Sustainability assessment framework for chemical production pathway: Uncertainty analysis

G Model JECE 1093 No. of Pages 12

Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

Journal of Environmental Chemical Engineering journal homepage: www.elsevier.com/locate/jece

Sustainability assessment framework for chemical production pathway: Uncertainty analysis Weng Hui Liewa , Mimi H. Hassima,* , Denny K.S. Ngb a

Department of Chemical Engineering/Institute of Hydrogen Economy, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia Department of Chemical and Environmental Engineering/Centre of Sustainable Palm Oil Research (CESPOR), The University of Nottingham, Malaysia Campus, Broga Road, 43500 Semenyih, Selangor, Malaysia b

A R T I C L E I N F O

Article history: Received 13 December 2015 Received in revised form 30 March 2016 Accepted 3 May 2016 Available online xxx Keywords: Assessment framework Process design Sustainability Inherent safety, health and environment (SHE) Economic performance (EP) Uncertainty

A B S T R A C T

The sustainability level of a chemical production pathway is an important element that requires to be assessed when developing a new process. Note that the typical sustainability assessment is normally emphasised on economic and technological development. In order to ensure more comprehensive level of sustainability, the protection on human health and preservation of the environment should be considered. This paper presents a systematic framework for assessment of chemical production pathway based on multi-sustainability criteria, i.e., inherent safety, health and environment (SHE) and economic performance (EP). In order to generate an optimal design solution, uncertainty analysis is also incorporated in this framework. Two optimisation approaches are adapted into this framework, i.e. fuzzy optimisation is used for multi-objective analysis, while multi-period optimisation is applied to address the multiple operational periods with presence of uncertainty. To illustrate the proposed framework, assessment on biodiesel production pathway based on enzymatic transesterification using waste oil is conducted. In the case study, three periods (low, medium and high demand period) of demand for biodiesel are considered, whereby each period is subjected to uncertainties, i.e. waste oil flow rate, waste oil price and enzyme price. To accommodate the uncertainties, sensitivity analysis is performed to determine the feasible operating condition, i.e. tert-butanol concentration and reactor residence time, as well as the appropriate sizing of the process modules (or known as unit operations). ã 2016 Elsevier Ltd. All rights reserved.

1. Introduction Production pathway design appears as an important task during the chemical process design to achieve sustainable development [16]. Typically, chemical process design is performed in stages, which starts from the research and development (R&D), followed by preliminary engineering and basic engineering stage [11]. During these early design stages, the pathway design can be enhanced through various assessments [19]. In assessment, it is critical to consider the criteria which contribute to sustainability of the production pathway [35]. In terms of sustainability, it is essential to prioritise on the protection of human and conservation of the environment [20]. This is supported by a significant number

Abbreviations: EP, economic performance; HQI, health quotient index; IBI, inherent benign-ness indicator; IE, inherent environment; IH, inherent health; IS, inherent safety; R&D, research and development; SHE, safety, health and environment; WAR, waste reduction algorithm. * Corresponding author. E-mail address: [email protected] (M.H. Hassim).

of undesired events involving chemical plant industries that had took place in the past, e.g. Bhopal disaster, 1974, Piper Alpha disaster, 1988, Texas city refinery explosion, 2005, etc. Those accidents have caused great losses in terms of disastrous impacts on human life and the environment. In relation to this, the demand from the public and voluntary initiatives to improve the safety, health and environmental (SHE) performance in chemical production has gradually increased [12]. For the improvement of SHE, it is important to adopt the inherent safety (IS) principle during the early process design stages. The IS principle basically emphasises on the reduction or elimination of hazard by intrinsic mean, without the application of external add-on system or procedures [15]. In addition, it is highly recommended to perform the IS assessment during early process design stages due to the benefits of having higher degree of freedom for performing engineering modification with much lower cost [13]. Apart from considering the IS aspect, Kletz [15] also suggested to apply the IS principle to health and environmental aspects to promote more comprehensive protection on human and conservation of the environment. Therefore, the objective of

http://dx.doi.org/10.1016/j.jece.2016.05.003 2213-3437/ã 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

2

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

Nomenclatures

COSTTPDC

Total plant direct cost

COSTTPEC

Total purchase cost for all process modules

TPEC
PM

COSTTPIC

Total capital investment based on process modules Total plant indirect cost

COSTUtil

Total utility cost

CONSUtil
CW l

COSTWC

Consumption of cooling water for process module l Consumption of low pressure steam for process module l Consumption of medium pressure steam for process module l Consumption of electricity for process module l Working capital cost

