and why is it necessary to the kinetics knowledge of devolatilization.

Chapter 2 gives the overview of pulping process and recovery section.

Chapter 3 includes the literature survey. In this chapter, the scenario of pulp and paper

industry has been discussed; black liquor composition and various models used for the

pyrolysis of black liquor are outlined.

Chapter 4 includes the experimental work performed for the purpose of study of

devolatilization of Black Liquor solids and also includes the results of neural network and

response surface methodology.

Chapter 5 includes the conclusion for the dissertation.



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CHAPTER 2

PULPING PROCESS OVERVIEW

Wood is ground and pulped to separate the fibres from each other and to suspend the fibres in

water. Pulping breaks apart the wood fibres and cleans them of unwanted residues. In chemical

pulping, wood chips are cooked in an aqueous solution at high temperature and pressure.

Chemical processes dissolve most of the glue that holds the fibres together (lignin) while leaving

the cellulose fibres relatively undamaged. Chemical pulping produces a waste stream of

inorganic chemicals and wood residues known as black liquor . The Black liquor is concentrated

in evaporators and then incinerated in recovery furnaces, many of which are connected to steam

turbine cogeneration systems. The wood residues provide the fuel and the chemicals areseparated as smelt which is then treated to produce sodium hydroxide. In Figure 2.1 a simple

schematic description for the production of pulp and black liquor is shown.

Figure 2.1: Schematic representation of pulping process



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2.1.Types of Pulping Processes

The manufacture of pulp for paper and cardboard employs mechanical (including thermo-mechanical), chemi-mechanical, and chemical methods.

2.1.1.

Mechanical pulping

The two processes of mechanical pulping are stone ground wood (fiber is torn from the

side of short logs with grindstones) and refiner ground wood (wood chips are passed

through disc refiners). Mechanical pulping does not separate the cellulose fiber from the

lignin in the wood, while chemical pulping does. In chemi-mechanical pulping, the wood is

softened with chemicals before grinding. There is also a process called thermo-mechanical

pulping, where the wood is softened with heat and then disc-refined under pressure.

2.1.2.

Chemical pulping

Chemical pulping uses chemicals to separate the cellulose from the lignin. This creates

pulps with different properties that can be used for higher quality paper. The three types of

chemical pulping are Kraft, Sulfite, and semi-chemical.

The Kraft process, also called the sulfate process, involves an alkaline treatment with

solutions of sodium sulfide and sodium hydroxide. Sulfite pulping began with the use of

calcium as the sulfite liquor base. Today the base has been changed to magnesium,

ammonia, or sodium. This allows for the recovery of spent liquor.

The semi-chemical pulping process involves cooking wood chips in a neutral solution of

sodium sulfite and sodium carbonate or sodium hydroxide. Only some of the lignin is

removed, and then the pulp undergoes mechanical disintegration. Comparison of all

pulping processes is presented in Table 2.1.



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Table 2.1 Comparison of the different types of pulping [4]

Kraft: chemical Sulfite: chemicalCTMP: chemi-

mechanical

Ground wood:

mechanical

yield of pulp 40-42% same as Kraft 90-92% about 80%

strength of

fibers

higher thanmechanical,lower than CTMP

lower than Kraft highest lowest strength

color/ brightness

more effective bleaching

methods have been developed

lower than Kraft high poor

cost

higher thanmechanical or

CTMP

lower than Kraftlower than

chemical

low

water use

depends on waterreuse, higher thanmechanical orCTMP

same as Kraftlower thanchemical

low

uses

fine paper, paperboard,

cartons,magazines, etc.

same as Kraft same as Kraft

low quality paper,newspaper,

tissues, towels,etc.

advantages

better than

Groundwood andsulfite, betterrecovery ofchemicals

better thanGroundwoodtechnique

less processingrequired, range ofwood species used

printing

disadvantages

toxic wastestreams (caused by bleaching,

odor

same as Kraft

highlyconcentratedeffluents, worse

than chemical

low strength,impermanent paper (reactive)



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2.2.Chemical Recovery

For economic and environmental reasons, chemical and semi-chemical pulp mills employ

chemical recovery processes to reclaim spent cooking chemicals from the pulping process. At

Kraft and soda pulp mills, spent cooking liquor, referred to as “weak black liquor,” from the

brown stock washers is routed to the chemical recovery area at Kraft and soda pulp mills. The

chemical recovery process involves concentrating weak black liquor, combusting organic

compounds, reducing inorganic compounds, and reconstituting the cooking liquor. The typical

Kraft chemical recovery process consists of the general steps described in the following

paragraphs [5].

Black liquor concentration:

Residual weak black liquor from the pulping process is a dilute solution (approximately 12 to

15 percent solids) of wood lignins, organic materials, oxidized inorganic compounds (sodium

sulfate [Na2SO4], Na2CO3), and white liquor (Na2S and NaOH). The weak black liquor is first

directed through a series of multiple-effect evaporators (MEEs) to increase the solids content

to about 50 percent to form “strong black liquor.” The strong black liquor from the MEE

system is either oxidized in the black liquor oxidation (BLO) system if it is further

concentrated in a direct contact evaporator (DCE) or routed directly to a non-direct contact

evaporator (NDCE), also called a concentrator. Oxidation of the black liquor prior toevaporation in a DCE reduces emissions of odorous total reduced sulfur (TRS) compounds,

which are stripped from the black liquor in the DCE when it contacts hot flue gases from the

recovery furnace. The solids content of the black liquor following the final

evaporator/concentrator typically averages 65 to 80 percent [6].

Recovery furnace

The concentrated black liquor is then sprayed into the recovery furnace, where organic

compounds are combusted, and the Na2SO4 is reduced to Na2S. The black liquor burned in therecovery furnace has a high energy content (5,800 to 6,600 British thermal units per pound

[Btu/lb] of dry solids), which is recovered as steam for process requirements, such as cooking

wood chips, heating and evaporating black liquor, preheating combustion air, and drying the

pulp or paper products. The process steam from the recovery furnace is often supplemented



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with fossil fuel-fired and/or wood-fired power boilers. Particulate matter (PM) (primarily

Na2SO4) exiting the furnace with the hot flue gases is collected in an electrostatic precipitator

(ESP) and added to the black liquor to be fired in the recovery furnace. Additional makeup

Na2SO4, or “saltcake,” may also be added to the black liquor prior to firing. Molten inorganic

salts, referred to as “smelt,” collect in a char bed at the bottom of the furnace. Smelt is drawn

off and dissolved in weak wash water in the smelt dissolving tank (SDT) to form a solution of

carbonate salts called “green liquor,” which is primarily Na2S and Na2CO3. Green liquor also

contains insoluble unburned carbon and inorganic impurities, called dregs, which are removed

in a series of clarification tanks.

