DESIGN PROCESS MODEL FOR OPTIMIZING DESIGN

OF CONTINUOUS PRODUCTION PROCESSES

Mika Strömman, Ilkka Seilonen, Jukka Peltola and Kari Koskinen

School of Electrical Engineering, Aalto University, Espoo, Finland

Keywords: Multidisciplinary Design, Process Engineering, Optimization.

Abstract: The non-growing market situation in pulp and paper industry has tightened the competition. Cutting the

design costs by integrating design activities is not going to be enough but the design itself has to be

improved. The design of continuous production processes can be enhanced by utilizing optimization

techniques during the design process. The benefit of the optimization techniques in process design depends

on adequate usage of them during the design process. However, this paradigm shift will require changes in

the existing design processes. In this study, the required changes are identified and a new design process

model describing the optimizing design utilization is developed. The model is then assessed through a case

study and an interview study to ensure that the design process can be realized in the conceptual design phase

of a real delivery project.

1 INTRODUCTION

The market situation in pulp and paper industry have

is setting requirements for the design methods. The

design process itself has to be conducted efficiently,

but in the last years the costs has already been cut

off with better project management and concurrent

engineering. One possibility for rationalization lies

in the design itself; traditionally, the design of the

plant is more oriented into structural design and less

to the optimal combination of operational and

structural design. The design problem can be

formulated as a bi-level multi-objective optimization

problem (BLMOO). Mathematical methods for

solving BLMOO problems exists and the method

have been applied in process facility design in

research projects.

However, the utilization of such optimization

methods requires enhancement of the engineering

process so that the required information for

optimization is available on the right time and the

results of optimization can be used in design. A

design process describing optimizing design of

continuous production processes hasn’t been thus far

presented and it is a necessity for adopting BLMOO-

methods in real delivery projects.

This research has been conducted as a part of a

larger research project in which the objective is to

develop a new optimization based method for

designing a process plant. Our part of the research is

to define a model for optimizing design process and

assess the usability of that model. The research

methods of this study include experimental

definition of a business process model, case study

(with the model) and interview study evaluating the

properties of the model.

In the first chapter the related work and state of

the art is discussed. The following chapter presents

the new engineering business process which takes

into account the optimizing method. Next, a case

evaluating the new engineering business process is

presented and the observations based on expert

interviews are discussed.

2 PROCESS DESIGN AND

OPTIMIZATION

2.1 Design of Continuous Production

Systems

A process plant design is a multidisciplinary process

(process design, automation, software etc.)

(Watermeyer, 2002). Traditionally the process plant

design process has been water fall model like linear

process with stages ending to document deliveries.

492

Strömman M., Seilonen I., Peltola J. and Koskinen K..

DESIGN PROCESS MODEL FOR OPTIMIZING DESIGN OF CONTINUOUS PRODUCTION PROCESSES.

DOI: 10.5220/0003647804920501

In Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SDDOM-2011), pages

492-501

ISBN: 978-989-8425-78-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

In a delivery project, the deadlines are counted

backwards from the day that the plant should be

operational. The length of work phases are

determined based on time needed for work and

procurement (Cziner, 2006).

The plant engineering process can be divided

into steps e.g. problem analysis, conceptual design,

detailed engineering, and construction (Tuomaala,

2006). More business oriented divisions are also

possible, for example conceptual phase, pre-

feasibility study, feasibility study, investment

decision and implementation (Diesen, 2007).

Although all the phases are equally important for

reaching the goal, the focus in this research is put on

the conceptual design phase, because the

optimization methods researched in this research

project aim to solve problems on conceptual design

level. Other phases of the engineering process are

relevant to our research in that sense that the tools

and methods should be compatible to the proposed

changes.

In the conceptual design phase a very small

amount of information is available and the time and

resources are limited (Seuranen, 2006). Still the

decisions in this phase fix 80% of the total costs of

the project (Douglas, 1988). Decisions in the early

phases of the project are also quality-critical,

because the costs of changes increase tenfold in each

phase (research – process flow – final design –

production) (Bollinger, 1996). In process plant

engineering, the conceptual design phase is led by

process design. All the other engineering disciplines

are more or less in consulting role. For these

reasons, the greatest advantages can be achieved in

early phases of the business process.