COST Ves lpv

Cost of pressure vessel lpv

COST Dist
PL ldc

DDist ldc

Cost of platform and ladder for distillation column ldc Cost of platform and ladder for pressure vessel lpv Diameter for distillation column ldc

DVes lpr

Internal shell diameter for reactor lpr

DVes lpv

Internal shell diameter for pressure vessel lpv

DVes n;lpvt Eco E’ F’ Dist
D F ldc

Diameter of storage tank vessel lpvt for product n Scoring index for economic performance Scoring index for explosiveness Scoring index for flammability Distillate flow rate for distillation column ldc

F Feed m

Flow rate of raw material m

F nNon
Prod
O 0

Flow rate of non-product outlet stream n’

F Prod n

Flow rate of product stream n

F Re
In j;lpr

Flow rate of inlet process stream j to reactor lpr Standard fugitive emission rate based on process stream j connected to process module l Total fugitive emission for chemical k

COST Sets j k l ldc lhe lpv lpvd lpvr lpvt n’

Process stream Chemical Process module Distillation column Heat exchanger Pressure vessel Decanter vessel Reactor Storage tank Non-product outlet stream

Parameters cEL k COEFDist COSTUnit
CW l

Exposure limit for chemical k in air Coefficient for tray spacing and liquid surface tension Unit cost of cooling water for process module l

COSTUnit
Elec l

Unit cost of electricity for process module l

Unit cost of raw material m COSTUnit
Feed m COSTUnit
LPS l

CONSUtil
LPS l CONSUtil
MPS l CONSUtil
Elec l

Ves
PL COST lpv

COSTUnit
Prod n ftPres lhe

Unit cost of low pressure steam for process module l Unit cost of medium pressure steam for process module l Unit price of product n Pressure factor for heat exchanger lhe

ftMat lhe

Material factor for heat exchanger lhe

ftLen lhe HY I0 I1 IncTax RRDist ldc

Tube-length correction factor for heat exchanger lhe Annual operating hour Base CE index Latest CE index Income tax rate Reflux ratio for distillation column ldc

SPDist ldc

Spacing of plate for distillation column ldc

Dist
Shell thldc

Thickness of shell for distillation column ldc

FETotal k

Ves
Shell thlpv

Shell thickness for pressure vessel

GDist ldc

UHEX lhe

Overall heat transfer coefficient for heat exchanger lhe Upper fuzzy limit for period p Lower fuzzy limit for period p Occurrence probability for period p Standard potential environmental impact value for chemical k

HQITotal

Total health quotient index value for all chemicals

Variables AHEX lhe

Heat transfer surface for heat exchanger lhe

HQIk I’

Health quotient index for chemical k Scoring index for chemical inventory

COSTUnit
MPS l

XU p XLp

ap ck

FEPM j;l

HDist ldc

Allowable vapour velocity for distillation column ldc Height of distillation column ldc

HVes lpr

Height for reactor lpr

HVes lpv

Shell tangent-to-tangent height for pressure vessel lpv Height of storage tank vessel lpvt for product n

HVes n;lpvt

ck

Concentration of chemical k in air

IE

Scoring index for inherent environment

cTB lpr

tert-butanol concentration in reactor lpr

IH

Scoring index for inherent health

COSTCFC

Contractor’s fee and contigency

IS

Scoring index for inherent safety

COSTDFC

Total depreciable capital

COSTFeed

Total cost of feedstock

COST PM ldc

Total cost of distillation column ldc

PEINon
Prod
O Potential environmental impact resulted by all non-product outlet streams P’ Scoring index for pressure

COST PM lhe COST PM lpv Prod

Total purchase cost for heat exchanger lhe

Q HEX lhe

Heat transfer rate for heat exchanger lhe

Total cost of pressure vessel lpv

R’

Scoring index for reactivity

REV

Annual sales revenue

ROI

Return on investment

qDist ldc

Number of plates in distillation column ldc

COST

TCI

COST

TPC

COST

Annual production cost Total capital investment Total plant cost

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

T’

Scoring index for temperature

tRe lpr

Residence time for reactor lpr

tSt n

Required storage duration for product n

VAir

Volumetric flow rate of air

W Dist ldc W Ves lpv

Weight of distillation column ldc

xj,l,k

Composition of chemical k for process stream jl connected to process module l

xn’,k

Composition of chemical k in non-product outlet stream n’

Y’