Causticizing and calcining

Decanted green liquor is transferred to the causticizing area, where the Na2CO3 is converted to NaOH by the addition of lime (calcium oxide [CaO]). The green liquor is first transferred to a

slaker tank, where CaO from the lime kiln reacts with water to form calcium hydroxide

(Ca(OH)2). From the slaker, liquor flows through a series of agitated tanks, referred to as

causticizers, that allow the causticizing reaction to go to completion (i.e., Ca(OH)2 reacts with

Na2CO3 to form NaOH and calcium carbonate [CaCO3]). The causticizing product is then

routed to the white liquor clarifier, which removes CaCO3 precipitate, referred to as “lime

mud.” The lime mud is washed in the mud washer to remove the last traces of sod ium. The

mud from the mud washer is then dried and calcined in a lime kiln to produce “reburned”

lime, which is reintroduced to the slaker. The mud washer filtrate, known as weak wash, is

used in the SDT to dissolve recovery furnace smelt. The white liquor (NaOH and Na2S) from

the clarifier is recycled to the digesters in the pulping area of the mill.

Kraft Lime Kilns

Kraft (and soda) pulp mills use lime kilns to regenerate a portion of the chemical cooking

solution. The function of the lime kiln is to oxidize lime mud (CaCO3) to reburned lime

(CaO), a process known as calcining. The CaO produced in the lime kiln is used in the

causticizing reactions that take place in the green liquor slaker and causticizers to produce the

NaOH used in the white liquor. In the Kraft pulping and chemical recovery process, biomass

carbon from the wood is dissolved and either emitted as biomass CO2 from the recovery



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furnace or captured in Na2CO3 exiting in the smelt from the recovery furnace. In the process

of converting the Na2CO3 into new pulping chemicals, this biomass carbon (i.e., the carbonate

ion) is transferred to CaCO3 in the causticizing process. In the lime kiln, the CaCO3 is

converted to CaO (i.e., lime material used in the chemical recovery process) and biomass CO2

originating from the wood residuals contained in black liquor is released to the atmosphere.

Figure 2.2 contains a simplified representation of the Kraft pulping and chemical recovery

process [7].

Figure 2.2: Simplified Representation of the Kraft Pulping and Chemical Recovery System

Unlike lime kilns used at lime production facilities, where CO 2 emissions are entirely fossil in

nature, the CO2 emitted from Kraft mill lime kilns originates from two sources:

(1) Fossil fuels burned in the kiln, and

(2) Conversion of CaCO3 (or “lime mud”) generated in the recovery process to CaO

(lime).

As shown above (in Figure 2.2), the calcium carbonate-derived CO2 emissions almost

exclusively originate from biomass. The lime kiln typically produces about 95 percent of the

lime needed for the causticizing reaction. Either make-up lime or limestone is purchased to

account for losses.



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CHAPTER 3

LITERATURE SURVEY

3.1.Indian Scenario of pulp and paper mills

Traditionally world-wide, paper has been made from wood and bamboo as raw materials. As

paper mills in India continue to face challenges with forest based-raw materials, industries

started looking for alternative sources of raw material for manufacture of paper. Pulp and

paper industries have been categorized based on the raw material usage. There are three basic

categories which are as under-

1. Forest based: Mills using wood and bamboo contribute 31% production.

2.

Agro based: There are mills using agro residues like Bagasse, wheat straw, rice straw,etc., which contribute 22% of total production.

3. Recycled fiber: Mills using waste paper contribute around 47% of country’s production

and over the years usage of recycled fiber has increased considerably.

The paper industry, irrespective of the raw material or process used, is highly classified as one

of the major polluting industries. A dominant pollutant is the black liquor effluent containing

lignin which is separated from cellulosic fibers. The Black liquor is also rich in residual

chemicals (e.g. caustic soda) whose recovery can substantially enhance the economy of paper

production. With proper selection of appropriate pulping & bleaching technologies and

efficient chemical recovery system, pulp & paper industries can become one of the very

sustainable industries as water cycles can be closed, chemical cycles can be closed, and

energy can come from renewable biomass.

The pulp and paper industry is one of the key industrial sectors contributing to the Indian

economy. The growth of paper mills (Table 3.1) is quite rapid in last two decades [8].



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Table 3.1 Growth of paper mills in India [8]

Year No. of units Installed capacity

(million tonnes)

Production

(million tonnes)

Capacity

utilization (%)

Per capita

consumption (kg)

1950 17 0.13 0.11 85 0.9

1970 57 0.77 0.75 99 1.9

1990 325 3.3 2.43 62 3.6

2000 380 3.94 4.87 99 5.5

2006 660 8.5 6.8 80 6.7

2007 667 8.5 8.3 100 8.3

2010 759 12.7 10.11 80 9.3

The main growth drivers for paper demand includes increase in literacy rate, improvement in

standard of living, booming retail sector, increase in packaging and advertising etc. As paper

has become an essential requirement of our life and with the population of 1.2 billion, this will

further fuel the demand of paper.

3.2.Black liquor

Black liquor is a complex solution containing polydisperse lignin, a host of degraded reaction

products, extractives and several inorganic compounds. It contain practically all alkali (most

alkali is present as Na2CO3) originally charged into the digesters along with almost over 50%of original weight of the non-wood fibrous raw materials. The liquor is intense black tending

to reddish brown on dilution. During handling, its concentration changes from nearly 10-16%

in weak black liquor to 60-80% in concentrated black liquors [9].



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Weak black liquor is Non-Newtonian in nature and has properties which influence its

transportation, heat transfer, combustion and stability. The properties are broadly given in the

following areas:

Rheological properties: density, viscosity

Thermal properties: specific heat, conductivity, foaming characteristics, boiling point rise

Combustion properties: ignition temperature, volatile matter calorific value, swelling

characteristics. The composition of black liquor is given in Table 3.1.

Table3.2 Elemental analysis of the dry black liquor solids [10]

Element w/w (%)

C 36.4

H 3.5

O 34.3

N 0.14

Na 18.6

K 2.02

Cl 0.24

S 4.8

total 100

From the point of view of fluid mechanics, black liquor is a liquid. However, it has two states,

fluid and solid. Its main components are inorganic cooking chemicals, lignin and other

organic constituents removed from the wood during pulping in the digester, and water. These

organic constituents are combined chemically with sodium hydroxide (NaOH) in the form of

sodium salts such as Na2S, Na2CO3 and Na2SO4. Table 3.3 shows a comparison of black

liquor, wood and two coals in terms of elemental composition and the most important

combustion properties that also affect the selection of a modeling approach.



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Table 3.3 Comparison of black liquor with other fuels [11, 12, 13].

Black liquor Wood Pine Coal Gardanne Coal Göttelborn

C, dry 38.2 48.9 48.0 72.4

H, dry 3.4 6.0 3.2 4.8

O, dry 31.1 43.8 19.7 11.1

S, dry 5.2 0.06 4.2 1.0

Na, dry 19.8 0.003 0.03 0.04

K, dry 1.9 0.051 0.07 0.2

Cl, dry 0.1 <0.01 0.09 0.09

N, dry 0.1 0.17 1.2 1.5

Moisture, % 15-30 40 30 1

Ash, % dry 40 0.5 31.4 18.1

Fuel ratio 0.25 0.09 0.47 1.58

Swelling, d/d0 3-4 - - -

Particle size,

mm

3-5 0.1-100 0.01-50

HHV, MJ/kg 14 19.1 18.7 30

Black liquor water content is relatively high, but lower than for wood. In modern recovery

boilers the water content of the black liquor burned approaches 15-20%, which means that

problems related to this (e.g. risk of water smelt explosion) will be smaller. As Table 3.2 also

shows, the fuel ratio (char/volatile matter) of black liquor is very low which means that most

of the organics will burn in the gas phase. This increases the rate of combustion and makes the

conversion of larger particles more effective.