Because of the shortened delivery times, the

other engineering disciplines have to begin their

work before the process design is ready. The saving

using concurrent engineering is calculated to be up

to 50% of the calendar time in a delivery project

(Bañares-Alcantara, 2005).

The sub-processes of any process design task are

design task definition, process structure design,

process operation design an design acceptance.

Process structure design and process control design

interact and should therefore be designed

simultaneously (Pajula, 2006). The existing process

design approaches can be divided to heuristic and

engineering experience based methods, optimization

based methods and case-based reasoning methods

(Seuranen, Pajula and Hurme 2001). Case based

reasoning (CBR) has been applied for design of the

pulp process. The main challenge in CBR is the need

of extensive database to provide the required

knowledge (Pajula, 2006). Outsourcing of the design

work is a common practice nowadays. Fathianathan

and Panchal (2009) have proposed a model to

support outsourcing decisions.

2.2 Optimization in Process Design

Current work practices in forest industry process

engineering are almost solely based on engineering

experience. Simulation and optimization is used in

the design of unit processes, but less in the design of

the process as whole. Plant wide simulation enables

the validation of process structure and control

concepts even before selecting suppliers and

therefore it reduces risks (Ylén, et al, 2005) and

gives a deeper understanding of the process

(Pulkkinen, Ihalainen and Ritala, 2003). According

to the interviews, plant wide simulation is more

useful when building a plant with totally new

concepts when the “rules of thumb” are not

available.

For combining the optimization of plant

structure and plant control, there are several options.

Optimization strategy can be sequential, iterative, bi-

level or simultaneous. (Fathy, Reyer, Papalambros

and Ulsoy, 2001).

Bi-level optimization has been under an active

research lately (Dempe, 2002). Still only a few

research is dealing with multi-objective bilevel

problems. Eichfelder (2010) presents an algorithm

for solving bilevel multi-objective problems. The

combination of dynamic simulator model and

dynamic optimization has been researched for

papermaking process (Linnala, et al, 2011).

2.3 Information Systems for Process

Design

The variety of the Computer Aided Engineering

(CAE) tools supporting process systems engineering

(PSE) is enormous. One of the interviewed

engineering enterprises is using over 50 different

engineering tools. A trend, as seen in modern

integrated process engineering tools, is the

transformation from document-centric design to

data-centric design, realized with database

technology (Comos, 2011),(Smart Plant, 2011),

(Bentley, 2011). Major tool vendors have developed,

acquired and integrated engineering tools from other

engineering disciplines under unified product

families. Modern process engineering support

systems combine modeling and information

management features for engineering of many

aspects of plant engineering, e.g. process, piping,

DESIGN PROCESS MODEL FOR OPTIMIZING DESIGN OF CONTINUOUS PRODUCTION PROCESSES

493

electrical and instrumentation, 3D layout, equipment

lists, part data sheets, etc, thus comprising an

integrated plant information model. This also

enables advanced change management, where

modification of an object through one view notifies

users of other views, looking at the same object.

Multi site work flow management is featured for

both engineering and commissioning. Integration to

external CAE tools is possible through export and

import interfaces using standard or proprietary data

formats. An important prerequisite for cost efficient

integrated engineering is the use of common data

models defined in the standards. ISO 15926,

”lifecycle data for process plant” (ISO 15926) is a

standard dedicated to the process industry, widely

accepted by tool vendors. It has a central role in

pursuing information interoperability between

engineering systems and it is used in many plant

information exchange tool initiatives, such as iRing

(iRing 2011) and XMpLant (XMpLant, 2011) and

even as a native data model of a plant modeling tool

(Bentley, 2011).

Plant information models and semantic

technologies have induced much academic research.

For example, POSC Caesar association (POSC

Caesar, 2011) assembles R&D around the ISO

15926 and modeling methods, such as (Batres, et al,

2007). However, Wiesner, Morbach and Marquardt.

(2010) questions whether a single global plant

information standard is a realistic goal in the first

place and suggest a semantic integration framework

OntoCAPE.

3 MODEL FOR OPTIMIZING

DESIGN PROCESS

Optimizing process design is here modelled in terms

of a business process model. The model describes

the stakeholders of the optimizing process design

and their activities together with the data, knowledge

and utilized mathematical models. Based on these

the requirements for IT support are identified.

