CHAPTER 3

An Intelligent Simulation-Optimization

Framework for Sustainable Process Operations

Design and operation of chemical plants always involves a combination of

synthesis, analysis and evaluation of different alternatives. Such activities have

traditionally been driven by economic factors first, followed by engineering, safety,

and environmental considerations, in that order. However, the situation has changed

much during the recent past. Chemical companies have embraced the concept of

sustainable development as part of their core business values. This means that the

feed materials and energy must be renewable, products non-toxic and biodegradable,

and wastes minimized or even eliminated at source. Such need for sustainable

development has challenged the chemical process industries to seek new approaches

to tackle the waste problem. This includes exploitation of popular, commercial tools

such as process simulators to evaluate process retrofit options. While process

simulators are useful, one main shortcoming of their application to the waste

minimization is the lack of decision support for the non-expert user. The inherent

capability of process simulators is limited to predicting the behavior of the process

in response to changes in one or more variables. When used for waste minimization,

the overall outcome of the study is still very much dependant on the insight, skill,

27

and expertise of the user in diagnosing the traits of waste generation in that specific

case, identifying the relevant features (root causes) that control them, exploring and

generating different alternatives, and tuning the necessary variables to optimize the

process. This shortcoming of the process simulator-based approaches has indeed

been highlighted during a joint workshop organized by the US Environmental

Protection Agency, the Department of Energy, and the Center for Waste Reduction

Technologies (Eisenhauer and McQueen, 1993). Their recommendation was to

develop an integrated framework comprising of an expert system and process

simulator. The rationale behind this is that since waste minimization is a

multifaceted problem, its analysis requires the application of different computational

tools, each providing a different perspective.

In this work, a framework comprising of an expert system, process simulator,

and multi-objective optimization is proposed for both qualitative and quantitative

waste minimization analysis. The qualitative approaches of expert system are

practical, easy to use even by a non-expert and require less precise process

information. However, they only provide broad direction for improving the process.

On the other hand, quantitative approaches through process simulation and

optimization can provide precise actions for retrofitting. However, they need

specialized process knowledge. In this chapter, an intelligent framework that

combines the strength of each approach is proposed. In the following sections, each

element of the framework will be discussed but before that, a literature case study

that illustrates the different components of the framework is first described.

28

29

3.1 Hydrodealkylation (HDA) Process of Benzene from Toluene

The flowsheet of HDA process is shown in Figure 3.1 (Douglas, 1988).

Fresh toluene and hydrogen are initially mixed with a recycle stream containing

hydrogen, methane, benzene and toluene. The feed mixture is first heated in a

furnace before being passed to an adiabatic reactor (PFR-100) for the

hydrodealkylation reaction. In the reactor, toluene and hydrogen react to form

benzene product, accompanied by the generation of byproduct methane and waste

diphenyl. The reactor effluent comprising the products and unreacted hydrogen and

toluene is next condensed in a cooler (E-100), followed by separation in a flash

separator (V-100) to remove the aromatics from the non-condensable hydrogen and

methane. A fraction of the vapor stream leaving the top of the separator which

contains significant amount of methane and hydrogen is recycled and mixed with the

raw material streams while the rest is purged as waste. The liquid from the flash

separator is split into two. The first stream is mixed with the reactor effluent and

recycled back to the cooler. The second stream is passed through a series of

distillation columns (stabilizer T-100, benzene column T-101 and toluene column T-

102) to separate the benzene product and the methane byproduct from the other

components. Benzene product is obtained at 99% (mol) purity from T-101. The

distillate stream from T-102 contains high purity toluene and is recycled, while the

diphenyl stream from the bottom of the column becomes waste. The process

currently incurs an environmental impact of 1473 and an operating cost of $1504 per

hour. The objective in this case study is to retrofit the process to make it

environmentally benign – reduce waste material and energy while being

economically attractive.

30

13

Furnace

Figure 3.1 Flowsheet of hydrodealkylation process

E-Cond101

E-Cond100

E-Reb100 E-Reb101

Benzene-product

Fuel-gas

17

18

T-100T-101

16Sep-bottoms

TEE-100 12

RCY-1

TEE-101Purge

15

V-100

Sep_inletE-100

E-Cooler

14

Diphenyl-waste

E-Reb102

T-102E-Cond102

Toluene-byproduct

P-101

E-P101

Toluene-recycle

RCY-3

MIX-102

9 10 11

E-Reactor E-102 PFR-100E-Furnace

Toluene-feedP-100

E-P100

6 MIX-101

1

2

Recycle-gas

K-100

E-Compressor

5

7

8

H2-feed

MIX-100

RCY-2

3.2 Overview of the WAR Algorithm

The WAR algorithm was first developed by Hilaly and Sikdar (1994), who

introduced a concept of pollution balance based on the mass balance of pollutants.

Cabezas et al. (1999) developed a generalized WAR algorithm based on the

potential environmental impact (PEI) balance of pollutants, as an improvement upon

the original WAR algorithm. From the PEI balance calculation, a relative indication

of the environmental friendliness of the chemical process can be compared.

In the WAR algorithm, a potential environmental impact .

I , of a chemical k

in a non-product (NP) stream of j of a process is expressed as:

.

I NP = .

M j (3.1)

where

NPkjx k

.

M j is the mass flowrate of stream j, is the mass fraction of chemical k in

the non-product stream j and is defined as the overall potential environmental

impact of chemical k, which is developed using the following expression

NPkjx

k

k = l

slkl , (3.2)

where l is a relative weighting factor for impact category type l independent of

chemical k and is the specific potential environmental impact of chemical k for

an environmental impact type l which includes the following categories: global

warming, ozone depletion, acid rain, smog formation, human toxicity, aquatic

toxicity and terrestrial toxicity (see Table 3.2). The relative weighting factor

slk ,

l

allows the environmental impact of a chemical k, k, to be customized to specific or

local conditions. The recommended value is between 0 to 10 according to local

needs and policies. Throughout this thesis, l is set to 1. Chemical impact scores k

31

of several chemicals have been quantified using this expression. A database

software containing the environmental impacts of various chemicals has also been

developed by the USEPA (Martin and Young, 2008).