Scoring index for process yield

DHR’ DT HEX m;lhe

Scoring index for heat of reaction

Weight for pressure vessel lpv

Mean temperature driving force for heat exchanger lhe

rDist ldc rLldc rVldc rVes lpv

Material density for distillation column ldc

Xp

Objective index for period p

lX,p

Sustainability indicator for objective X and period p Sustainability indicator (mutual performance of all objectives) for period p

lp

Density of liquid in distillation column ldc Density of vapour in distillation column ldc Material density for pressure vessel lpv

attaining inherently SHE-er process should be emphasised in chemical production pathway development. Apart from inherent SHE, assessment of the economic performance (EP) is also important to provide the indication of the economic viability of the process. Hence, EP would appear as key justification to the project owners or stakeholders for business investment. The assessment of economic performance in chemical industries has been considered in various works as presented in literature [28] and nonetheless, EP should also be included with other multicriteria, e.g. SHE [14]. In addition, all the assessment criteria should be assessed in simultaneous way so that to ensure comprehensiveness [14]. In this case, multi-objective optimisation should be adopted in the assessment. Besides the challenge of conducting multi-criteria assessment in chemical production pathway, the production profile of chemical operation could also be dynamic due to fluctuating product demand or raw material supply [7]. Hence, a specific study for each of the operational period in multi-period optimisation is greatly suggested [7]. In conjunction with the multiperiod optimisation, uncertainty may exist in the form of product demand, environmental condition, economic cost, etc. The relevant uncertainty should be incorporated into the assessment in order to generate a more optimal and practical solution. In view of the aforementioned scopes of assessment, this work aims to present a systematic framework for the assessment of chemical production pathway based on the multi-objective of inherent SHE, and EP. In the framework, fuzzy optimisation is adopted for multi-objective analysis. Besides, in order to generate an optimal solution, multi-period optimisation approach is integrated into the framework to consider for every operational period with the presence of uncertainty. Next, the methodology for the uncertainty analysis to determine the feasible operating condition for each considered period, and the most optimum sizing of the process modules (or known as unit operations), is presented. Since this framework is more specified to the operating condition and the general sizing of the process modules, it is therefore, applicable to the early design stages, rather than latter design phase called detailed engineering stage.

3

To illustrate the proposed framework, the assessment on biodiesel production pathway is performed. Biodiesel is recognised as an important renewable energy source which would potentially reduce the total dependency on fossil fuel. Considering the mentioned fact, it is deemed necessary to assess the sustainability aspect of biodiesel production, prior to continuous expansion of biodiesel production in global stage. As discussed in the literature [18], there are concerns on safety, environmental performance, etc. that should be taken into consideration. Therefore, the sustainability assessment on biodiesel production is selected as case study in this paper, so that the aspects of inherent SHE and EP can be further assessed. For biodiesel production, three routes have been developed, i.e. microemulsion, thermal cracking (pyrolysis) and transesterification [25]. Nonetheless, the most common method used for biodiesel production is known as transesterification reaction [18]. At present, biodiesel production through transesterification is expected to continually increase as an effort to reduce total dependency on petroleum fuel [2]. In this work, assessment of biodiesel production pathway via enzymatic transesterification using waste oil is performed as it is found to have higher performance in inherent SHE and EP compared to other alternatives (e.g. base-catalysed, acid-catalysed, or supercritical transesterification) [43,21]. Besides, the presence of uncertainties, such as waste oil flow rate, waste oil and enzyme price, is also considered in the assessment. Note that those three uncertainties tend to vary according to the biodiesel demand, and hence it is important to evaluate those uncertainties in this work.

Step 1: Identification of the presence of different operational periods and the uncertainty parameters

Step 2: Determination of appropriate assessment methods of IS, IH, IE and EP

Step 3: Determination of the relevant manipulated variables related to the components on piping and process modules

Step 4: Development of the mathematical optimisation model

Process data (through simulation), and chemical data

Multiple periods and uncertainties

Assessment methods of IS, IH, IE and EP

Step 5: Integration of multi-period optimisation and fuzzy optimisation into the mathematical optimisation model

Step 6: Sensitivity analysis based on the potential variation of the uncertainty parameters

Step 7: Identification of the most optimum range of operating condition and the process modules sizing Fig. 1. Overall approach of the proposed assessment framework.