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Another important factor is the swelling behavior; the diameter of the particle may increase by

a factor of 3-4 during devolatilization. This effectively increases the external reaction surface

area, which affects the rate of external heat and mass transfer controlled processes. In the

recovery boiler, black liquor droplets are sprayed which do not keep their original shape

during swelling and in most of the modeling, spherical particles were considered [14]. From

the boiler operation point of view black liquor is a very difficult fuel as it has high ash content

and more importantly, black liquor ash has a very low melting point that may give rise to

severe boiler fouling problems [15].

3.3. Black liquor Gasification

In the pulp and paper industry, large quantities of forest biomass have been used and the by-

products or residues resulting from the industry including bark, forest logging residues and

black liquor are one of the major biomass resources that can be further used for energy

purpose to produce electricity, heat and bio fuels [16]. Black liquor gasification (BLG) is

considered as an alternative technology to replace conventional recovery cycle with the

recovery boiler. BLG technologies are distinguished in two major classes; (i) Low

temperature gasification, and (ii) High temperature gasification. Low temperature gasifier

operates at 600 – 850 oC, below the melting point of inorganics, thus avoiding smelt-water

explosions. High temperature gasification units generally operate in the 900 – 1000 oC range,

and produce a molten smelt [17].

3.4. Combustion of Black liquor

The combustion of black liquor has been studied for more than 40 years. Knowledge of the

composition of black liquor particles are required in order to study the combustion of the

black liquor in the boilers. A literature survey was conducted on the combustion stages of

black liquor. It is followed by explanation of different types of black liquor combustionmodels currently available to broaden the knowledge of the art of the modelling in this

category.The rapid development of numerical simulation methods has made it possible to

simulate a recovery furnace and accurately predict its performance [9] and has the potential to

enhance our understanding of the processes occurring in a recovery furnace.



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Combustion of the organic portion of liquor in a recovery boiler will form sodium sulfide and

sodium carbonate and thus, produce heat. Then, the heat produces high pressure steam to

generate electricity and low pressure steam for process use. In order to convert the chemicals

and energy in the black liquor in an efficient way, the black liquor has to be atomized to

droplets and sprayed into the recovery unit in some way [18]. Typical representation of the

recovery boiler consists mainly of the furnace & several heat exchangers unit is shown in

figure 3.1.

Figure 3.1: Typical recovery furnace

Figure 3.2 below shows some principal inorganic reactions in a recovery boiler and also

where in the furnace the reactions take place. In a conventional recovery boiler there is an

oxidizing zone in the upper part and a reducing zone in the lower part. The thick liquor is

introduced through one or several nozzles into the reducing zone. Combustion air is mostly

supplied at three different levels as primary, secondary and tertiary air (from the bottom up)



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Figure 3.2: Some conceptual chemical reactions in a recovery boiler [SEPA report 4008, 1992]

The main objective of the Kraft recovery steps is to minimize, as efficiently as possible, the

loss and subsequent makeup of the chemicals used in preparation of the cooking liquor

(commonly called white liquor). Pre – heated concentrated BL is sprayed into the furnace via

number of nozzles, the liquor guns. The droplets formed by the nozzles are typically 2-3mmdiameter. The droplets then go through a series of processes involving drying, pyrolysis, char

gasification and finally homogeneous combustion and are shown in figure 3.3. Burning of the

solid char residue that remains after pyrolysis of the black liquor occurs largely in the char

bed that covers the floor of the furnace. Most of the inorganic BL constituents remain in the

char and finally form a smelt at the bottom, consisting mainly of sodium carbonate & sodium

sulphides.



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Figure 3.3: Different stages of black liquor droplets in recovery boiler [19]

It has been understood that black liquor behaves in very unconventional fashion during

combustion. Its combustion behaviour is more like that of solid fuels, such as coal, than that

of oil or other liquid fuels. As the black liquor droplets enters the recovery unit they are being

exposed for hot gases and will undergo drying, pyrolysis, char conversion and smelt reaction.

During these stages the droplets undergo morphological changes which lead to changes in

both the heat transfer and aerodynamic properties of the droplets. In figure 3.4 these threemain stages are shown graphically.

Figure 3.4: Conversion stages of a black liquor droplet

The organic matter in black liquor solids begins to degrade thermally above 200 oC, producing

water vapor, CO2, CO, hydrogen, light hydrocarbons, tar, and light sulfur-containing gases



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[20, 21]. For the relatively large (2-3 mm) droplets fired in black liquor burning,

devolatilization is essentially complete when the residue temperature reaches 650-750 C [22].

The char residue contains fixed carbon, some hydrogen, and most of the inorganic matter. The

following reactions very simply explain the combustion of the combustible organic

compounds:

C + O2 → CO2 + heat

2C + O2 → 2CO + heat

CO + ½O2 → CO2 + heat

2H2 + O2 → 2H2O + heat

In conjunction with the above combustion the following chemical reactions are also

occurring:

H2S + 1½O2 → SO2 + H2O

Na2O + SO2 + ½O2 → Na2SO4

NaOH + CO2 → Na2CO3 + H2O

Na2O + CO2→ Na2CO3

SO2 + ½O2 → SO3

The chemical transformation that takes place in the recovery boiler is not just due to

combustion, but also involves the reduction of sodium sulfate to sodium sulfide. The main

reaction is:

Na2SO4 + 2C + heat→ Na2S + 2CO2

This reaction consumes heat, i.e. it is endothermic.For determining the combustion behavior of 1.5-2.5 mm particles, a muffle furnace, figure 3.5

has been extensively used [22, 23]. The duration of different combustion stages, swelling

behavior and mass loss and the release of species at predetermined time intervals are



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measured. This type of reactor can be well used for modeling purposes as the boundary

conditions can be defined for a one-dimensional single particle combustion model.

Figure 3.5: Muffle furnace reactor [10]

W. J. Frederick et al., 1994 [24], evaluated the yields of volatiles and char carbon for spent

pulping liquors by pyrolyzing single liquor droplets in N2/CO atmospheres at 600-1200 oC,

weighing the droplet residue, and analyzing the residue for carbon content. The author

concluded that the droplet mass had no effect on volatiles or char carbon yield over the range

studied.

Raimo Alen et al., 1994 [25], presented and studied the swelling behavior and properties of

two different black liquors (pine and birch) at temperature 25-600 oC in an inert atmosphere.

The results showed that black liquor starts swelling at temperature range of 180-270 oC and

there is no difference in swelling properties of two liquors.