Based on the steady state balance, the environmental impact of any processes

can be written as follows:

.

I gen = .

I out - .

I in (3.3)

where .

I in is the input impact rate of stream entering the system, .

I out is the output

impact rate of stream leaving the system and .

I gen is the rate of impact generation by

the system. For balance involving only the non-product (NP) streams, the following

equation can be analogously written by adding the superscript non-product (NP) to

the above equation:

.

I NPgen =

.

I NPout -

.

I NPin (3.4)

Using the terminology described in equation (3.1) and the impact balance of

equation (3.4), the potential environmental impact .

I gen generated by the non-

product stream can thus be described as:

.

I NPgen =

j

outjM

.

k

kNPkjx -

j

injM

.

k

kNPkjx (3.5)

To account for the product stream of the process, an index

I NPgen , is introduced as

follows: NPgen =

p

p

NP

gen

P

I.

.

(3.6)

I

where

I NPgen is defined as a measure of the potential impact created by all non-

product streams in producing all of the products .

P produced by the system.

32

By setting the potential environmental impact k of all product streams to

zero, which means no environmental impact generated by the product streams and

setting all the non-product streams k to one, the following index can be deduced

from equation (3.5) and (3.6): NPgen =

pp

k

NPkj

j

inj

k

NPkj

j

outj

P

xMxM

.

..

(3.7)

M

where

M NPgen is a measure of the mass inefficiency of the process, i.e. it gives the

ratio of mass converted to an undesirable form to mass converted to a desirable

form.

One main drawback of the WAR algorithm arises due to the difficulty,

ambiguity and subjectivity involved in assigning the impact factor k of each

chemical in relation to the different environmental impacts potentially generated by

the process. Nevertheless, the algorithm provides a useful basis for comparing the

environmental friendliness of each process modification applied to the process.

3.3 Intelligent Simulation-Optimization Framework for Sustainable

Process Design

In practice, a waste minimization study is done by a team of experts in a two-

stage review (Allen and Rosselot, 1997). During the preliminary stage, the team

would assess the different parts of the process and derive several possible

alternatives to reduce the waste generated within the process. Since source reduction

gets a higher priority, it is explored first before recycling options are considered. All

the alternatives, which are generally available in qualitative form, are assessed in

more detail in the next stage of the study. At this stage, process simulators may be

33

used to evaluate the resultant changes in the product and waste streams.

Environmental and economic impacts would also be calculated for short-listing

promising alternatives. Finally, optimization would be performed to find the best

alternative(s) that minimize environmental impact and cost. This optimization is

typically performed through trial-and-error by adjusting the process variables to

obtain reduction in waste subject to economic boundaries. Any resulting trade-offs

between different alternatives would be highlighted and taken into consideration

when implementing the modifications in the plant.

A thorough waste minimization analysis is therefore knowledge-intensive,

laborious, time consuming, and expensive. Given the complex and multifaceted

nature of such analysis, a systematic method of identifying and evaluating suitable

design alternatives is thus essential. In this section, a framework that mimics the

two-stage waste minimization review is introduced for generating waste

minimization alternatives to chemical processes. The framework has been developed

by combining different process systems engineering (PSE) methodologies into one

coherent simulation-optimization framework for the sustainable process operations

problem.

Figure 3.2 shows the proposed framework for conducting sustainability study

which involves the following elements:

(1) process information representation,

(2) waste source diagnosis,

(3) knowledge-based alternative generation,

(4) quantitative assessment of alternatives, and

(5) multi-objective optimization.

34

A process graph-based scheme is used for diagnosing the waste sources in the

process. The hierarchical design approach of Douglas (1992) is used to derive

heuristic alternative design solutions that address the origin of waste. The efficacy of

each solution is measured based on simulation conjoined with economic and

environmental impact calculations. As conflicting effects between environmental

and economic factors are commonly encountered, multi-objective stochastic

optimization is then employed to identify solution design strategies that concurrently

satisfy the different objectives. Each of these is described in detail next.

35

USER INPUT

Material classification

Environmental impact data

(WAR)

Economic data Stream status

Process flowsheet

E

Figure 3.2 Proposed intelligent simulation-optimization framework

ENNVVOOPPEEXXPPEERRTT IF-THEN

heuristic rules

P-graph model

Functional model

Simulation engine Multi-objective

Simulated Annealing

QUANTITATIVE ANALYSIS

WASTE MINIMIZATION SOLUTIONS

Heuristic Solutions Pareto-optimal Solutions

705

710

715

720

725

730

735

78300 78800 79300 79800 80300

Operating Cost ($)

Envi

ronm

enta

l Im

pac

36

37

3.3.1 Process Information Representation

The first step in performing waste minimization in any process plant is to

extract appropriate knowledge about that process including the flowsheet, reaction

chemistry, and material and energy balances. If a simulation of the process already

exists, such information can be extracted easily using industrial standards such as

eXtensible Markup Language or XML. XML is a text-based markup language for

distributing documents over the web. Similar to HyperText Markup Language

(HTML), the XML syntax consists of a set of tags, i.e., identifiers enclosed in angle

brackets (Kokkonen et al., 2003). The starting-tag, ending-tag and parsed character

data in between the tags form an element of an XML document. Additional

information can also be embedded inside a tag as pairs of attributes and values. A

parent element can contain many child elements to form a hierarchical structure.