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

4

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

2. Systematic assessment framework The proposed systematic framework presents the holistic approach for chemical production pathway assessment with uncertainty analysis. The step-by-step methodology for assessment and the formulation of the mathematical optimisation model are described in this section. Fig. 1 shows the overall approach of the proposed assessment framework. Note that it consists of six major steps. In Step 1, the variation of the operational periods is first determined. For instance, the plant operation mode is changed according to the product demand in certain period. In such circumstance, several operational periods which are subjected to product demand can be classified, and this forms multiple operational periods. In line with the determination of multiple operational periods, the associated potential uncertainty parameters related to the operation of the plant should also be identified, e.g. feedstock supply, etc. Next, various assessment methods for inherent safety, IS, inherent health, IH, inherent environment, IE and EP which are appropriate for application in early process design stages, are identified (Step 2). As discussed in previous section, process data that are available in early design stage, i.e. mass flow rate, stream composition, pressure, temperature, energy flow rate, etc., are used as input data for the selected assessment methods. In Step 3, the relevant manipulated variables in terms of the process operating condition which are influential to the inherent SHE performance and EP (e.g. flow rate, pressure, etc.) can be determined based on literature or experimental study. The manipulated variables applied in this assessment are focused on the reactor operating

condition and it is discussed in latter section of this paper. Apart from that, the sizing data of the process modules is also taken as the manipulated variables. In this case, the most optimum sizing of process modules which fulfils the process requirement, as well as with the lowest capital investment cost, can be determined. After determining the aforementioned details as stated in Steps 1–3, a mathematical optimisation model is developed (Step 4). Through this mathematical model, the process data required for the assessment (i.e. stream mass flow rate, stream composition, energy consumption, inventory of process modules, etc.) can be generated. As discussed previously, the expressions for the sizing of the process modules is configured in the model. In this work, the most optimum dimension of sizing is determined through the optimisation. Subsequently, mathematical expressions of the assessment methods (for IS, IH, IE and EP), and fuzzy optimisation as well as multi-period optimisation approach, are also configured into the model (Step 5). The optimisation can be executed by any commercial optimisation software to determine the optimum set of operating condition according to each operational period, as well as the sizing data of process modules. To improve the result of optimisation, sensitivity analysis is conducted to evaluate the impact incurred to the operating conditions and sizing of the process modules due to the variation of the uncertainty parameters (Step 6). In Step 7, the feasible range of operating condition for each operational period, as well as the largest process modules size can be finalised from the result of the sensitivity analysis. Note that the largest calculated process modules size should be taken as the final design data. This is to ensure that the process modules are sufficiently designed to cater

Mathematical Optimisation Model

Periods

Model Output

Assessment

Low Season

Mass flow rate

IS Assessment Model

Intermediate Season

Utility energy consumption

High Season

Chemical inventory Fugitive emission

Uncertainties Waste Oil Supply Flow Rate

Potential environmental impact of output streams

IH Assessment Model IE Assessment Model EP Assessment Model

Waste Oil Price Lipase Enzyme Price

Operating Condition

Optimisation

Process modules sizing

Fuzzy Optimisation

Capital and Production Cost

Multi-Period Optimisation

Fig. 2. Overview of mathematical optimisation model.

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

for the worst scenario throughout all possible operational periods. The detailed methodology of optimisation and uncertainty analysis are described in the latter section.

HQ k ¼

ck cEL k

5

8k

ð4Þ

X HQ k

ð5Þ

3. Mathematical optimisation model As shown in Fig. 2, there are several main components included in the mathematical optimisation model. From the figure, the predetermined operational periods (expressed in the form of occurrence probability) and the uncertainties serve as input variables to the mathematical optimisation model. Based on the defined manipulated and constraint variables, the output data (as shown in Fig. 2) are generated for the assessment through the application of fuzzy optimisation and multi-period optimisation approach. In this assessment, the model is developed using commercial optimisation software, LINGO v.14 with Global Solver [22]. In the following sections, the related assessment methods used and the adopted optimisation approach are discussed. 3.1. Inherent SHE assessment model

ð1Þ

For inherent health assessment, the estimation of fugitive emission is emphasised in HQI. In specific, the total amount of fugitive emission of chemical k at streams connected to the process modules is calculated using Eq. (2). As shown in the equation, the stream composition of chemical k for the stream j connected to process module l,xj,l,k, and the standard emission rate based on the PM process module, FEPM j;l are involved [9]. Hassim et al. [10], FEj;l is dependent on the types of the process modules involved, and the characteristics of the process stream (i.e. light liquid, heavy liquid or gas/vapour phase). Next, the total amount of fugitive emission for chemical k,