If water content of a black liquor drop decreases enough, either locally on the surface of the

drop, a bright yellow flame surrounding the drops will be formed at some distance from the

drop surface. Appearance of the flame indicates the initiation of devolatilization process and

disappearance of the flame indicates the end of the stage. Devolatilization processes are much

dependent on the maximum drop temperature. The processes include:

(a) Heating the black liquor drop from its temperature at ignition to that at the end of

devolatilization (i.e., beginning of char combustion),

(b) Vaporization of the residual water, and

(c) Supplying the heat for pyrolysis reactions.



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Devolatilization time was taken as the duration of the visible flame and time needed to

complete above mentioned processes. The point of maximum swelling of the drop also

indicates the end of devolatilization stage.

During the devolatilization of black liquor droplets, investigation has been done on the factors

which influence the yield of volatiles and the carbon content of the char produced, i.e. liquor

type, droplet size, dry solid content, oxygen content, furnace temperature and pyrolysis time.

It was found that the amount of volatiles formed increased and the carbon content of the char

decreased with increasing reaction time at a constant furnace temperature. Carbon, sodium

and total mass loss from the char particles continued beyond devolatilization. This was

assumed to be due to sodium carbonate decomposition. The volatiles yield for different Kraft

liquor ranged from 35% to 47 % of the initial mass of the liquor [26].

Frederick et al. 1999 [27], studied the devolatilization of black liquor at temperatures of 400-600°C at high heating rates and extracted constants for a Kobayashi-type kinetic model for the

release of fixed C, S, and N. In the Kobayashi model, devolatilization is described by two

parallel irreversible reactions that both produce a volatile and solid product. The solid

products cannot any more undergo devolatilization and are regarded inert. The model is

schematically described in figure 3.6. The reactions are characterized by the fraction of solids

produced by the two reactions (α1 and α2) and by the activation energies and pre-exponential

coefficients of the reactions (Ea1

and A1 and Ea

2and A

2). The Kobayashi model is a simple

experimental model. Its advantage is that it can describe the dependence of char yield on

particle temperature history.

Volatile 1 (V1) + Char 1 (S1)

Fuel (C)

Volatile 2 (V2) + Char 2 (S2)

Figure 3.6: Kobayashi devolatilization model



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Hupa et al., 1994 [28], published an article on black liquor combustion in which they defined

the stages of black liquor combustion as drying, devolatilization and char burning. Their work

with a laboratory furnace enabled them to define three time-periods describing the entire

droplet combustion process: drying time, ti, from the initial contact with the gas to ignition;

devolatilization time, tv, from the appearance of the flame to maximum expansion; and char

burning time, tc. Good empirical correlations were established for all three: ti is proportional to

the initial droplet diameter, and both tv and tc are proportional to the 5/3 power of that

diameter. They were also able to generate profiles of the swelling and of the droplets

temperature as a function of the liquor combustion time.

As the combustion behaviour of black liquor is somewhat like of coal and other biomass so

kinetic model of devolatilization of coal and biomass were also studied.

A common approach in coal pyrolysis modeling is a single step nth order reaction. It assumes

that solid fuel, during one global decomposition step, transforms into solid residue (char) and

gaseous volatiles. The simplest of these models is the single 1st order model, which is popular

in engineering applications. For n = 1, the reaction rate is linearly dependent on the

concentration of the unreacted fuel. The single-step models are based on an empirical

expression of the global kinetics and do not describe the physical and chemical phenomena

taking place during pyrolysis. The rate of volatile release for reaction i is written as [29]:

( )

Here Vi,t is the volatile yield at time t, Vi,∞ the total volatile yield and n the reaction order. The

reaction rate coefficient K i is obtained from the well-known Arrhenius equation:

/RT)

The pre-exponential factor k i describes the probability that a collision between two molecules

leads to reaction. Ei is the activation energy and can be described as the height of an energy



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barrier that must be overcome before the reaction can take place. R is the universal gas

constant and T temperature.

The modeling of coal pyrolysis has progressed from simple, (single equation) kinetic model to

complex "network" models. The most fundamental pyrolysis models, which try to describe the

physical and chemical phenomena taking place during devolatilization, are the network

models. These models approximate coal as a polymeric structure and provide more detailed

predictions of pyrolysis behavior than is possible with the simpler models. A total of three

network coal devolatilization models have been developed, [30-32], which have various

capacities for predicting coal thermal decomposition under practical conditions. These coal

devolatilization models are:

Functional Group - Depolymerization, Vaporization, Cross linking (FG-DVC) model

Chemical Percolation Devolatilization (CPD) model

FLASHCHAIN model

Belosevic et al., 2010 [33], indicated the importance of co-firing of coal with biomass which

can lead to CO2 reduction. In his study, different modelling approaches were reported for

different processes occurring during combustion of biomass.

3.5. Modelling techniques

A method for practical process modeling is the black box approach, where models are

obtained exclusively from experimental plant data. Such models do not provide a detailed

knowledge of the underlying physics of the problem, but they do provide a description of the

dynamic relationship between input and output variables. Statistical models based on

regression analysis are an example of such a black box approach. Recently a promising

modeling technique, artificial neural networks (ANNs) and response surface methodology

(RSM), has found numerous applications in representing the functional relationships between

input variables and the output (response) of the process using experimental data.



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3.5.1. Artificial Neural Networks (ANNs)

In many situations, the functional relationship between covariates (also known as input

variables) and response variables (also known as output variables) is of great interest.

Artificial neural networks can be applied to approximate any complex functional

relationship. Neural network approach, unlike the mechanistic model, doesn’t require any

external manifestation of parametric relationship [34]. This makes artificial neural

networks a valuable statistical tool. Observed data are used to train the neural network

and the neural network learns an approximation of the relationship by iteratively adapting

its parameters. ANNs is built to train Multi-layer Perceptrons (MLP) in the context of

regression analyses, i.e. to approximate functional relationships between covariates and

response variables. Figure 3.7 shows the neural network topology with 4 inputs, 1 hidden

layer (15 neurons) and 1 output.

Figure 3.7: Neural Network topology

Fundamental aspects

The MLP is usually composed of an input, a hidden and an output layer of neurons. The

neurons in the input layer are typically linear, while the ones in the hidden layer have

nonlinear (often sigmoidal) activation functions. The neurons in the output layer may be

linear or nonlinear. Each interconnection between two layers of neurons has a parameter



23

associated with it that weights the feed forwardly passing signal. Additionally, each

neuron in the hidden and output layers has an intercept, also known as bias. Neural

networks are mainly characterized by the type of learning rule, neurons used and the way

that they are organized, number of layers and number of neurons per layer. These are

related to the number of training points and to the nature of the function [35]. The

learning algorithm is a procedure for modifying the weights and biases of the network.

Among the many leaning (training) algorithms of neural networks, the back propagation

(BP) has demonstrated excellent capability for various prediction and classification

problems [36]. Transfer functions for the neurons are needed/introduce to bring the non-

linearity into the network. Without this nonlinearity, neurons would behave in a linear

fashion and the ANN would not be able to account the nonlinear input-output

relationships. Now-a-days, three types of transfer function are being commonly used:Linear, Sigmoid and Tang hyperbolic [37].