Various XML schema customized to chemical process modeling have been

reported including CapeML (von Wedel, 2002), Logical data model (Karhela, 2002),

and HYSYS-XML (Hyprotech, 2003). However, no universally agreed-upon XML

schema currently exists for modeling and simulation purposes. In this work, HYSYS

is used as the simulator and therefore use its XML schema for importing process

information. The XML document of a simulation contains all the building blocks of

the case study, includes the list of materials, list of unit operations, stream and unit

operation connections, reaction kinetics, thermodynamic model, and values of

operating variables or process parameters. An extract from the XML document for

the HDA case study is shown along with the HYSYS-XML schema in Figure 3.3.

<FSO OwnerType="FlowShtObject" OwnerName="Main" Type="ParamSet"> <FSOName OwnerType="FlowShtObject"

OwnerName="Main" Type="Param"> <Value>Toluene-feed</Value> </FSOName> <Icon OwnerType="FlowShtObject" OwnerName="Main" Type="Param"> <Value>MaterialStream</Value> </Icon> <X OwnerType="FlowShtObject" OwnerName="Main" Type="Param"> <Value>838.718143</Value> </X> <Y OwnerType="FlowShtObject" OwnerName="Main" Type="Param"> <Value>-371.314925</Value> </Y> </FSO>

Case definition

Case description

File name Software version Model type Date

Basis

Component list

Reaction package

Fluid package

Component name

Reaction name

Reaction type

Property coefficient

Thermodynamic model

Flowsheet objects Flowsheet

Stream or unit name

Variable and parameter

Material stream name Pressure Temperature Composition

Energy stream name Energy flow

Unit operation name Feed stream list Product stream list Energy stream list Design parameter

Flow diagrams

Stream or unit position

X and Y coordinates XML Data

XML Data

<ComponentName OwnerType= FluidPkgMgrObject" OwnerName="" Type="Param"> <Value>Hydrogen</Value> <Status>Specified</Status> </ComponentName> <ComponentName OwnerType="FluidPkgMgrObject" OwnerName="" Type="Param"> <Value>Methane</Value> <Status>Specified</Status> </ComponentName>

Figure 3.3 Hierarchical representation of process flowsheet in XML document 38

In addition to these, other data needed for the sustainability study are the

classification of material components into raw material, utility, waste or product (see

Table 3.1); status of each material component in output streams – desirable or

detrimental; environmental impact factors of the different materials (Table 3.2);

economic data (Table 3.3), and bounds on the decision variable (Table 3.4).

Table 3.1 Classification of material components in the HDA process

Status in Stream Component Purpose

Purge Fuel-gas Benzene-product

Diphenyl-waste

Toluene

raw-material

desirable

detrimental

detrimental

detrimental

Hydrogen

raw-material

desirable

detrimental

detrimental

detrimental

Benzene

product

desirable

detrimental

desirable

detrimental

Methane

product

desirable

desirable

detrimental

detrimental

Diphenyl waste-byproduct

desirable

detrimental

detrimental

desirable

Table 3.2 Environmental impact indexes (per mass basis) of chemicals in HDA process

Index \ Pollutant Hydrogen Methane Benzene Toluene Diphenyl

HTPI (Human Toxicity Potential by Ingestion)

0 0 0.12 0.078 0.12

HTPE (Human Toxicity Potential by Exposure)

0 0 0.092 2.20E-06 0.0016

ATP (Aquatic Toxicity Potential)

0 0.057 0.09 0.065 0.88

TTP (Terrestrial Toxicity Potential)

0 0 0.12 0.078 0.12

GWP (Global Warming Potential)

0 0.0035 0 0 0

ODP (Ozone Depletion Potential)

0 0 0 0 0

PCOP (Photo Chemical Oxidation Potential)

0 0.014 0.39 1.2 0

ARP (Acid Rain Potential)

0 0 0 0 0

Total impact 0 0.0745 0.812 1.421 1.1216

Source : Fu et al. (2000)

39

Table 3.3 Economic information for HDA process

Description Cost

Raw material ($/kmol) Hydrogen Toluene Waste treatment ($/kmol) Purge Diphenyl waste Energy ($/kWh) Furnace Compressor Pump Cooler Condensor (distillation) Reboiler (distillation) Reactor

2.50 14.00

0.7 1.8

0.05 0.07 0.07 0.10 0.004 0.02 0.02

Table 3.4 Initial values and bounds for decision variables in HDA process

Process variables Initial Minimum Maximum

H2 feed (kmol/h) Toluene feed (kmol/h) Energy of furnace (kJ/h) Energy of compressor (kJ/h) Energy of cooler (kJ/h) Energy of pump P-101 (kJ/h)

222.4 123.8

2.47×107

6.14×104

3.0×107

7172

220 120

2.47×107

6.1×104

3.0×107

7150

223 124

2.48×107

6.2×104

3.1×107

7200

3.3.2 Qualitative Waste Source Diagnosis

Once the required process information is available, the sources of wastes in

the process can be diagnosed. For this, the process graph (P-graph)-based approach

proposed by Halim and Srinivasan (2002a) is adopted. P-graph originates from the

work of Friedler et al. (1994) who demonstrated a special directed bipartite graph for

representing process structure suitable for the synthesis problem. In the P-graph

40

model, a material stream is represented by a circle, an operating unit by a bar and

connections between material streams and operating units by directed arcs. The flow

of material in the process is succinctly represented in the P-graph. Halim and

Srinivasan (2002a, 2002b) showed that the P-graph for a continuous process can be

derived automatically from the process flowsheet. They also reported methods for

identifying the sub-graph of streams and unit operations that contribute to the

presence of different material components in each waste stream. Figure 3.4 shows

the P-graph model for the HDA process. The flow paths of methane and benzene

(highlighted) have been traced from the diphenyl-waste and the benzene and fuel-

gas product streams, in the upstream direction until the input streams.

