Systematic Equipment Performance Analysis of Canadian Kraft Mill

Through New and Adapted Key Performance Indicators

Doctoral Consortium Contributions

Radia Ammara, Louis Fradette and Jean Paris

Department of Chemical Engineering, École Polytechnique de Montreal, Montréal, Canada

1 RESEARCH PROBLEM

Lower paper prices and demand, external

competition and high energy and chemical costs

have caused economic problems for the Canadian

pulp and paper industry. As a result to this

precarious situation, significant efforts are being

undertaken to transform the pulp and paper industry

into an efficient a profit-oriented industry. A pioneer

solution to address this issue is the retrofitting of

biorefineries into existing mills. This alternative

helps P&P mills diversify their product portfolio and

generate new revenues. However, this

implementation requires additional supply of energy.

Thus, a key step to be undertaken before

implementation of a biorefinery option is the

optimization of a mill with respect to energy and

material. Several process integration (PI) techniques

such as pinch analysis or mathematical optimization

showed interesting results when applied in

methodological way to a P&P mill (Kermani et al.,

2014). However, these integration or optimization

techniques implicitly assume that the unit operations

and equipments in place operate efficiently and as

intended to (Moshkelani et al., 2013), which is often

not the case in a real Kraft mill. On the other hand,

there is no incentive in seeking to optimize a

process, when it does not fairly represent the real

system. The results of the optimisation technique are

in this case biased and not trustworthy. Equipment

performance analysis is a necessary prerequisite step

to be undertaken prior to any optimization or

enhancement measure. The assessment of equipment

performance applied in a strategic and

methodological way using adapted key indicators

can help identify areas with poor efficiency,

diagnose the causes of inefficiencies and propose

improvement projects with low investment cost and

that can significantly reduce the operating cost of the

mill (Keshtkar, 2013); (Mateos-Espejel et al.,

2010a); (Mateos-Espejel et al., 2010b); (Mateos-

Espejel et al., 2011a); (Mateos-Espejel et al., 2011c).

2 OUTLINE OF OBJECTIVES

The main objective of this research study is to

develop a strategic methodology to evaluate the

performance of unit operations using new and

adapted key performance indicators. The systematic

approach allows to:

 Systematically identify areas and equipments

with poor performance from the perspective of

energy, water and raw materials utilization,

through the use of new and adapted key

performance indicators.

 Diagnose effectively the causes of inefficiency.

More than one KPI will be used to characterize

the unit operation and thus allowing a better

understanding of performance.

 Propose improvement projects to address the

found inefficiencies.

At the end o this research study, a practical step by

step guideline will be provided to the mills managers

to help them characterize their unit operations,

analyse their energy efficiency, examine their

utilization of material and resources and diagnose

the performance of key unit operations with more

than one KPI for the purpose of overall performance

monitoring, control and enhancement.

3 STATE OF THE ART

Pulp and Paper industry ranks fourth in terms of

energy consumption among industries worldwide.

Globally, in 2007, the P&P industry accounted for

approximately 5% of total world industrial final

energy consumption. The pulp and paper industry

produces various types of pulp from virgin materials

(wood and non-wood) and recycled materials (waste

paper) that are subsequently processed into paper

produced in either integrated or non-integrated mill.

The Kraft process is the prevalent pulping

Ammara R., Fradette L. and Paris J..

Systematic Equipment Performance Analysis of Canadian Kraft Mill Through New and Adapted Key Performance Indicators - Doctoral Consortium

Contributions.

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process in North America because of its closed loop

recovery of pulping chemical and its strong and

bright produce. A simplified schematic of the Kraft

process is shown in figure (1).

Kraft process consists of two main parts: a paper

line and a chemical recovery line. The paper line is

composed of four departments: digesting where the

woodchips are cooked, washing, bleaching and

chemical preparation where the remaining lignin is

removed from the fibers, and finally the paper

machine.

Figure 1: Simplified diagram of the mill Kraft process.

The core of the Kraft process is the chemical

digester (also called cooking vessel) in which the

individual cellulosic fibers are separated from lignin

to form pulp. A series of counter-current washers is

used to separate the black liquor from the fibers in

the washing department. The purpose of bleaching is

to remove the leftover lignin and brighten the fibers.

Modern bleaching is achieved through a continuous

sequence of process stages using different chemicals

and conditions in sequence. The bleached pulp is

then sent to paper machine. The chemicals used on

site are prepared in the chemical preparation

department. On the other hand, the chemical

recovery line consists of three main departments:

evaporation, recovery boiler, and chemical recovery.

The weak black liquor (spent cooking liquor coming

out of the digester) is concentrated in multi-effect

evaporators and then concentrated before being

burnt the recovery boiler to produce steam for the

process and recover the spent chemicals. The latter

is sent to the chemical recovery plant where a series

of reactions take place and the white liquor is

regenerated and sent back to the digester.