Interpretability of statistical models, or the understanding of the way inputs relate to an

output in a model, is a desirable property in applied research. Commonly used models,

such as the logistic regression model, are interpretable, but often do not provide adequate

prediction, thus making their interpretation questionable. Statistical aspects of ANNs,

such as approximation and convergence properties, have been discussed, and compared

with properties of more classical methods [38, 39]. The large flexibility provided by

neural network models results in prediction with a relatively small bias, but a large

variance. Careful methods for variance control [40-42] can lead to a smaller prediction

error and are required to robustify the prediction.

3.5.2. Response Surface Methodology (RSM)

Response surface methodology (RSM) is a collection of mathematical and statistical

techniques for empirical model building. By careful design of experiments, the objective

is to optimize a response (output variable) which is influenced by several independent

variables (input variables). An experiment is a series of tests, called runs, in which

changes are made in the input variables in order to identify the reasons for changes in the

output response. The application of RSM to design optimization is aimed at reducing the

cost of expensive analysis methods (e.g. finite element method or CFD analysis) and their



24

associated numerical noise. In recent years, multivariate statistical techniques have been

preferred to identify the optimal combination of factors and interactions among factors,

which are not possible to identify using the univariate method. In addition, these

techniques are very useful tools to reduce the time and cost of studies. The experimental

design involves estimation of the coefficients in a mathematical model, predicting the

response, and checking the adequacy of the model [43].

Response Surface Methodology (RSM) combined with Artificial Neural Networks

(ANN) becomes a powerful tool for modeling and simulations for various processes.

Both methodologies don’t need the explicit expressions of the physical meaning of the

system or process under investigation.

Abbas et al., 2003 [44], presented the shortcomings of models for devolatilization,

combustion & emission and reported a new approach based on artificial neural network.Then neural network model was implemented with the existing 3D CFD combustion

code. Model based on neural network was found comparable with other devolatization

model and is fast, reliable for solving complex control problems.

Previous literature papers employed neural networks to predict characteristics of

combustion, pyrolysis or gasification processes. In reference [45], a hybrid gasification

model using ANNs was developed to estimate reactivity parameters of different types of

coal with relative success. Reference [46] used ANNs to predict the emission of pollutant

gases in the combustion process of a mixture of coal and urban solid residuals with a

good agreement between experimental and predicted data.

Joseph et al., [47] performed a comparison between classical statistical methods and

ANNs. They concluded that ANN models perform better than regression models and

show more tolerance to noise in the data.

Marchitan et al., 2010 [48], presents a comparative study between RSM and ANN for

reactive extraction optimization of tartaric acid from aqueous solution using amine. The

extraction efficiency was modeled and optimized as a function of three input variables,

i.e. tartaric acid concentration in aqueous phase, pH of aqueous solution and amine

concentration in organic phase. Both methodologies have been compared for their

modeling and optimization abilities. Both models have been employed for construction of



25

response/output surface plots in order to reveal the influence of input variables on

extraction efficiency as well as to figure out the interaction effects between variables.

To overcome the RSM at a disadvantage i.e. getting trapped in local minimum

(maximum), Babak Abbasi et al., 2012 [49], used neural networks as a means to improve

the estimation in the RSM. This approach leads to reducing the calculations.



26

CHAPTER 4

EXPERIMENTAL WORK

4.1.

Preparation of dry Black liquor solids (DBS)

Black liquor procured from a Star Paper Mills in Saharanpur after the evaporation and the

solid concentration is 62%. The black liquor sample was first heated and stirred to decrease

the viscosity and increase the homogeneity of the liquor. About 150 g of the liquor were

poured in a rectangular galvanized iron sheet of dimensions 35cm by 25 cm, and spread over

the sheet to form a thin film of liquor. This was necessary in order to increase the surface area,

shorten the drying time, and prevent non-uniformity in the liquor. Four sheets of liquor were

prepared with this method, and placed in a drying oven controlled at 105 °C for 12 hours to

evaporate the water content of black liquor, Figure 4-1. A thin dried layer of black liquor

remained on the sheet after drying and it was allowed to cool down to room temperature. The

weight of the sheet plus black liquor was noted periodically and when this weight reached a

constant value indicating that all the moisture had evaporated, the solids were scraped off. It

was powdered in a domestic mixer-grinder and sieved into different particle size ranges 63-90

µm.

4.2. Experiment using dry Black Liquor solids

For the experimental studies in the muffle furnace, a known amount of DBS was taken in a

standard silica crucible fitted with a lid. The crucible was purged with nitrogen and the initial

weight of the crucible & DBS noted. A laboratory muffle furnace (M/S Commercial

Equipments, New Delhi, India) fitted with kanthal aluminium heating elements, asbestos

lagging and digital temperature indicator/controller was used. Through a tiny hole in the door

of the muffle furnace, nitrogen (N2) gas was supplied to create an inert atmosphere in the

furnace. The furnace was heated up to the required temperature and the silica crucible quickly

inserted into the furnace using tongs. The crucible was kept in the muffle furnace for the

desired time period and then removed. To cool down the crucible to room temperature ice

quenching was done. The final weight of the cooled crucible was noted. An electronic

weighing balance (Sartorius GC16035-OCE, Germany) with an accuracy of 10 mg was used



27

for noting the weights. The range of the experimental parameters used in the study is given

below. Most of the experiments were replicated to verify the obtained results.

Temperature: 300 – 1000 oC

Holding time: 30 – 180 sec

Mass of DBS: 2 & 5 gms

4.3. Experimental results obtained from the pyrolysis of dry Black Liquor solids

The experimental results obtained in the temperature range of 300oC-1000oC for different

time periods are shown in Figures 4.1-4.14 and the data is presented in Tables.

Fig.4.1: Mass vs Time for DBS (2 gm) at 300oC

20 40 60 80 100 120 140 160 180 200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft



28



20 40 60 80 100 120 140 160 180 200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft

20 40 60 80 100 120 140 160 180 200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft



29



20 40 60 80 100 120 140 160 180 200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft

20 40 60 80 100 120 140 160 180 200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft



30



20 40 60 80 100 120 140 160 180 200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft

20 40 60 80 100 120 140 160 180 200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft



31



20 40 60 80 100 120 140 160 180 200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft

20 40 60 80 100 120 140 160 180 200

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

3.0

3.3

3.6

3.9

4.2

4.5

4.8

5.1

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft



32



20 40 60 80 100 120 140 160 180 200

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

3.0

3.3

3.6

3.9

4.2

4.5

4.8

5.1

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft

20 40 60 80 100 120 140 160 180 200

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

3.0

3.3

3.6

3.9

4.2

4.5

4.8

5.1

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft



33



20 40 60 80 100 120 140 160 180 200

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

3.0

3.3

3.6

3.9

4.2

4.5

4.8

5.1

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft

20 40 60 80 100 120 140 160 180 200

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

3.0

3.3

3.6

3.9

4.2

4.5

4.8

5.1

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft



34

Fig.4.14. Mass vs Time for DBS (5 gm) at 1000oC

Table 4.1 Experimental Mass loss Data of Dried Black Liquor solids at 300 oC at different holding times

Temp. (◦C) 300

Run

no.