Process design as an optimization problem

The process design task can be considered as an

optimization problem. There are a few general

requirements for the process. The process must be

operable, reliable and yield products of sufficient

quality with minimum operational cost. On the other

hand the investment and maintenance cost of the

process should be minimized as well. On this basis it

is natural to consider and model the design problem

as a bi-level multi-objective optimization problem.

The mathematical representation of the general bi-

level multi-objective optimization problem is:

(1)

where

F(x) are the upper level objective functions,

f(x) the lower level objective functions,

G(x), g(x), H(x) and h(x) the upper and lower level

inequality and equality constraints. (Dep and Sinha,

2008)

There are multiple methods for solving bi-level

multi-objective optimization (BLMOO) problems

(Eichfelder, 2010) and (Branke, Dep, Miettinen,

Slowinski, 2008) and the solution method should be

chosen according to the problem itself and the

possibilities for interaction with the decision maker

(Miettinen, 1999). In the plant design process, there

is a logical division to optimization levels, so that

plant structure is the upper level (F(x)) and the

operation of the plant is the lower level (G(x)). The

nature of the plant design is also multi-objetive; the

balancing between design parameters as for example

the total cost of the plant, operational costs,

production quality, production volume and expected

oee-value is difficult and the decision of these values

belongs to the plant owner, not the designer.

Therefore the gathered requirements should also

cover business oriented user preferences.

In this research the solution of the optimization

problem was simplified by scalarizing the lower

level optimization problem, but this simplification

has no affect to this part of the research focusing on

the business process of the design.

3.1 Stakeholders

In the model of optimizing design, new stakeholders,

an optimizer and a modeler, are added to the group

of stakeholders involved in process design as

illustrated in Figure 1. The optimizer is an expert of

mathematical optimization whose responsibility is to

help the process designer in finding more optimal

process designs. The optimizer also needs to

cooperate with the modeler in order to be able to

take into account the operational aspects of the

designed process. These cooperation connections

with the optimizer will also change the work of the

process designer and modeler. Successful

{}

)

(, 1

() 1 ())

() ( )

min () ( (),... ()),

arg min ( ),..., ( ) 0, ( ) 0 ,

() 0, () 0,

,1,...,.

xx M

ix mx

iii

Fx Fx F x

subject to x f x f g x h x

Gx Hx

xxxi n

∈≥=

≥=

≤≤ =

SIMULTECH 2011 - 1st International Conference on Simulation and Modeling Methodologies, Technologies and

Applications

494

cooperation between the stakeholders is a necessity

for useful design optimization.

Figure 1: Stakeholders of optimizing design.

The role of the optimizer can be described as an

analyst 0. His responsibility is not to make decisions

about process designs but to produce useful

information for the designer about possibly better

designs. In order to do this, the optimizer will need

to have expertise in multi-objective optimization and

familiarity in process design. The adoption of

optimization also changes the roles of preexisting

stakeholders. Designer is the decision-maker of the

process design and the client of the optimizer. In

optimizing design the designer has to select a part of

his design problem for optimization together with

the optimizer. In addition to this, the designer also

has to cooperate with the optimizer during the

optimization process and finally interpret the results

and decide how to apply them. Again, the optimizer

will become the client of the modeler.

In order to adopt the new business process, all

the stakeholders should gain some advantage of the

enhanced business process. The process designer

gains competitive advantage by offering design that

is more tailored and more cost effective along the

life cycle of the plant. For the optimizer and

modeler, the new model opens a totally new

business possibility.

3.2 Business Processes

Dynamic and stochastic nature. In this subtask the

designer and the optimizer can rely on the expertise

In the model of optimizing design the activities of

process design have partially changed. The basis for

the activities is the existing design processes that are

extended and partly modified. The suitable time for

optimization is the conceptual design phase. When

the designer identifies a need for optimization in his

conceptual design, he initiates cooperation with the

optimizer. During this cooperation an optimal design

balancing both structural and operational aspects of

the design are being searched for. This process can

be described as expert cooperation in which also the

modeler will be included.

Figure 2: Activities of optimizing design.

The optimization activities take place in a few

stages as an extension to conceptual process design

phase as illustrated in Figure 2. The process starts

from optimization problem definition and continues

through optimization problem-solving until result

interpretation. During these stages different

cooperation patterns between the designer, optimizer

and modeler are needed. The whole process and

each of its stages may also be iterative.

The purpose of the optimization problem

definition is to define a part of the designer's design

problem as BLMOO for the optimizer. This stage is

performed by the designer and optimizer together.