In general, five sources can be identified through P-graph analysis:

(1) useless material in inlet stream,

(2) excessive feed of useful material in inlet stream,

(3) useful material transformed at low conversion rate,

(4) useless material produced from reaction or phase change phenomena, and

(5) ineffective separation of useful material.

The term ineffective separation is used to describe a separation that causes an escape

of non-waste materials into a waste stream. Waste generating operations are

identified based on information of the status of the material. The presence of a

product material in the waste stream, for example, is detrimental. Likewise, a waste

material present in the product stream. An ineffective separation can be flagged if

there is a material that is detrimental in the waste stream, yet desirable in other

streams. The next step is to find the intersecting separation operation which leads to

the escape of such material into the former stream. In the example in Figure 3.4, the

41

42

stabilizer and benzene columns lead to the escape of desirable methane and benzene

into the waste streams can be identified as ineffective separators. Such analysis,

when performed on all components in the waste streams, reveals the following waste

sources in the HDA process:

(1) excessive raw materials in the H2 and toluene streams leading to their presence

in the purge and waste streams,

(2) diphenyl by-product and low conversion of toluene and hydrogen in the reactor,

(3) inefficient methane separation in stabilizer column (T-100),

(4) inefficient benzene separation in benzene column (T-101), and

(5) inefficient toluene separation in toluene column (T-102).

The subsequent step after diagnosing the waste sources is to derive decision

solutions to eliminate them.

RECYCLE GAS

Hydrogen Methane Toluene Benzene

H2 FEED Hydrogen Methane

TOLUENE FEED

FUEL-GAS

Methane (desirable)

DIPHENYL WASTE Methane Benzene

(detrimental)

BENZENE PRODUCT

Benzene (desirable)

Waste flow path

Intersecting unit

Intersecting unit

Figure 3.4 P-graph model of HDA process

43

3.3.3 Knowledge-Based Alternatives Generation

A set of design heuristics for minimizing waste generation and recovering

the valuable material from the waste stream has been identified (Halim and

Srinivasan, 2002b). These heuristics, which are based on Douglas (1992) and Smith

(1995), can be summarized below:

(1) If an impurity exists in a feed stream, then prevent it from entering the process.

(2) If a useful material in the feed stream exits through a waste stream, then prevent

excessive feed of that material.

(3) If a unit-operation generates waste, then eliminate or reduce that byproduct

formation.

(4) If an inefficient separation unit exists, then improve the separation process or

include another separation unit.

(5) If a useful material exists in a waste stream, then recover and recycle that

material.

By using these heuristics, various waste minimization solutions can be proposed.

For example, in the HDA process, the presence of toluene and hydrogen in the waste

streams can be eliminated or minimized through reducing their amounts in the feed

stream or material recycling. To eliminate diphenyl byproduct in the waste stream,

the solutions involve eliminating it during the reaction by changing the operating

conditions of the reactor. To prevent the escape of benzene product into the waste

stream, the operating conditions in the ineffective separator needs to be modified.

Table 3.5 shows the list of solutions derived for this HDA process.

44

Table 3.5 Qualitative waste minimization alternatives for HDA process

Stream/Unit Name

Waste minimization solution

Toluene-feed Prevent excessive feed of toluene component in the toluene stream Use alternative material in the toluene stream

H2-feed Prevent excessive feed of hydrogen component in the hydrogen stream Use alternative material in the hydrogen stream

Purge Direct recycle or recovery-recycle of hydrogen, toluene, methane, and benzene component

Use further separation system to recover the useful hydrogen, methane and benzene

Diphenyl waste

Direct recycle or recovery-recycle of hydrogen, toluene, methane, and benzene component

Use further separation system to recover the useful hydrogen, methane, toluene and benzene

Reactor (PFR-100)

Increase conversion of toluene and hydrogen

Use alternative reaction route to eliminate diphenyl production

Optimize the operating condition in reactor to eliminate diphenyl production

Stabilizer column

(T-100)

Use further separation system to recover the useful methane

Optimize the operating condition in Stabilizer column (T-100) to avoid the escape of methane

Benzene column

(T-101)

Use further separation system to recover the useful benzene

Optimize the operating condition in Benzene column (T-101) to avoid the escape of benzene

Toluene column (T-102)

Use further separation system to recover the useful toluene

Optimize the operating condition in Toluene column (T-102) to avoid the escape of hydrogen, methane, toluene and benzene

The heuristics for diagnosing the source of waste using P-graph analysis and

generating the qualitative process alternatives to eliminate or minimize the sources

of waste has been implemented as a knowledge-based system, called ENVOPExpert,

using Gensym’s G2 expert system shell (Halim and Srinivasan, 2002a; Halim and

Srinivasan, 2002b; Halim and Srinivasan, 2002c). It has been applied on several case

45

studies including a hydrocarbon separation and a chemical intermediate

manufacturing process and found to successfully emulate the decision making

process of human experts.

The heuristics provide potential solutions whose benefits to the plant can be

justified only when significant reduction in the waste amount can be demonstrated.

The analysis based on the P-graph model only suggests “how” a particular waste

source can be solved. It is not capable of specifically determining the variables that

should be manipulated and the extent of the resulting “benefits” in quantitative

terms. For example, consider the alternative “optimize the reactor condition to

eliminate diphenyl production”; there are many ways of improving the reaction,

such as changing the temperature, pressure and flow of the reactants. These

alternatives cannot be evaluated by using the P-graph model. For example, consider

the alternative “optimize the reactor condition to eliminate diphenyl production”;

there are many ways of improving the reaction, such as changing the temperature,

pressure and flow of the reactants. These alternatives cannot be evaluated by using

the P-graph model. Thus, the next step in the sustainable design framework is to

perform quantitative analysis using the simulator.