Holding

Time (sec)

Crucible

wt (gms)

A

Lid wt

(gms)

B

DBS (gms) = 2 After Pyrolysis

A+B+DBS A+B+DBS Mass loss Mass left

1 30 58.56 26.04 86.60 86.60 0 2

2 45 56.91 30.45 89.36 89.35 0.01 1.99

3 60 58.14 32.47 92.61 92.58 0.03 1.97

4 75 52.74 31.66 86.40 86.37 0.03 1.97

5 90 41.86 26.04 69.90 69.85 0.05 1.95

6 105 53.09 30.45 85.54 85.48 0.06 1.94

7 120 52.74 26.04 80.78 80.64 0.14 1.868 135 58.14 32.46 92.60 92.43 0.17 1.83

9 150 53.09 31.68 86.77 86.59 0.18 1.82

10 180 56.91 30.43 89.34 89.16 0.18 1.82

20 40 60 80 100 120 140 160 180 200

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

3.0

3.3

3.6

3.9

4.2

4.5

4.8

5.1

M a s s ( g m )

Holding time (sec)

Volatiles

Massleft



35


Temp. (◦C) 400

Run

no.

Holding

Time (sec)

Crucible

wt (gms)

A

Lid wt

(gms)

B



1 30 58.55 31.67 92.22 92.21 0.01 1.99

2 45 52.76 26.04 80.80 80.77 0.03 1.97

3 60 56.92 30.46 89.38 89.33 0.05 1.95

4 75 53.06 30.42 85.48 85.42 0.06 1.94

5 90 58.53 26.04 86.57 86.47 0.10 1.90

6 105 41.85 32.45 76.30 76.17 0.13 1.87

7 120 52.76 31.68 86.44 86.27 0.17 1.838 135 58.09 26.04 86.13 85.92 0.21 1.79

9 150 41.82 30.43 74.25 74.03 0.22 1.78

10 180 53.09 32.52 87.61 87.36 0.25 1.75


Temp. (◦C) 500

Run

no.

Holding

Time (sec)

Crucible

wt (gms)

A

Lid wt

(gms) B

DBS (gms) = 2

After Pyrolysis


1 30 58.08 31.71 91.79 91.74 0.05 1.95

2 45 58.50 37.18 97.68 97.60 0.08 1.92

3 60 52.72 32.43 87.15 87.04 0.11 1.89

4 75 42.10 31.67 75.77 75.61 0.16 1.84

5 90 53.13 32.43 87.56 87.39 0.17 1.836 105 56.84 37.18 96.02 95.77 0.25 1.75

7 120 41.76 23.66 67.42 67.11 0.31 1.69

8 135 52.72 32.45 87.17 86.79 0.38 1.62

9 150 42.10 37.18 81.28 80.72 0.56 1.44

10 180 58.76 31.68 92.44 91.80 0.64 1.36



36

Table 4.4 Experimental Mass loss Data of Dried Black Liquor solids at 600 oC at different holding timee

Temp. (◦C) 600

Runno.

HoldingTime (sec)

Crucible

wt (gms)A

Lid wt

(gms) B

DBS (gms) = 2 After Pyrolysis A+B+DBS A+B+DBS Mass loss Mass left

1 30 53.13 32.47 87.60 87.54 0.06 1.94

2 45 58.57 26.04 86.61 86.50 0.11 1.89

3 60 52.73 37.18 91.91 91.79 0.12 1.88

4 75 58.07 32.46 92.53 92.37 0.16 1.84

5 90 56.87 30.42 89.29 88.97 0.32 1.68

6 105 41.80 37.18 80.98 80.40 0.58 1.42

7 120 42.15 32.47 76.62 75.96 0.66 1.34

8 135 53.15 26.04 81.19 80.48 0.71 1.29

9 150 58.09 30.43 90.52 89.78 0.74 1.26

10 180 52.74 24.58 79.32 78.51 0.81 1.19


Temp. (◦C) 700

Run

no.

HoldingTime

(sec)

Cruciblewt (gms)

A

Lid wt(gms)

B



1 30 52.62 32.43 87.05 86.95 0.10 1.9

2 45 42.06 31.65 75.71 75.59 0.12 1.88

3 60 53.08 37.17 92.25 91.90 0.35 1.65

4 75 41.75 26.02 69.77 69.35 0.42 1.58

5 90 58.48 26.04 86.52 86.06 0.46 1.54

6 105 58.01 31.67 91.68 91.04 0.64 1.36

7 120 42.09 37.18 81.27 80.55 0.72 1.28

8 135 58.02 32.47 92.49 91.72 0.77 1.23

9 150 52.73 32.46 87.19 86.39 0.80 1.20

10 180 41.78 31.19 74.97 74.13 0.84 1.16



37


Temp. (◦C) 800

Runno.

Holding Time (sec)

Crucible

wt (gms)A

Lid wt

(gms) B



1 30 42.12 31.69 75.81 75.68 0.13 1.87

2 45 56.83 26.03 84.86 84.63 0.23 1.77

3 60 52.74 30.44 85.18 84.58 0.60 1.4

4 75 53.10 21.96 77.06 76.44 0.62 1.38

5 90 52.71 32.46 87.17 86.50 0.67 1.33

6 105 41.78 32.45 76.23 75.54 0.69 1.31

7 120 58.07 37.18 97.25 96.54 0.71 1.29

8 135 42.13 23.69 67.82 67.07 0.75 1.25

9 150 56.84 30.43 89.27 88.41 0.86 1.14

10 180 53.13 31.70 86.83 85.90 0.93 1.07


Temp. (◦C) 900

Run

no. Holding

Time (sec)

Cruciblewt (gms)

A

Lid wt(gms)

B



1 30 58.55 30.42 90.97 90.81 0.16 1.84

2 45 58.08 31.69 91.77 91.41 0.36 1.64

3 60 41.82 23.69 67.51 66.81 0.70 1.30

4 75 56.87 24.58 83.45 82.73 0.72 1.28

5 90 52.75 26.04 80.79 80.04 0.75 1.25

6 105 53.15 32.46 87.61 86.83 0.78 1.227 120 41.82 23.69 67.51 66.71 0.80 1.2

8 135 58.56 32.45 93.01 92.14 0.87 1.13

9 150 42.14 30.42 74.56 73.61 0.95 1.05

10 180 58.11 31.66 91.77 90.65 1.12 0.88



38


Temp. (◦C) 1000

Run

no.

Holding

Time (sec)

Crucible

wt (gms)

A

Lid wt

(gms)

B


A+B+DBS A+B+DBS Mass loss Mass left1 30 56.9 30.42 89.32 89.00 0.32 1.68

2 45 58.55 31.68 92.23 91.66 0.57 1.43

3 60 53.09 31.68 86.77 86.01 0.76 1.24

4 75 58.13 31.68 91.81 91.00 0.81 1.19

5 90 58.55 30.48 91.03 90.14 0.89 1.11

6 105 56.91 30.42 89.33 88.43 0.90 1.10

7 120 56.91 26.04 84.95 84.03 0.92 1.08

8 135 52.78 30.42 85.2 84.22 0.98 1.02

9 150 58.14 26.04 86.18 85.20 0.98 1.02

10 180 58.53 31.65 92.18 91.15 1.03 0.97


Temp. (◦C) 300

Runno.