Process integration (PI) is an efficient approach

that allows industries to increase their profitability

through reduction of energy, water and raw material

consumption and thus reducing their operating costs.

PI embodies several conventional methods such as

pinch analysis, mathematical optimization or hybrid

techniques combining both the thermodynamics of

pinch and the numerical computations of the

mathematical optimization.

The problem of these process integration

techniques implicitly assume that unit operations

and equipments in place operate efficiently or as

intended to (Moshkelani et al., 2013). In reality, this

is often not the case. Equipment performance

analysis is a necessary prerequisite step prior to

undertake any integration measure. However, this

prerequisite step of evaluation of the process is

either simple or nonexistent in the actually available

and published Kraft or integrated biorefineries

performance enhancement/optimization methodo-

logies. There are no clear guidelines on how to

define a base case and how to evaluate a process for

complete performance efficiency in terms of energy

and resources utilization. There is no systematic or

strategic methodology for a complete equipment

performance analysis. Also, the key performance

indicators available and used are not well suited to

efficiently identify and diagnose the causes of

inefficiencies in the mill, in terms of process

performance or energy and material utilization

(Villarreal, 2011); (Mateos-Espejel, 2009).

On the other hand, pulp and paper mills have

several recycle streams that increase process

interactions, which makes conventional pinch

analysis or process integration techniques ill-suited.

These techniques are applied to tackle energy

efficiency without regards to the interactions of the

complete process systems. Thus, fail to address the

problems or the entire process with its interactions.

An equipment performance analysis when

strategically developed and applied may consider

both specific operating conditions of a unit operation

and its interactions with other sections of the

process. Many key performance indicators have

been developed and applied; however, they evaluate

the efficiency of a process or a unit operation by

comparing it to other mills and not to their own

theoretical maximum efficiencies. These KPIs may

be applied as a pre-benchmarking study helping

mills managers situate their energy consumption

performance comparing to that of other mills.

However, the available KPIs do not take into

SMARTGREENS2015-DoctoralConsortium

account operating conditions and design parameters.

This makes them not very suited for a good

performance analysis.

Dimensional analysis is a mathematical system

using conversion factor to move one unit of

measurement to a different unit of measurement

(Langhaar, 1951). The basic idea of dimensional

analysis is that physical laws do not depend on the

arbitraries in the choice of units of physical

quantities. Every physical equation or relation

between variables and/or dimensioned constants

should be dimensionally consistent. In other words,

each term of the equation or relation should have the

same dimensions. Dimensional consistency imposes

a certain number of constraints that are functional

relations between the variables. This constitutes the

main principle for dimensional analysis.

Manipulating variables to create dimensionless

groups or numbers to describe the physical

phenomenon has widely been used in the chemical

engineering or fluid mechanics field such as

Reynolds number (Re) to describe the type of flows

in all types of fluid problems, Froude number (Fr),

for modeling flow with a free surface, or Nusselt

(Nu), Biot (Bi), Peclet (Pe) for heat transfers or

Carnot (ƞ) for energy efficiency. Hence, it is a

pertinent idea to create performance indicators based

on dimensional analysis.

Pulp and paper industries are driven by steam,

water and chemicals which makes them suitable for

exergy studies. Exergy analysis is a valuable tool to

evaluate the efficiency of a process. However, it has

not evolved into a systematic method, such as Pinch

Analysis or Water Pinch and has not been applied on

a real Canadian Kraft mill, in combination with

other tools for equipment performance analysis.

Moreover, traditional energy studies only consider

thermal energy. Exergy analysis considers all forms

of energy and also the internal energy of the matter

called chemical exergy.

Most published studies on performance

evaluation analysis or energy improvement methods

are based on computer simulation models. A

recurrent problem of process simulation is the lack

of explanation or information of how the data, used

for all analyses, were gathered or treated. The

simulation models are often not based on real

reconciled mill data. There is no incentive in seeking

to optimize a model, when it does not match the

actual behavior of the real plant. A representative

model based on reconciled data is a prerequisite step

to any optimization or evaluation measure.

However, lack of data redundancy in real Kraft mills

has made data reconciliation complicated or

unfeasible. No data reconciliation of a complete

operating Canadian Kraft mill has been published.

There have been studies on data reconciliation on

Canadian newsprint mills, but never on a real Kraft

mill (Bellec et al., 2007); (Jacob and Paris, 2003).