The designer identifies parts of the overall design

problem in which balancing structural and

operational aspects of the design is essential. The

solvability of the problem is then assessed by the

optimizer, designer and modeler together. The

assessment requires expertise of all three

stakeholders because the result depends not only on

the problem itself but e.g. optimization tools,

process models and data about the process.

Eventually the designer and the optimizer should

agree on a useful and solvable design optimization

problem, which the optimizer then formulates as a

BLMOO problem.

An important subtask of the optimization

problem definition is process operation modeling.

Modeling the operational part of the design problem

is much more difficult than the structural part due to

its of the modeler. The modeler is expected to have

expertise about both mathematical modeling and the

DESIGN PROCESS MODEL FOR OPTIMIZING DESIGN OF CONTINUOUS PRODUCTION PROCESSES

495

designed process itself, i.e. its chemical and physical

characteristics. Based on his expertise the modeler

should be able to create such operational models that

are suitable to be used in optimization. The

suitability of the models will be assessed by the

optimizer and the designer.

The stage of problem-solving is focused on the

optimizer. However, cooperation with the other

stakeholders is likely to be needed also in this stage.

In the beginning of this stage the data and models

required in the optimization are expected to be

transferred to the optimizer in a form which he can

utilize. Depending on the utilized MOO method,

different type and amount of cooperation with

designer will be needed also during the actual

problem-solving. According to an interview (see

chapter “Interviews”) industrial experts seem to

favor optimization methods which lead to

representations of Pareto optimal designs.

The last stage of optimizing design is result

interpretation. Also this stage is performed in

cooperation between the designer and the optimizer.

The optimizer prepares result presentations, which

indicate Pareto optimal designs and help the

designer evaluate the impact of his preferences on

the design. The designer is expected to study the

design optimization result, assess its reliability and

make decision about possible changes to his design.

This is not necessary a straightforward task and is

likely to require assistance from the optimizer and

the modeler. The reliability of the optimization

result is dependent on used operational models and

data. Sensitivity analysis of the result might also be

needed. In the end, the designer can adopt changes

to his design or reject the optimization results and

reformulate the optimization problem with the

optimizer.

3.3 Data, Knowledge and Models

The optimizing design requires additional

knowledge, data and models than the state-of-the-art

approaches to process design. The new requirements

originate from the need to solve the process design

BLMOO problem. The new requirements for

knowledge, data and models in optimizing design

are summarized in Table 1. In addition to these, the

previous requirements are still valid, e.g. designer

knowledge for process design, use of design data

and design models.

The expertise and knowledge of the stakeholders

involved in optimizing design is complementary.

The designer has knowledge about industrial

processes and their design, customer requirements

and evaluation of process designs. Meanwhile, the

modeler is expected have knowledge about similar

processes and their mathematical modeling.

Table 1: Knowledge, model and data requirements in

optimizing design.

Knowledge Data Models

Designer

Process design,

process

knowledge, some

understanding

about

optimization

Design data,

customer

requirements

Flow diagram

P&ID

Plant Model

Optimizer

Optimization,

some

understanding

about design

Design data

and operational

data from

designer and

modeler

Operational

and design

level problem

formulation

models for

optimization

Modeler

Modeling,

Process

knowledge

Operational

data, some

design data

Operational

models (e.g

break

probability

model,

The knowledge of the optimizer concerns about

optimization and acting as an analyst in a decision-

making process of MOO. However, during the

activities of the optimizing design combination of

the knowledge of different stakeholders and

knowledge transfer between them is necessary. A

partially common understanding of the design

problem shared by the stakeholders has to be created

(Konda, Monarch, Sargent and Subrahmanian,

1992). This is may be done according to the

BLMOO of the process design.

Mathematical models of the designed process

have an important role in optimizing design. Models

are needed particularly for modeling the operation of

the process. Mathematical models have been used in

the design of continuous processes also previously,

e.g. in simulations (Ylén, et al, 2005), but these

models are not necessary suitable to be used in

optimizing design. In order to be able to be utilized

in optimizing design, the operational models need to

have a suitable balance of modeling capability and

computational requirements. The computational

requirements can be met by modeling only selected

parts of the process. More precise models may be

utilized after the design in a design validation stage.