Holding Time (sec)

Crucible

wt (gms)A

Lid wt

(gms) B



1 30 58.14 37.18 100.32 100.315 0.005 4.995

2 45 52.74 23.66 81.4 81.37 0.03 4.97

3 60 41.86 32.45 79.31 79.25 0.06 4.94

4 75 53.09 37.18 95.27 95.2 0.07 4.93

5 90 52.74 31.68 89.42 89.31 0.11 4.89

6 105 58.14 32.47 95.61 95.48 0.13 4.87

7 120 58.08 26.04 89.12 88.93 0.19 4.81

8 135 58.50 37.18 100.68 100.45 0.23 4.77

9 150 52.72 32.46 90.18 89.93 0.25 4.75

10 180 42.10 30.42 77.52 77.23 0.29 4.71



39


Temp. (◦C) 400

Run

no. Holding

Time (sec)

Crucible

wt (gms)

A

Lid wt

(gms) B



1 30 56.9 30.42 92.32 92.31 0.01 4.99

2 4558.55 31.68 95.23 95.2 0.03 4.97

3 6053.09 31.68 89.77 89.69 0.08 4.92

4 7558.13 31.68 94.81 94.7 0.11 4.89

5 9058.55 30.48 94.03 93.85 0.18 4.82

6 10558.56 26.04 89.6 89.33 0.27 4.73

7 12056.91 30.45 92.36 92.06 0.3 4.7

8 13558.14 32.47 95.61 95.26 0.35 4.65

9 15052.74 31.66 89.4 88.92 0.48 4.52

10 18041.86 26.04 72.9 72.35 0.55 4.45


Temp. (◦C) 500

Run

no. Holding

Time (sec)

Cruciblewt (gms)

A

Lid wt(gms) B

DBS (gms) = 5 After Pyrolysis A+B+DBS A+B+DBS Mass loss Mass left

1 3052.55 30.42 87.97 87.91 0.06 4.94

2 4541.91 31.69 78.6 78.49 0.11 4.89

3 6057.91 23.69 86.6 86.39 0.21 4.79

4 7553.07 24.58 82.65 82.3 0.35 4.65

5 9056.7 26.04 87.74 87.21 0.53 4.47

6 105 58.4 32.46 95.86 95.19 0.67 4.33

7 12052.57 23.69 81.26 80.5 0.76 4.24

8 13541.69 32.45 79.14 78.23 0.91 4.09

9 15041.94 30.42 77.36 76.38 0.98 4.02

10 18053.07 31.66 89.73 88.72 1.01 3.99



40


Temp. (◦C) 600

Run

no. Holding

Time (sec)

Crucible

wt (gms)

A

Lid wt

(gms) B



1 30 41.98 26.04 73.02 72.95 0.07 4.93

2 4552.55 30.45 88 87.87 0.13 4.87

3 6041.95 32.47 79.42 79.1 0.32 4.68

4 7541.73 31.66 78.39 78.03 0.36 4.64

5 9042 26.04 73.04 72.51 0.53 4.47

6 10552.61 30.45 88.06 87.45 0.61 4.39

7 12041.72 26.04 72.76 72.01 0.75 4.25

8 13541.96 32.46 79.42 78.45 0.97 4.03

9 15058.44 31.68 95.12 93.98 1.14 3.86

10 18057.96 30.43 93.39 92.23 1.16 3.84


Temp. (◦C) 800

Run

no.

Holding Time (sec)

Crucible

wt (gms)

A

Lid wt

(gms) B



1 3058.01 32.43 95.44 94.97 0.47 4.53

2 4542.09 31.65 78.74 78.22 0.52 4.48

3 6058.02 37.17 100.19 99.48 0.71 4.29

4 7552.73 26.02 83.75 82.85 0.9 4.1

5 9041.78 26.04 72.82 71.64 1.18 3.82

6 105

53.15 31.67 89.82 88.45 1.37 3.63

7 12041.82 37.18 84 82.36 1.64 3.36

8 13558.56 32.47 96.03 94.05 1.98 3.02

9 15058.44 31.68 95.12 77.58 2.02 2.98

10 18057.96 30.43 93.39 92.17 2.13 2.87



41


Temp. (◦C) 1000

Run

no. Holding

Time (sec)

Crucible

wt (gms)

A

Lid wt

(gms) B



1 30 53.09 30.42 88.51 87.85 0.66 4.34

2 4558.13 31.69 94.82 93.88 0.94 4.06

3 6058.55 23.69 87.24 86.15 1.09 3.91

4 7556.91 24.58 86.49 85.21 1.28 3.72

5 9056.91 26.04 87.95 86.4 1.55 3.45

6 10552.78 32.46 90.24 88.57 1.67 3.33

7 120 58.08 23.69 86.77 84.72 2.05 2.95

8 13558.50 32.45 95.95 93.7 2.25 2.75

9 15052.72 30.42 88.14 85.63 2.51 2.49

10 18042.10 31.66 78.76 76.21 2.55 2.45

4.4. Modeling techniques for devolatilization of dry Black Liquor solids (DBS)

4.4.1. Artificial neural network (ANN) model

There are several ANN models such as feed-forward, Multi-Layer Perceptron (MLP) and the

Radial Basis Function (RBF) have been used in engineering applications to model or

approximate properties [50]. In this study, feed-forward MLP (with tangent sigmoid transfer

function (tansig) at hidden layers and at output layer were used as ANN network to predict the

volatiles release from the dry Black liquor solids during pyrolysis (Fig 4.15). The input- output

patterns required for training were obtained from experimental work. Different back-propagation

(BP) algorithms were compared to select the best suited BP algorithm. The Marquardt –

Levenberg learning algorithm with a minimum mean squared error (MSE) was found as the best.

The performance function for feed-forward networks is mean square error (MSE)- the average

squared error between the network outputs (a) and the target outputs (t). It is defined as follows:



42

The ANN model and its parameters variation were determined based on the minimum value ofthe MSE of the training and prediction set. The training parameters are used as number of input

nodes: 3, number of hidden neurons: 6, 10, and 15 number of output node: 1, learning rule:

Levenberg – Marquardt, number of epochs: 1000, error goal: 0.0001, Mu: 0.01 in this

investigation.

Fig 4.15: Neural network structure

In the ANN, 173 samples were taken for the training of the network by changing the number of

neurons whereas 72 samples were kept aside for the purpose of simulation. When the network

was sufficiently trained, 72 samples were then tested to see the how well the network is

predicting the values of unseen data. From fig 4.16 to 4.21, the results of test data and simulation

data for different neurons were shown. So from the comparison, 10 neurons in the hidden layer is

predicting the good results and it is well agreement with the experimental values. So the structure

of ANN used for modeling is 3 inputs, one hidden layer with 10 neurons, one output layer with 1

neuron and 1 output i.e. 3 x 10 x 1 x 1.