FETotal is calculated via Eq. (2). Based on FETotal and air volumetric k k flow rate, VAir, the concentration of chemical k in air, ck can be computed using Eq. (3). Note that VAir can be quantified based on the estimated plot area, height of the plant and speed of wind, as described in Hassim and Hurme [9]. As expressed in Eq. (4), the health quotient, HQk, is calculated by dividing the ck with the exposure limit of chemical k, cEL k . Finally, total health quotient index, HQTotal is calculated via Eq. (5), which represents the score index of inherent health, IH. X ¼ xj;l;k FEPM 8k ð2Þ FETotal k j;l j;l

ck ¼

FETotal k V Air

k

Following the inherent safety and health assessment, the inherent environmental assessment is performed using the method of generalised WAR [5]. In WAR, the potential environmental impact (PEI) of the chemicals in the process streams is evaluated. In this assessment, the PEI value resulted by the nonproduct outlet streams, PEINon
Prod
O is emphasised as this gives direct impact to the environment. In this work, PEINon
Prod
O is taken as the score index of inherent environment, IE. According to Cabezas et al. [5], the relevant expression used is shown in Eq. (6). From the equation, flow rate of outlet non-product n’, F Non
Prod
O , n0 composition of chemical k in stream n’, xn’,k, standard PEI value for chemical k, ck, and the product flow rate, F Prod are included. n

There are three individual assessment methods used for inherent safety, health and environmental assessment. Those methods are known as Inherent Benign-ness Indicator (IBI) [39] for IS assessment, Health Quotient Index (HQI) [9] for IH assessment, generalised Waste Reduction Algorithm (WAR) [5] for IE assessment. As discussed in literature [21], those methods can be suitably applied for sustainability assessment. For inherent safety assessment, there are seven indices in IBI, i.e. reactivity, R’, explosiveness, E’, flammability, F’, heat of reaction, DHR’, pressure, P’, process yield, Y’ and temperature, T’. The additional index of chemical inventory, I’ is also included in this IS assessment for more comprehensive evaluation [21]. The original scoring details of I’ is referred to Heikkilä [9]. The inherent safety index score, IS can be calculated via Eq. (1). IS ¼ R0 þ E0 þ F 0 þ DHR 0 þ P0 þ Y 0 þ T 0 þ I0

HQ Total ¼

PEINon
Prod
O ¼

ð3Þ

ð6Þ

Following the description on IS, IH and IE assessment methods, it should be noted that there are no specific criteria of acceptance on the performance of those three objectives. Nonetheless, in this proposed assessment framework, the objective is set to minimise the inherent SHE impacts to be as low as possible. Alternatively, the user or the decision makers could opt to specify the requirement on the inherent SHE performance based on process requirement, regulatory or company standard and budget, etc. 3.2. EP assessment model The score index of economic performance, Eco used in this assessment is expressed as return on investment, ROI. The target of the optimisation is to maximise the ROI to achieve the highest economic performance. ROI is a robust indicator as the comprehensive economic aspects, i.e. income tax rate, IncTax, annual sales revenue, REV, annual production cost, COSTProd, and the total capital investment based on process modules, COSTTPEC,PM, are considered (refer to Eq. (7)).    1
IncTax REV
COST Prod ROI ¼ ð7Þ COST TPEC
PM As shown in Eq. (8), REV refers to the selling revenue from the product of biodiesel, and the by-product of glycerol. REV is quantified based on the flow rate of product n, F Prod , unit price of n

Table 1 The breakdown of capital cost based on factorial method [44]. Category TPDC

COST

COSTTPIC COSTTPC COSTCFC

8k

Sn0 Sk F Non
Prod
O xn0 ;k ck 0 n0 n; n2j Prod Sn F n

COSTDFC COSTWC COSTTCI

Description

Factorial Item

Total purchase equipment cost (TPEC) Installation and materials Engineering design Construction Total plant cost Contractor’s fee Contingency Total depreciable capital Working capital Total capital investment

100% COSTTPEC 25% COSTTPEC 25% COSTTPDC 35% COSTTPDC COSTTPDC + COSTTPIC 5% COSTTPC 10% COSTTPC COSTTPC + COSTCFC 15% COSTDFC COSTDFC + COSTWC

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

6

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

product n, REV ¼

COSTUnit
Prod , n

as well as the annual operating hour, HY. !