3.4 Quantitative Analysis

The connection between the qualitative results of ENVOPExpert and the

quantitative assessment of the alternatives using the process simulator is through a

list of decision variables which have to be optimized to enhance sustainability. This

procedure is described as follows.

46

3.4.1 Intelligent Identification of Decision Variables

Based on the qualitative solutions, a subset of variables to be manipulated

and optimized can be identified. This short-listing of variables is enabled by

functional models that capture the functional interactions between the constituent

elements of the process (Modarres, 1993). In this case, each of the unit operations in

the plant is classified according to its function. Following the terminology of Lind

(1994), each unit operation is considered to serve one or more of the following basic

flow functions:

(1) Source ─ a supply of mass and energy to the process, e.g., feed stream, reactor,

heater, and cooler

(2) Sink ─ an end point for mass and energy flow, e.g., product stream or waste

stream

(3) Storage ─ accumulator as well as distributor of mass and energy, e.g.,

separation tank

(4) Balance ─ acts as a balance between the incoming and outgoing flow of mass

and energy, e.g., stream mixer and splitter

(5) Barrier ─ prevents transfer of mass and energy between two flow functions, e.g.,

valve

(6) Transport ─ transfers mass and energy between the two flow functions, e.g.,

pump and compressor

Main and supporting variables that influence that function are also defined for each.

The term “main variable” is used to describe those variables that directly affect the

flow function of a unit. For example, temperature is the main variable for coolers or

heat exchangers and pressure for pumps or compressors. On the other hand,

47

48

“supporting variables” directly or indirectly influence the main variable. These

variables may be within the same unit or be associated (main or supporting

variables) with upstream units. Consider a cooler as an example. The cooling energy

would be the supporting variable as it directly impacts the main variable cooler

temperature. The energy of a compressor that is connected upstream of the cooler

would also be a supporting variable as this energy influences the compressor

temperature, which in turn affects the cooler temperature. Thus, given a process

flowsheet, the entire chain of functional interactions between the units can be

identified.

Figure 3.5 shows the functional model for the HDA process from the

perspective of main variables of each process unit. This reveals that the hydrogen

and toluene feed, heating-energy of furnace, and the energy of compressor, cooler

and pump (P101) are relevant variables (degrees of freedom) that control the flow

and concentration of diphenyl waste stream. These six variables thus become the

decision variables for optimization. Thus functional interactions enable us to

automatically shortlist the set of variables that are relevant to a design alternative

and link the qualitative analysis of ENVOPExpert with the simulation-optimization

procedure described next.

K-100 Pressure

49

TRANSPORT

Figure 3.5 Functional model of HDA process

SOURCE

SOURCE

SOURCE SOURCE

SOURCE

SOURCE

SINK

TRANSPORT

TRANSPORT STORAGE

STORAGESTORAGE

STORAGE BALANCE

BALANCE

BALANCE BALANCE

MIX-102

E-102 Temperature

Furnace Temperature

PFR-100 Temperature Concentration

T-102 Concentration Pressure Temperature

V-100 Concentration Pressure Temperature

T-100 Concentration Pressure Temperature

T-101 Concentration Pressure Temperature

Toluene-feed Concentration

H2-feed Concentration

Diphenyl-waste

TEE-101

TEE-100

E-100 Temperature

P-101 Pressure

P-100 Pressure

MIX-101

MIX-100

BALANCE

3.4.2 Quantitative Assessment of Alternatives

Numerous indicators have been developed to set the performance targets and

measure the effectiveness of implementing sustainable alternatives. Azapagic and

Perdan (2000) proposed categorizing sustainability indicators into different metrics

for environmental, economic, and social performance. The environmental indicators

include impacts such as global warming, ozone depletion, and ecotoxicity as well as

efficiencies (such as material recyclability, product durability), and measures of

compliance to environmental management system. These are calculated following

the products life-cycle. The UK IChemE produced a more complete set of indicators

to measure the three components of sustainable development (Tallis et al., 2002).

Schwartz et al. (2002) under the sponsorship of AIChE’s Centre for Waste

Reduction Technology introduced five basic indicators to measure the

environmental sustainability of a process: material intensity, energy intensity, water

consumption, toxic emissions and pollutant emissions. The term material intensity is

used as a measure of waste material per unit product and this is calculated by

subtracting the product outputs from the raw material inputs (excluding utilities). In

the same way, energy intensity is a measure of fuel consumption per unit of

production. These indicators are used to benchmark different production alternatives

and guide improvements in the operations. As it is not possible to set up a

standardized sustainability indicator that is applicable to every process, Krajnc and

Glavic (2003) used indicators from different sources (such as IChemE and AIChE)

and combined them with additional metrics that they proposed.

In this work, environmental and economic indicators are used as the

sustainability measure as they are the most practical to the operation of process

plants. For the former, pollutants are used as the measure of environmental impact

50

although the other metrics can be additionally used as well. In this case, the WAste

Reduction (WAR) algorithm, as described in section 3.2, has been implemented as

the environmental impact indicator due to its wide database of chemical compounds,

although other impact indicators such as those by Heijungs et al.(1992), Wright et

al.(1997), or Koller et al. (2000) can also be used. The economic objective is

calculated based on the costs of raw material, waste treatment, and energy

consumption.

In WAR, each process material is assigned an index value to indicate its

potential impacts to different environmental categories. Based on this index, the total

impact of a waste stream in the plant can be calculated as the sum of each material

index of the waste stream weighted by its flowrate. The total environmental impact

of a process is thus calculated as the sum of the environmental impact of all the

waste streams in the process. The flowrates for the economic impact calculation and

the costs are calculated using the process simulator.