The optimizing design requires data transfer

between the optimizer and other stakeholders, which

is not needed without optimization. The most

important data transfer takes place from the designer

to the optimizer. The designer has to pass the most

SIMULTECH 2011 - 1st International Conference on Simulation and Modeling Methodologies, Technologies and

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496

of the data describing the design optimization

problem to the optimizer, e.g. flow diagrams,

dimensions of equipments etc. The other source of

data to the optimizer is the modeler. He is expected

to deliver to the optimizer the operational models

and the data required by them, e.g. model describing

the probability of break. This data is intended for

algorithmic processing, which indicates a

requirement for adequate precision. The final data

transfer consists of the optimization results, which

are passed from the optimizer to the designer. This

data has a form of a document. A major requirement

for it is understandability.

3.4 Requirements for Information

Systems

The new requirements for the information systems

mainly rise from the new data flows between

designer, optimizer and modeler. The amount of data

from the designer’s plant model can be quite huge,

so the optimizer should have access to the designers

plant model tool to be able to import the needed set

of design data. A new thing is that the designer

should also include the constraints of the design to

the model when applicable. The design data should

be transferable to optimizing tool as well as the

models that the modeler has created. The support for

representing the alternatives to the designer is not

that critical, because that document should be kept

brief and simple.

4 ASSESSMENT OF THE MODEL

In this chapter, the business process model of

optimizing design is assessed through a small-scale

case study. This case study was carried out as a part

of a wider research project and the results of the

mathematical solution of the BLMOO in this case

can be found in our partners’ publications

(Ropponen et al., 2010), (Eskelinen, et al., 2010),

(Ropponen et al., 2011) and (Ropponen, Rajala,

Ritala, 2011). The case was evaluated by internal

review and expert interviews.

4.1 Case Study

4.1.1 Case Design Problem

The design task in the case study was to dimension

six storage towers of a part of a paper-making

process and to guarantee the runnability and stability

of the process. The dimensioned storage towers

Figure 3: Flow diagram of the process in case study.

include TMP (thermo-mechanical pulp), chemical

pulp, wet broke, dry broke, clean water and 0-water.

The design problem is illustrated in Figure 3 and

further explained in (Ropponen, Rajala, Ritala

2011).

4.1.2 Stakeholders

The actors involved in the design process in the case

study include the designer, optimizer and modeler.

The roles were manned by research teams involved

in the project.

The designer had the main responsibility of the

project. He carried out the requirement elicitation

with the end-customer, proposed a conceptual design

and initiated the problem formulation for the

optimization. He then had a key role in data

acquisition for the model building. After getting the

optimization results, he made the decisions

according to the end-users preferences.

The optimizer participated in the problem

formulation by having an opinion what kind of

problems can be solved with optimization. After the

problem formulation, the optimizer then asks the

modeler to build necessary models for optimization

and then chose the right optimization method.

Finally, a suitable method for presenting the results

was chosen.

The modeler was responsible for creating a

model simple enough to be calculated. The modeler

was also responsible to make sure that the

simplifications do not affect to the problem to be

solved.

TMP Chem pulp

Broke

(deficit/

surplus)

Clean water

Separation

0-water

Paper Machine

Quality

control

Dry broke

storage

DIL DIL

DIL

Mixing

TMP

Pulpers

Fresh

water

Fresh

water

Evaporated

water

Paper,

net

Paper,

brutto

Filler

DESIGN PROCESS MODEL FOR OPTIMIZING DESIGN OF CONTINUOUS PRODUCTION PROCESSES

497

4.1.3 Business Process

The project could be divided into four main tasks:

problem formulation, model building, problem

solving (optimization) and result interpretation.

Problem Formulation

At the starting point of the case study a part of the

conceptual design was already performed, e.g. the

number of storage towers and material flows

between them was defined. The designer and

optimizer then discussed the possibilities for a

manageable optimization problem. They designed

that the optimization activity concerns only about

operation design and the dimensioning part of the

structure design. Also the amount of optimized

parameters was reduced in negotiations between the

designer and the optimizer. During the optimization

activity a mathematical model of the problem was

created and used for finding an optimal design under

the specified requirements. The design problem was

formulated as follow:

{}

,max)

()

(() )

min

(())

(( 1) ())

Filler Filler

bw bw

strength

Eq nq

Eqnq

Eq n

Eun un

⎧⎫

⎪⎪

−

⎪⎪

−

⎨⎬

⎪⎪

+−

⎪⎪

−

⎩⎭

∑

(2)

where (

,

) is the investment cost of the 4

selected tower volumes, 



is the time till one of the

towers goes empty or flows over, and E

{} denotes

the expectation value of the system performance as

Ψ is the stochastic process with applied dosage

policy. (Ropponen, Rajala, Ritala, 2011)

The operational problem, i.e. the lower level of

the BLMOO was formulated as:

,max

()

1..