4 METHODOLOGY

To perform a complete equipment performance

evaluation, the overall unified methodology shown

in figure 8 is developed and applied. It consists of 6

main steps. The first step is to obtain a coherent

model simulation that represents a steady-state of the

process. To do so, real mill data collection, gross

error detection and data reconciliation have been

performed. Mill measurements data are collected for

a chosen period of time. Since measurements

inherently contain random errors due to sensors

noise, the mass and balance around unit operations

often do not balance. Data reconciliation is an

optimization problem that aims to minimize the

weighted sum of squared differences between the

measured and the reconciled values under

constraints that correspond to mass and heat balance

(Bagajewicz, 2000); (Leibman et al., 1992); (Maquin

et al., 2000); (Maquin et al., 1989). On the other

hand, while DR is meant to correct random errors,

gross errors due to a sensor failure should be

detected first (Maronas and Arcas, 2009). This is

done by verifying that all measurements remain

within acceptable data range. Many statistical tests

have been developed. However, they have never

been applied on a real operating mill (Dewulf et al.,

2008); (Gong and Wall, 1997); (Gong and Wall,

2001); (Regulagadda et al., 2010); (Sato, 2004). The

results of the GED and DR show largely adjusted

areas. This helps identify possible process leaks or

biases present in the system (Krishnan-Dumitrescu,

2008). DR allows getting a coherent process model

that represents a steady-state of the studied mill and

also identifies a preliminary list of suspected

problematic unit operations. Largely adjusted areas

are highlighted for further analysis.

From the coherent steady-state of the process,

exergy analysis of individual unit operations and of

entire departments of the process has been

performed. Exergy is a measure of both quality and

quantity of the energy involved in transformations

within and across the boundaries of a system. Unlike

energy, exergy can be destroyed or lost, and thus

unavailable for future transformation with the

process system. Hence exergy analysis allows

SystematicEquipmentPerformanceAnalysisofCanadianKraftMillThroughNewandAdaptedKeyPerformance

Indicators-DoctoralConsortiumContributions

identifying poor energy performance areas and gives

insight on how well exergy is used onsite.

Data reconciliation and exergy analysis help

target problematic areas. A list of suspect poor

performance efficiency equipments is established.

Figure 2: Overall methodology.

Adapted key performance indicators are

developed to characterize and describe the process

efficiency of the listed suspect equipments. A

synthesis of improvement projects is then

performed.

A list of priority improvement projects are

proposed and recommended to the mills, in order to

improve their overall efficiency.

Key performance indicators are developed by

doing a dimensional analysis study around each unit

operation. More than one KPI is used to describe the

performance of the phenomena that takes place in a

unit operation.

Figure 1 schematically displays the proposed

overall methodology of the PhD study.

5 EXPECTED OUTCOME

Figures 3 and 4 display the results from the three

first steps of the methodology. Figure 2 displays the

largely adjusted variables in the process after data

reconciliation. 53 measured variables have been

considered to be able to obtain an observable

representative system, suitable for reconciliation.

The variables being largely adjusted are gathered

around specific unit operations suspected of poor

performance efficiency or process leak or biase.

Data reconciliation and gross error detection

identified the washing and digester departments and

more precisely the steaming vessel (in the digester)

and the 2 brown stock washers to be inefficient.

DR and GED fail to identify or locate the error

without any doubt. It should be combined with other

tools to effectively locate the error and diagnose the

cause of inefficiency.

Figure 3: Largely adjusted areas after data reconciliation.

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Figure 3 displays the results of the exergy

analysis performed around all unit operations and

sections of the case-study process. Chemical,

electrical and thermal exergies have been

considered. Kinetic and potential exergies have been

neglected. Exergy efficiency has been defined as the

ratio of the exergy of useful output product over the

useful exergy input. This definition is very

appropriate from engineering and practical point of

view as the lost or unavailable exergy is not used

within a system and the transiting exergy does not

contribute in any exergy transformation and thus

should not be considered in the calculation of the

efficiency. The digester, washing, recausticizing,

bleaching and steam plant departments are areas

where energy savings are possible. Exergy

destruction could be reduced in the digester and the

recovery and power boilers. Exergy lost from the

washing and steam plant could be reduced by

recovery of the heat content of the effluents and

stack gases. This is possible either by PI techniques

and/or by combination with the installation of heat

upgrading systems.

Figure 4: Exergy efficiency per department (%).

The steaming vessel, the brown stock washers 1

and 2, the D

EOPD

bleaching towers, the recovery

boiler and 4 power boilers and the clarifiers should

be further investigated using more specific and

adapted KPIs to diagnose the cause of inefficiencies.

This is done in steps 4, 5 and 6 of the methodology.

Dimensional analysis of key equipments gave

rise to a number of nondimensional groups with

significant physical meaning and that describe the

phenomena that takes place in the unit operation.

The main characteristics of these non-dimensional

groups are that they describe the phenomena looking

into account operating conditions and design

parameters which makes them suitable for KPIs

developments.

At the end of this PhD thesis, practical and

adapted KPIs will be developed for all key

equipments of the Kraft process. A practical

stepwise guideline will be provided for mills

engineers to assist them in their equipment

performance evaluation.

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