Like other design problems, the environmental impacts and economic

aspects of the proposed alternatives may conflict one another. For example,

increasing the reactant flowrate to reduce the byproducts formation and thus the

environmental impact would lead to higher operating costs. Thus the next step

would be to perform multi-objective optimization to identify and resolve the trade-

offs.

3.4.3 Multi-Objective Optimization using Simulated Annealing

Although multi-objective simulated annealing algorithm is used for

optimization method in this work, other techniques such as genetic algorithm

51

(Srinivas and Deb, 1994) or tabu search (Cavin et al., 2004) can be used as well. The

basic principle of simulated annealing is derived from the statistical mechanics of

annealing of metals. In contrast to greedy search which moves in the direction of

improving objective function values and is hence susceptible to local optima,

simulated annealing seeks to reach the global minimum energy state by temporarily

accepting even poorer solutions, with a probability P (Kirkpatrick et al., 1983). In

other words, given a prior solution x and a new solution x for the optimization

problem min ( )f x , xwould be accepted with a probability P:

( ) ( )

1f x f x

T

if f x f xP

e Otherwise

(3.8)

where T is the annealing temperature. The classical simulated annealing algorithm

was designed for single objective optimization. However, it has recently been

extended to multi-objective optimization problems, where it is used to identify the

Pareto set. A solution to a multi-objective problem is deemed as Pareto-optimal or

non-dominated, if no other solution is found to dominate it (Smith et al., 2004). As

an illustration, consider the optimization problem involving minimization of

environmental impact (fenv) and operating cost (feco) :

Min )(),( xfxf ecoenv (3.9)

where x is the vector of decision variables, and fenv and feco are the two objective

functions. For two solutions x and x , x is said to dominate x if fenv( x ) is no

worse than fenv( x ) and feco( x ) no worse than feco( x ). x would be Pareto-optimal

solutions if x is better than x in exactly least one of the two objective functions.

The set of all Pareto-optimal solutions is termed as the Pareto set, which represents

the possibly optimal trade-offs between different objectives.

52

Suppapitnarm et al. (2000) proposed an extension of the simulated annealing

to the multi-objective case using the concept of archiving solutions. As applied to

the sustainable design case, it consists of the following steps:

(1) Specify the elements of the solution vector x , the various decision variables that

control the sustainability profile of the process. Specify the minimum and

maximum values for each. Use the values from the base-case design as the initial

solution vector x .

(2) Initialize the Pareto set with the triplet )(),(, xfxfx ecoenv as its element.

(3) Using the candidate solution vector, simulate the process to obtain the

environmental and economic objective function values.

(4) Specify an initial annealing temperature T .

(5) Perform random perturbation to generate a new solution vector x in the

neighborhood of x and evaluate its objective function values – fenv( x ) and

feco( x ) using the simulator.

(6) Compare objective values of xwith all solutions in the Pareto set. If

xdominates any element of the Pareto set, replace that element with x . If x is

Pareto-optimal with all the elements in the set, then include it in the set.

(7) If x is dominated, temporarily accept it as the current solution vector with a

probability P defined as

min 1,exp .expenv ecoS SP

T T

(3.10)

53

where

env env env

eco eco eco

S f x f x

S f x f x

(3.11)

(8) x is accepted as the new solution, i.e., x x iff P > Prand a random number

in [0 1]; otherwise the earlier solution vector is retained.

(9) To escape from local optima, periodically, x is replaced with a randomly

selected solution from the Pareto set.

(10) For convergence, periodically the annealing temperature T is reduced using a

problem dependent temperature reduction factor tR :

tT T R (3.12)

(11) Steps (3) to (10) are repeated for a predefined total number of iterations NTotal.

The above multi-objective simulated annealing algorithm as implemented by Suman

(2004) has been used to serve as the optimizer in the proposed framework.

Both the steps described above have been automated so that the various tasks

for sustainable design are transparent to the user. The automated procedure starts

with ENVOPExpert extracting the information about the process from the HYSYS

model. This is done by converting the flowsheet information into an XML file,

parsing it into ENVOPExpert and creating corresponding objects, connections, and

relations in G2. Once all the relevant information is available, P-graph diagnosis is

performed by ENVOPExpert and qualitative waste solutions derived. HYSYS is

also used for the quantitative assessment of the process. A bridge between G2 and

HYSYS has been developed using G2-ActiveXLink and the HYSYS-Browser

components. The former enables communication between G2 and the COM

(Component Object Model)-compliant applications such as Microsoft Excel. The

54

latter allows connection between HYSYS and Excel through VisualBasic for

Applications (VBA). This two-way connection is used to send the values of decision

variables to HYSYS, which simulates their effects and returns the results back to

G2. Environmental and economic evaluation is performed in ENVOPExpert using

these results. The multi-objective simulated annealing algorithm has also been

embedded in ENVOPExpert, so it plots the next random move of the decision

variables and sends them again to HYSYS for simulation. This cycle is repeated for

a number of iterations to obtain the complete Pareto solution set.

Table 3.6 Multi-objective optimization results for HDA process

Pareto Optimal Solutions Description

Base case

1 2 3 4

Decision variables H2 feed (kmol/h) 222.44 221.42 220.58 221.26 221.44

Toluene feed (kmol/h)

123.83 120.99 121.39 121.01 120.82

E-Furnace (×107 kJ/h)

2.47 2.47 2.48 2.48 2.48

E-Compressor (×104 kJ/h)

6.142 6.163 6.164 6.163 6.163

E-Cooler (×107 kJ/h)

3.000 3.08 3.06 3.05 3.07

E-Pump101 (kJ/h)

7172

7168 7177 7174 7174

Objectives

Operating cost ($/h)

1504 1495 1486 1489 1493

Total impact 1476 1197 1218 1214 1205

Table 3.6 shows the complete Pareto solution set for the HDA case study.