,min

(

1..

()( ( ) )

min

()(( ) ( 1)

(( ) )

max

()

(( ) )

max

Filler Filler

strength strength

kq n k q

k qbw n k q

kq n k q

kun k un k

pV n k V

+−

+− +−

∑

)

,min ,max

()

jjj

st u u u

⎧⎫

⎪⎪

⎨⎬

⎪⎪

⎩⎭

≤≤

(3)

where 



, 



, and 



are the quality

variables with 



s as their nominal values, 



is the

optimization horizon,

(k) a time-wise weighting

factor, u is a vector of pulp/water flows to be

controlled,





()

(



)

and 



()

(



)

are the accepted

risks for a tower overflow/goes empty k time steps

from the present time n defined as





(/)

(



)

1−1−



(/)





, i refereeing to the storage towers

for clean water, 0-water, broke, and dry broke. V

i,max

is the volume of the ith storage tower, i.e. the

maximum amount of pulp/water in the tower, and

i,min

is the minimum amount of pulp/water in the

tower. U is the control variable describing the broke

dosage from the broke tower to the system

(Ropponen, Rajala, Ritala, 2011).

Simplified, on the operational level we optimize

the variances of the quality attributes of the paper

and the broke dosage and the probability of

under/overflows. On the design level, we optimize

the design according to the sizes of the tanks and

expectation values of the system performance.

Model Building

At the same time that the optimizer negotiated with

the designer about the problem formulation, he had

to discuss with the model builder if a suitable model

for the problem can be built. In this discussion there

were two main themes: is the physical phenomenon

of the problem known or is there enough data to

model the problem stochastically and can the model

be simple enough that it can be calculated fast

enough in the optimization loop.

Optimization and Result Interpretation

In this case example, the tasks of problem

formulation, model building and optimization were

performed simultaneously and were highly iterative.

The main focus of the case example was in

optimization. The results of the optimization are

described in (Ropponen et al., 2011) and (Ropponen,

Rajala, Ritala, 2011).

After the optimization, the results were presented

to the designer as two-dimensional Pareto optimal

sets. In Fig.3, a Pareto optimal set in respect to the

two most important parameters is presented. The

designer then made the decisions e.g. between a

decent investment cost and an acceptable probability

of break.

4.1.4 Data, Knowledge and Models

The designer in this case had a wide experience in

process design, paper making, modeling and

optimization. The optimizer was mathematically

oriented, but had only minor experience on paper

SIMULTECH 2011 - 1st International Conference on Simulation and Modeling Methodologies, Technologies and

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making or process design. The modeler was familiar

with process modeling and optimization.

The largest data flow in the process was from

designer to optimizer. The designer had to

communicate the customer requirements, the

original design about the structure and operation and

the freedoms and limitations for optimization in the

design. The main models for this communication

were a process flow sheet and steady-state model of

the process. Making of these models was mainly a

task for the designer. The designer was able to

formulate most of the limitations and requirements

in numerical form, e.g. the probability of the break

may not be greater than Pmax. Due to the nature of a

first time project, the data transfer between the

optimizer and modeler was also huge.

Modeller was responsible for building three

models: dynamic model, predictive model and a

validation model. The two first mentioned were used

in optimization while the validation model build

with different simulation software was used only for

one selected design.

Practically, the problem formulation and

optimization required simultaneous model

development, because there wasn’t previous

knowledge about feasible models.

The results of the optimization were delivered as

a document containing simulation graphs and Pareto

optimal sets (one example in Figure 4) of

optimization results.

Figure 4: Design solutions in respect to investment cost

and time until production stop. Pareto optimal set of

designs circled. (Ropponen, Rajala and Ritala, 2011).

4.1.5 Information Tools

This case example was carried out as a research

project, and therefore the engineering tools used

didn’t match the ones used in industry. MATLAB

was used both for the optimization and simulation

for optimization. APROS process simulator was

used in validating the results of optimization.