X Prod F n COSTUnit
Prod n

 HY

n2j

ð8Þ

   Ves
Shell Ves Ves W Ves HVes lpv ¼ p Dlpv þ thlpv lpv þ 0:8Dlpv Ves
Shell

thlpv

rVes lpv

8lpv 2 lpvr; lpvd; lpvt

ð13Þ

n

On the other hand, referring to Eq. (8), COSTProd is calculated based on the total cost of raw materials, COSTFeed, utility cost, COSTUtil, and annual operating hour, HY. COSTFeed is calculated based on the raw material consumption rate, F Feed and its unit cost, m . For utility cost, COSTUtil, it accounts for the low and COSTUnit
Feed m medium pressure steam, cooling water, and electricity required for the operation of the plant. Refer to Eq. (11), the utility consumption rate for process modules l, i.e. low pressure steam, CONSUtil
LPS , l medium pressure steam, CONSlUtil
MPS , cooling water, CONSlUtil
CW and electricity, CONSlUtil
Elec are quantified by the model based on the energy requirement. From the equation, the unit cost of those , COSTUnit
MPS , COSTUnit
CW four types of utilities, i.e. COSTUnit
LPS l l l and

COSTUnit
Elec l

COST Prod

are included in the calculation.  ¼ COST Feed þ COST Util  HY n 2 j 

X Feed F m COSTUnit
Feed m

COST Feed ¼

m2j

ð9Þ

ð10Þ

m

COST

Util

X X CONSlUtil
LPS COSTUnit
LPS þ CONSlUtil
MPS COSTUnit
MPS

¼

l

l

X CONSUtil
CW COSTUnit
CW þ CONSUtil
Elec COSTUnit
Elec l l

X l

!

l

ð11Þ

It should be noted that the volume of the vessel should fulfil the process requirement. As shown in Eq. (14), the installed volume of reactor (term in left hand side) have to be larger than the required volume (term in right hand side). Note that the installed volume of Ves reactor is calculated based on the diameter, DVes lpr and height, Hlpr of reactor lpr. Besides, the required volume is calculated based on the

flow rate of inlet stream j to reactor lpr, F Re
In j;lpr , and the reactor residence time, tRe lpr . 0 1 p Ves 2 Ves @X Re
In A Re D tlpr Hlpr  F j;lpr 4 lpr j

pressure vessel lpv, COST Ves lpv can be calculated based on the weight of the vessel, W Ves lpv , as shown in Eq. (12): n h i Ves COST lpv ¼ exp 6:775 þ 0:18255 InW Ves lpv 

2 þ0:02297 InW Ves lpv  g

8lpv 2 lpvr; lpvd; lpvt

F Prod and the required storage duration, tSt n of product n (term in n right hand side). In Eq. (15), DVes n;lpvt and refer to the diameter and height of storage tank vessel, lpvt, for product n. pX Ves 2 Ves   F Prod D H tSt 8n ð15Þ n n 4 lpvt n;lpvt n;lpvt When the platform and ladders for the vessel are needed, its cost, COST Ves
PL can be calculated via Eq. (16). lpv  0:73960  0:70684 ¼ 285:1 DVes HVes COST Ves
PL lpv lpv lpv 8lpv 2 lpvr; lpvdlpvt

Ves
Shell

The total cost of the vessel, COST PM lpv is calculated using Eq. (17):

Ves
Shell

ð12Þ

, tangent-to-tangent height of shell, HVes lpv ,

and rVes are found from material design data. lpv

Ves;PL Ves COST PM lpv ¼ COST lpv þ COST lpv

8lpv 2 lpvr; lpvd; lpvt

ð17Þ

Apart from pressure vessel, the purchase cost for distillation column is computed. As shown in Eq. (18) [17], the diameter of the distillation column, DDist ldc is calculated by considering the factors of Dist
D , reflux ratio, RRDist distillate flow rate, F ldc ldc , and the allowable Dist vapour velocity, GDist ldc . The value of Gldc can be obtained through Dist is the coefficient derived from tray spacing Eq. (19), where COEF and liquid surface tension, rLldc and rVldc are the density of liquid and

calculated based on spacing of plate, SPDist ldc and number of plates,

, as shown in Eq. (13). Note that both and the material density, rVes lpv thlpv

ð16Þ

vapour. Subsequently, the height of the distillation column, HDist ldc is

Ves W Ves lpv is expressed as a function of the internal shell diameter, Dlpv ,

shell thickness, thlpv

ð14Þ

Next, refer to Eq. (15), the total storage volume of product n for all storage tank vessel lpvt (term in left hand side), must be larger than the required storage volume calculated based on the flow rate,