43

Fig 4.16: Output vs experimental values of 173 samples Fig 4.17: Actual vs predicted values of 72 samples (6(6 neuron) neuron)

Fig 4.18: Output vs experimental values of 173 samples Fig 4.19: Actual vs predicted values of 72 samples (10(10 neuron) neuron)

R² = 0.9647

0

0.5

1

1.5

2

2.5

3

0 1 2 3

Test data

R² = 0.7565

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5

Simulation data

R² = 0.9505

0

0.5

1

1.5

2

2.5

3

0 1 2 3

Test data

R² = 0.9241

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 0.5 1 1.5

Simulation data



44

Fig 4.20: Output vs experimental values of 173 samples Fig 4.21: Actual vs predicted values of 72 samples (15

(15 neuron) neuron)

4.4.2. Response surface methodology (RSM) model

Box behnken design was used to understand the influence of the experimental factors and their

interactions on the release of volatile gases and to make predictions for different input values.

Tests were performed to investigate the factors affecting the mass loss of DBS. The levels of the

experimental factors and the design matrix are given in Table 4.15. The Box behnken had three

levels i.e. (-1, 0, +1) resulting in a total of 17 experiments used to optimize the chosen variables

for the mass loss. Experiments were performed in a random order, according to the below

experimental plan to avoid systematic errors.

R² = 0.9188

0

0.5

1

1.5

2

2.5

3

0 1 2 3

Test data

R² = 0.3701

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5

Simulation data



45

Table 4.15 Experimental data set for Box behnken design

Factors Symbols

Level of factors

-1 0 1

Temperature (oC) X1 300 650 1000

Time (sec) X2 30 105 180

DBS (gm) X3 2 3.5 5

Run Actual and coded level of variables Mass loss (gm)

X1 X2 X3

1 -1 0 -1 0.062 0 1 1 0.65

3 0 -1 -1 0.084 1 0 -1 0.85 -1 1 0 0.556 -1 -1 0 0.017 0 1 -1 0.818 1 1 0 1.99 0 0 0 0.9810 0 0 0 1.0111 0 0 0 0.9812 0 0 0 0.9813 1 0 1 1.55

14 -1 0 1 0.1315 0 0 0 116 1 -1 0 0.617 0 -1 1 0.1

The experimental data was processed using Design Expert 9 Software. As can be seen from

Table 4.16, linear, square and interaction coefficients (X1X2) were found as significant terms at

the 5% probability level for the response mass loss.



46

Table 4.16 Estimated coefficients for Mass loss (gm) at coded units

Factor Coefficient Standard Error p-value

Intercept -0.010 0.098 < 0.0001

A-Temperature 1.30 0.078 < 0.0001

B-Time 1.17 0.078 < 0.0001

C-DBS 0.18 0.078 0.0464

AB -0.71 0.11 0.0001

A2 -0.53 0.11 0.0009

B2 -0.73 0.11 < 0.0001

C2 -0.62 0.11 0.0003

S = 0.22 R-Sq = 98.67% R-Sq(adj)= 97.64% PRESS = 2.51

According to the reduced quadratic model suggested by the RSM and recommended

transformation was natural logarithm, the relationship between the mass loss of DBS and the

factors was obtained with coded variables as follows:

Fig 4.22 shows the effect of mass loss with the change in the variables (factors) within the

experimental range. So from the perturbation curve, it can be interpreted that temperature and

time are the significant variables which affect the release of volatiles during devolatilization.

This can also be justified by the equation given by the RSM that the intereaction of temperature

and time is the only interaction parameter which is affecting the mass loss. In the fig 4.23, effect

of temperature and time on the mass loss is shown and with the inrease in temperature and time,

relaese of volatiles is also significantly increasing.



47

Fig 4.22: Perturbation curve Fig 4.23: Effect of temperature and time

on mass loss by holding DBS at 0

4.4.3. Comparison of RSM and ANN models

At this stage, comparison criteria are needed to quantify the difference between values produced

by the models and the actual values. To evaluate the performance of the constructed ANN and

RSM, two statistical estimators which are the root mean squared error (RMSE) and coefficient of

determination (R 2) were used:

∑ ∑

For the validation purpose, few of the samples from the experimental work were taken which do

not belong to the training data set in ANN. The actual and predicted values are presented in

Table 4.17.



48

The RMSE for ANN and RSM were found as 0.073and 0.103. These results indicate that the

RSMs prediction has a greater deviation than the ANNs prediction. R 2 values for ANN were

found as 0.9363 and 0.8942 respectively. R 2 provides a measure of how well future outcomes are

likely to be predicted by the investigated model.

Table 4.17 Validation data

Run

Temp.

(oC)

Time

(sec)

DBS

(gm)

Mass

loss

(gm)

ANN RSM

Predicted Residual Predicted Residual

1800 30 2 0.13

0.192101 0.062101 0.142445 0.012445

2800 45 2 0.23

0.340376 -0.11038 0.220364 -0.00964

3

800 60 2 0.6

0.476778 0.123222 0.321492 -0.27851

4800 75 2 0.62

0.534374 0.085626 0.442323 -0.17768

5800 120 2 0.71

0.738628 -0.02863 0.810361 0.100361

6800 135 2 0.75

0.808974 -0.05897 0.881864 0.131864

7800 150 2 0.86

0.820841 0.039159 0.905031 0.045031

8800 180 2 0.93

0.809269 0.120731 0.79947 -0.13053

9

400 30 2 0.01

0.013089 -0.00309 0.012011 0.002011

10400 45 2 0.03

0.016715 0.013285 0.021873 -0.00813

11400 60 2 0.05

0.027738 0.022262 0.037564 -0.01244

12400 75 2 0.06

0.05165 0.00835 0.06084 0.00084

13400 90 2 0.1

0.083137 0.016863 0.092927 -0.00707

14600 30 5 0.07

0.201446 -0.13145 0.070332 0.000332

16

600 45 5 0.130.210779 -0.08078 0.118051 -0.01195

17600 60 5 0.32

0.258437 0.061563 0.186862 -0.13314

18600 75 5 0.36

0.408716 -0.04872 0.278941 -0.08106



49

CHAPTER 5

CONCLUSION

RSM and ANN are modeling tools able to solve linear and nonlinear multivariate regression problems. RSM and ANN models were used to investigate the release of volatiles from dry black

liquor solids during pyrolysis process. Both models were well fitted to experimental data.

However, predictive power of ANN was found more powerful than that of CCD. On the other

hand, RSM has the advantage of giving a regression equation for prediction and showing the

effect of experimental factors and their interactions on response in comparison with ANN.

However, the main limitation of RSM assumes only quadratic non-linear correlation. Since ANN

can inherently capture almost any form of non-linearity, it can easily overcome limitation of

RSM. Another advantage of ANN is that this methodology does not require a standard

experimental design to build the model. In addition, an ANN model, unlike statistical models

operates upon the experimental data without data transformations.



50

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kshama thesis1 (repaired)

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