4.2 Interviews

In order to get information of the process

engineering business process today and to validate

the proposed changed to the process in order to

adopt a new optimizing design process, a set of

interviews were performed. The interviewees

represented actors in both chemical and pulp &

paper industries and contained process designers,

automation designers and IT-system experts in

process design companies and engineering

enterprises. In addition a simulation expert and an

optimization expert were interviewed.

The topics of the interviews were motivation and

feasibility of optimizing design, current design

practices vs. optimizing design and IT systems vs.

requirements of optimizing design.

The following observations could be made about

issues concerning the motivation and feasibility of

optimizing process design:

There are business requirements to decrease the

costs of plant design projects. At the same time the

quality of the design should be increased and cost

decreased. The effect of optimizing process design

process on all three aspects (design quality, design

cost, project cost) should be taken into account.

The process design practices in different

industries are heterogeneous. In paper and pulp

industry process design can be characterized as

engineering-oriented, i.e. an engineering design

system is the primary design tool. As a comparison,

in chemical industry process design is quite

simulation-oriented, i.e. a simulator is the primary

design tool. The design practices of chemical

industry are closer to the optimizing process design

process than the ones in paper and pulp industry.

The following observations could be made about

issues concerning the differences between current

design practices and optimizing design:

Cooperation between different parties involved

in a design project has recently been emphasized by

engineering companies. Cooperation is needed for

the efficiency of a design process, e.g. finding out

the requirements of the customer early enough,

ensuring consistency of the designs from different

designers and handling the effects of design

changes. The optimizing design process should fit to

the cooperation practices.

The design of a process is divided to several

designers according to different systems or parts of

7000 8000 9000 10000 11000 12000

1/time (x10E-3)

Investment cost

DESIGN PROCESS MODEL FOR OPTIMIZING DESIGN OF CONTINUOUS PRODUCTION PROCESSES

499

the process. This is done due to the different

expertise of the designers and concurrency of the

design work. There are usually some buffers in the

design between the designs by separate designers.

From the optimization viewpoint this division is

questionable. The optimizing design process is likely

to change the division of work.

The division of work is also reflected to current

optimization practices. They are optimizing unit

processes rather than the whole process. The

optimizing design process should change this

practice, too.

The trust of the customer on the feasibility of the

process design in a very important issue, which is

affected by many factors, e.g. references of the

vendor and difference of the design to existing ones.

It was mentioned that particularly in the paper and

pulp industry customers do not trust simulations as a

process design validation tool. Validation of the

design results should be a primary concern also in

optimizing process design.

The following observations could be made about

issues concerning the differences between

requirements for current IT systems and IT systems

when using optimizing design:

The IT-architecture of an engineering company

is usually quite heterogeneous, i.e. there are several

different IT-systems used during a design project.

Sometimes there are even several alternative IT-

systems for same design tasks, e.g. due to customer

requests. The heterogeneity of IT-systems may

hinder the implementation of IT-support optimizing

design.

There is a slowly progressing shift from

document-centered design paradigm to data-centered

design paradigm in plant design. The optimizing

process design process should be made to fit the data

model -oriented design paradigm because its

meaning seems to be increasing in the future. It is

also likely to be more suitable basis for optimizing

design than the older document-oriented design.

5 CONCLUSIONS

In this paper a business process model for

optimizing design in continuous process facility

engineering has been presented. This model was

considered from the viewpoints of stakeholders,

process, knowledge, data, models and tools. The

model for optimizing design was then assessed by

applying it in an experimental case study and by

interviewing experts.

Based on this study, a few conclusions can be

made. The greatest change is the new roles of

optimizer and modeler, which make the process

more iterative between optimizer and process

designer. The new roles require a shared knowledge,

because the work can be described as expert co-

operation. The business process in optimizing design

is more iterative than in traditional design because of

the need for negotiation in the problem formulation

and the uncertainties in the modeling. In addition to

this, the interviews also illustrate the importance of

validation of process designs. Validation of the

designs so that the customer will trust them is a

primary concern to be observed in future research.

It must be noted that the design business process

in this paper is presented at a general level and it

must be specified when used as actual process. In

future, the process model is evaluated and specified

in a larger case study.

ACKNOWLEDGEMENTS

This research was supported by Forestcluster Ltd

and its Effnet program.

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