TPEC
PM

, is mainly Next, for the total capital investment, COST based on the purchase cost of process modules together with other associated engineering costs. In this assessment, the cost for process modules l are inclusive of pressure vessel, lpv, distillation column, ldc and heat exchanger, lhe. Pressure vessel, lpv consists of reactor, lpvr, decanter vessel, lpvd, and storage tank, lpvt. The purchase cost calculation for the aforementioned process module is referred to Seider et al. [36]. The purchase cost for

8lpr

(see Eq. (20)). Note that qDist is dependent on the distillation qDist ldc ldc column design according to the composition of the desired product quality and the feed stream. 30:5 2 Dist;D  Dist F ldc RRldc þ 1 Dist 4 5 8ldc ð18Þ Dldc ¼ 0:785GDist ldc

Table 2 List of uncertainties according to three given periods. Uncertainty Parameters

Low Demand Period

Medium Demand Period

High Demand Period

Waste oil flow rate (kg/hr) Waste oil price (USD/kg) Enzyme price (USD/kg)

70,000 0.42 846.27

84,700 0.53 310.07

100,000 0.59 100.00

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

7

as shown in Eq. (26). GDist ldc

  0:5 ¼ COEFDist rLldc rLldc
rVldc

Plate Plate HDist ldc ¼ SPldc  qldc

DDist ldc

8ldc

8ldc

and HDist Other than ldc , and the material density, Dist ldc

ð19Þ

ð20Þ the thickness of the shell,

r

Dist
Shell thldc

can be determined from the material design data, and all those variables are needed to calculate the weight of the column, W Dist ldc using Eq. (21) (modified from Eq. (13)). Next, the purchasing cost of single tower of the distillation column can be determined via Eq. (22).    Dist
Shell Dist
Shell Dist Dist Dist HDist thldc W Dist rldc 8ldc ldc ¼ p Dldc þ thldc ldc þ 0:8Dldc ð21Þ  h  i h  i2 Dist Dist þ COST Dist ¼ exp 7:0374 þ 0:18255 In W 0:02297 In W ldc ldc ldc

ð22Þ Apart from the cost of the tower, calculation of the platform and Dist
PL is also done (refer to ladder cost for distillation column, COST ldc

Eq. (23)). Lastly, the total cost of the distillation column, COST PM ldc is quantified via Eq. (24).  0:63316  0:80161 ¼ 237:1 DDist HDist 8ldc ð23Þ COST Dist
PL ldc ldc ldc

Dist Dist
PL COST PM ldc ¼ COST ldc þ COST ldc

8ldc

ð24Þ

AHEX lhe ¼

Q HEX lhe

HEX UHEX lhe DT m;lhe

8lhe

ð25Þ

n h  i HEX COST PM lhe ¼ exp 11:0545
0:9228 In Alhe h  i2 Mat Len gftPres þ0:09861 In AHEX lhe lhe ftlhe ftlhe

8lhe

ð26Þ

Following the aforementioned calculations, the total purchase cost for all process modules, COSTTPEC can be estimated by using the cost index method as expressed in Eq. (27). Note that I1 in Eq. (27) is the latest Chemical Engineering (CE) index (generally known as plant cost index) [31], while the I0 is the base CE index. In this case study, the value of I1 and I0 are given as 612 and 394 respectively. 0 X X X TPEC COST ¼ @ COST PM COST PM COST PM lpvr þ lpvd þ lpvt lpvr

lpvd

lpvt

X X I1 þ COST PM COST PM ldc þ lhe Þ I0 ldc lhe

ð27Þ

Lastly, the total capital investment, COSTTCI is quantified using the factorial method summarised in Yi et al. [44], as tabulated in Table 1. From the table, the total plant cost, COSTTPC is calculated based on total plant direct cost, COSTTPDC and total plant indirect cost, COSTTPIC. The total depreciable capital, COSTDFC covers the total plant cost, COSTTPC and contractor’s fee and contingency, COSTCFC. Lastly, COSTTCI is obtained by summing the COSTDFC and working capital, COST WC.