Among the optimal solutions, the environmental impact ranges from 1197 to 1218

and the operating cost from 1486 to 1495. Compared to the base case, this signifies a

reduction of up to 19% for environmental impact and 1% for costs. The same case

55

study has been previously used by Fu et al. (2000) for optimization using four

decision variables: hydrogen and toluene flowrate, furnace temperature and reaction

conversion. In their approach, the optimization problem was solved as single

objective optimization with respect to economic and four environmental categories –

HTPI / TTP, HTPE, GWP and PCOP. (In this work, HTPI and TTP have been

classified as one environmental category as both share the same material indexes.

ODP and ARP have also been eliminated from assessment due to their zero

indexes.) Table 3.7 presents the optimization results for the five different objective

functions. The table shows that the environmental impact of this process is

dominated by PCOP followed by ATP; in fact the two contribute to 93% of the total

environmental impact. The table also shows a reduction in both environmental

impact and operating cost compared to the base case. However, it also reveals a

trade-off between cost and environmental impact (See “Min cost” and “Min

PCOP”). Compared to their approach, where the analysis and formulation had to be

done manually, the proposed approach is completely automated. In addition, Table

3.7 also reveals that the multi-objective optimization yields better results than

solving any single objective optimization. All the single-objective results are

dominated by the multi-objective solutions, as shown in Figure 3.6. This further

highlights the advantage of the proposed methodology.

56

Table 3.7 Comparison of HDA design alternatives

Description

Base case

1 Min Cost

2 Min HTPI,

TTP

3 Min

HTPE

4 Min

GWP

5 Min

PCOP Decision variables H2 feed (kmol/h) 222.44 221.125 220.718 220.912 221.751 221.22

Toluene feed (kmol/h)

123.83 121.274 121.365 121.743 120.936 121.279

E-Furnace (×107 kJ/h)

2.47 2.476 2.476 2.477 2.474 2.474

E-Compressor (×104 kJ/h)

6.142 6.152 6.263 6.165 6.160 6.151

E-Cooler (×107 kJ/h)

3.000 3.028 3.063 3.075 3.063 3.068

E-Pump101 (kJ/h)

7172

7178

7174

7179

7175

7173

Objectives

Operating cost ($/h)

1504 1486 1489 1493 1496 1494

HTPI 89 77 75 75 75 75 TTP 89 77 75 75 75 75

HTPE 2 1 1 1 1 1

ATP 369 369 371 371 365 368 GWP 7 6 6 7 6 6

PCOP 920 723 691 700 706 703 Total impact 1476 1253 1219 1229 1228 1228

1190

1200

1210

1220

1230

1240

1250

1260

1484 1486 1488 1490 1492 1494 1496 1498

Operating cost ($/hour)

Envi

ronm

enta

l im

pac

t

Pareto plot of multi-objective solution

Figure 3.6 Single- vs. multi-objective optimization results

57

3.5 Case Study: Biodiesel Production

In this section, the application of ENVOPExpert to a biodiesel production

plant is illustrated. Different routes for biodiesel production have been developed

including alkali-catalyzed transesterification and acid-catalyzed process using virgin

or waste oil (Zhang et al., 2003a). The process considered here is based on acid-

catalyzed processing of waste canola oil. It consists of a transesterification reaction

followed by a series of separation steps involving waste oil (represented by triolein),

methanol, sodium hydroxide, water, glycerol and biodiesel (methyl oleate). Figure

3.7 shows the flowsheet of the process (Zhang et al., 2003a), which involves the

following operations:

(1) Transesterification: Initially, a stream of fresh methanol and sulfuric acid is

mixed with the recycled stream for reaction with waste oil (triolein). The

reaction, which is carried out at 80°C and 400 kPa pressure, converts 97% (mass

basis) of oil into biodiesel (methyl oleate) and glycerol byproduct:

Triolein + 3 Methanol 3 Methyl Oleate + Glycerol

(2) Methanol recovery: The reaction product is cooled prior to separation in a

vacuum tower to recover the excess methanol. In this process, 94% of methanol

is recovered and recycled back to the reactor. The bottom stream is again cooled

and sent to the acid removal unit.

(3) Acid removal: In this step, sulfuric acid is neutralized by adding calcium oxide

(CaO) to produce CaSO4 and water. This is followed by settling to remove

CaSO4. The reaction is assumed to fully remove the acid component.

58

59

(4) Water washing: The purpose of this operation is to separate the biodiesel from

the glycerol, methanol and acid catalyst. This is done through washing with

water in an extraction column.

(5) Biodiesel purification: In the final stage, the biodiesel product is distilled further

under vacuum to obtain the required purity of 99.6% (mass basis).

The process currently generates four waste streams: bottom liquid of the acid

neutralization column, washwater waste of the extractor column, vent gas of the

purifier column, and reboiler liquid. As the vent flowrate is negligible compared to

the other waste streams, it is thus ignored during analysis. Table 3.8 shows the cost

and environmental data for this process. The process generates a total environmental

impact of 126.9 as calculated from the three waste streams. The economic objective

is represented by the operating cost – including costs of raw material, waste

treatment and energy – and is $728.4/hour for the base case. Thus, with a throughput

of 3 kmol/hour and a biodiesel selling price of $170/kmol (Zhang et al., 2003b), a

loss of $218.4 per hour of operation is incurred. The process can be made more

efficient to lower the loss through better utilization of raw material and energy; this

is indeed the goal of the current plant waste minimization study.

Recycle

Recycled-methanol

60

Vent-gas

Figure 3.7 Flowsheet of biodiesel production process

Reactor-feed

Waste-vapor

Washed-mixture

Waste-oil

Water

Settler Neutralized-mixture

Neutralizer

Vent-gas2

Biodiesel-bottom

Methanol-pump

E-pump2

Methanol-top Methanol-column

E-cooler1

Reactor-product

Reactor E-reactor

Oil

Recycled-feed

Pumped-feed

E-pump1 Feed-pump

Feed-mixer

Fresh-feed

H2SO4

Methanol

Top-mixture

Reactor-mixer

Product-cooler Cooled-product

E-condenser1

E-reboiler1

Bottom-cooler

E-cooler2

Cooled-mixture

CaO

E-neutralizer

E-condenser2

Biodiesel-product

E-settler CuSO4-waste

Extractor

Bottom-Waste

Waste-mixer

Waste-water

Purifier E-reboiler2

Table 3.8 Cost and environmental data for biodiesel process

Description Value Description Value

Cost of raw material ($/kmol) Methanol H2SO4

Triolein Water CaO Price of product ($/kmol) Biodiesel Cost of waste stream ($/kmol)Waste vapor Waste water Cost of energy ($/kWh) Pump Cooler Reactor Condensor (distillation) Reboiler1 Reboiler2 Neutralizer

5.77 5.88

177.09 0.18 2.24

170.08

10.46 7.23

0.062 0.003 0.003 0.003 0.31 0.45

0.062

Environmental impact (per mol basis)* Methanol Triolein Methyl-oleate Glycerol H2SO4 Water CaO CaSO4

11.95 67.29 20.46

7 62.77

0 43.24 42.2

*Source: Martin and Young (2008)

Using the P-graph based approach, the following waste sources were first

diagnosed by ENVOPExpert:

(1) Waste byproducts (glycerol and CuSO4) which are formed in the reactor and the

neutralizer.

(2) Inefficiencies in the extraction and purification columns that lead to the escape

of biodiesel into the waste water stream.

(3) Excessive feed of methanol, H2SO4 and oil cause the presence of those

components in the waste water and the waste vapor stream. However, as the

waste vapor flowrate is small, it can be ignored.

(4) Excessive use of CaO and water as the neutralizing and extraction agent lead to

waste water stream. However, as CaO is assumed to be fully converted, its

61

presence in the waste water stream is assumed negligible.

Subsequently, the design modifications listed in Table 3.9 were proposed. Six

variables – feed streams of waste oil, methanol, H2SO4, the energy of the two

coolers, and water flowrate – are selected as the decision variables and their values

varied in the ranges shown in Table 3.10. Such ranges of the variables have been

decided to ensure that flowsheet convergence can be attained during the

optimization run. This is particularly important as changes, even small ones, in these

variables could lead to failed convergence, especially in the column operation. For a

process without column operation, a wider range of variables can always be used.

Like in the HDA case, the objective in this process is to minimize both the

environmental impact and the operating cost.

Figure 3.8 shows the Pareto optimal set. Compared to the base design, the

maximum reduction in the environmental impact and operating cost is found to be

4% and 1.5%, respectively. This finding thus confirms the benefits of implementing

waste minimization to the plant. Figure 3.8 also shows the trade-off between the

objectives. Overall, about 80% of the environmental impact is due to the presence of

un-recovered methanol and H2SO4 in the waste stream; about half of the operating

cost comes from energy consumption. Therefore, decreasing the amount of methanol

and CaSO4 in the waste by reducing the feed amounts leads to lower raw material

and waste treatment cost, but the energy needed increases since the efficiency of

methanol- H2SO4 separation has to increase. It should also be noted that, in this case

study, glycerol has been considered as a waste byproduct and its contribution to the

overall environmental impact is about 6%. Thus from the decision-maker’s

standpoint, it would be desirable to find a beneficial use for glycerol, so that it can

become a product.

62

Table 3.9 Waste minimization alternatives for biodiesel process

Source Stream/Unit name

Waste minimization alternative

Methanol H2SO4

Feed stream

Oil

Prevent excessive feed Substitute with other material

CaO Utility stream Water

Prevent excessive feed Substitute with other material

Reactor product Reactor Neutralized

mixture

Increase the reactant conversion Use alternative reaction route to

eliminate byproduct Optimize reaction condition to

minimize byproduct Bottom waste Separator

Waste oil Add another separation unit to

recover useful material Optimize separation condition to

recover useful material Waste water Waste

stream Waste vapor Direct recycle or recovery recycle

of useful material

Table 3.10 Initial values and bounds for decision variables in biodiesel process

Decision variables

Base case Minimum Maximum

Oil (kmol/h) Methanol (kmol/h) H2SO4 (kmol/h) Water (kmol/h) Energy of Cooler 1 (kJ/h) Energy of Cooler 2 (kJ/h)

1.15 6.66 1.54 5.92

65600 17500

1.1 6.5 1.4 5.5

64000 17000

1.2 6.8 1.7 6.5

67000 19000

63

64

121

122

123124

125

126

127

128129

130

131

716 718 720 722 724 726 728 730

Operating cost ($/hour)

Base case (728.4, 126.9)

En

vir

on

me

nta

l Im

pa

ct

Figure 3.8 Optimization results for biodiesel production

To sum up this chapter, an advanced version of ENVOPExpert system has

been proposed for synthesizing waste minimization analysis generic to any chemical

processes operating on continuous mode. It consists of three elements – a knowledge

base for identifying the root cause of waste generation in a process, a heuristic

design method for generating alternative designs and a simulation-optimization

algorithm for manipulating the decision variables optimally to improve the

environmental and economic objectives. In the next chapter, extension of

ENVOPExpert framework to batch operations is described. Unlike continuous

process, batch operation delivers its product in discrete amount. Consequently any

process wastes generated from batch operation would vary with time. To

accommodate this type of operation, the operating procedure has to be analyzed to

both diagnose the source of waste generation as well as identify process

modifications that will eliminate it.