For heat exchanger, its purchased cost is generally related to the heat transfer surface area Seider et al. [36]. The heat transfer

3.3. Fuzzy optimisation and multi-period optimisation approach

surface area, AHEX lhe can be estimated through the heat transfer rate,

In the proposed framework, there are two types of optimisation approaches applied simultaneously, i.e. fuzzy optimisation and multi-period optimisation. In specific, fuzzy optimisation is used for multi-objective analysis, while multi-period optimisation is applied to optimise the system by addressing multiple operational periods with presence of uncertainties. Fuzzy optimisation is developed based on the theory of fuzzy set [3]. In principle, this optimisation approach is aimed to model the system as fuzzy model. As discussed in literature [29], through fuzzy optimisation, single objective can be established by aggregating the multi-objective via max-min aggregation approach Zimmermann

HEX Q HEX lhe , overall heat transfer coefficient, Ulhe , and the mean

temperature driving force, DT HEX m;lhe , as shown in Eq. (25). The base purchasing cost of different types of heat exchangers can be calculated based on AHEX lhe , as stated in Seider et al. [36]. For instance, shell-and-tube heat exchanger is applied in this work and its total HEX purchase cost, COST PM lhe , is quantified based on Alhe , pressure factor, Mat Len ftPres lhe , material factor, ftlhe , and tube-length correction factor, ftlhe ,

Fig. 3. Illustration of process flow diagram (Aspen HYSYS simulation sheet) for pathway of enzymatic transesterification using waste oil [21].

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

8

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

Table 3 Result of sensitivity analysis based on three uncertainty parameters. Impacta

Parameters

Waste Oil Flow Rate

Waste Oil Price

Enzyme Price

Operating condition tert-butanol concentration (%) Reactor residence time

No (All) Minor (All)

No (All) Minor (All)

No (All) Minor (All)

Process module Reactor, C-100 Volume (m3)

No (L, M); Minor (H)

No (L, M); Minor (H)

No (All)

Distillation column, T-100 Height (m) Diameter (m) Condenser heat transfer area (m2) Reboiler heat transfer area (m2)

No (All) Minor (All) Minor (All) Minor (All)

No No No No

(All) (All) (L, M), Minor (H) (L, M), Minor (H)

No No No No

(All) (All) (All) (All)

Distillation column, T-101 Height (m) Diameter (m) Condenser heat transfer area (m2) Reboiler heat transfer area (m2)

No (All) Minor (All) Minor (All) Minor (All)

No No No No

(All) (All) (L, M), Minor (H) (L, M), Minor (H)

No No No No

(All) (All) (All) (All)

Decanter, X-100 Volume (m3)

Minor (All)

No (All)

No (All)

Decanter, X-101 Volume (m3)

Minor (All)

No (All)

No (All)

Heat exchanger, E-100 Heat transfer area (m2)

Minor (All)

No (All)

No (All)

Heat exchanger, E-101 Heat transfer area (m2)

Minor (All)

No (All)

No (All)

Biodiesel storage tank Total volume for all tanks (m3)

Minor (All)

No (All)

No (All)

Glycerol storage tank Total volume for all tanks (m3)

Minor (All)

No (All)

No (All)

a

No: Change <1%; Minor: Change within 1% to 10%; Medium: Change within 10% to 30%; High: Change >30%; L: Low demand period; M: Medium demand period; H: High demand period; All: All operational periods.

[45]). As described in Rommelfanger [33], this approach avoids the need for intensive information processing which is commonly required in classical modeling method. Besides, large quantity of data is not required in fuzzy optimisation and instead, only the available data in early design stage is needed for the solution of the multiobjective problem [33]. At first, the optimisation target is set based on individual objective, i.e. minimisation of IS, IH and IE (refer to Eq. (28)), and maximisation of Eco (refer to Eq. (28)). Based on the results, the maximum and minimum values of each index are taken as the L U upper, XU p and lower, Xp fuzzy limits. Note that, the range within Xp

and XLp is denoted as the feasible range of each objective X, and the most optimum value of X under that range is to be determined via fuzzy optimisation in the next step. Since multi-period is considered, the index of lX,p is known to be subject to the operational period p. Next, the lXp can be aggregated into single index of lp using Eq. (30). Note that the application of Eqs. (28)– (30) enable the effective trade-off of multi-objective. Lastly, lp which represents mutual performance of all objectives, X of inherent SHE and EP dedicated for operational period p, can be computed.

lX;p ¼

XU p
Xp L XU p
Xp

8X8p

ð28Þ

lX;p ¼

X p
XLp L XU p
Xp

lp  lX;p

8X8p

ð29Þ

ð30Þ

In line with fuzzy optimisation, the multi-period optimisation is also executed to analyse the most optimum operating condition that leads to the least hazard of inherent SHE and maximised EP for a given set of periods. This optimisation approach has been widely used in chemical engineering researches [41]. For solving multiperiod optimisation problem, the occurrence probability, ap for each operational period p is introduced and integrated into the fuzzy optimisation as expressed in Eq. (30). The ap is defined as the time fraction, which represents the ratio of specific period p against the total duration considered [1]. As case study, the monthly biodiesel production volumes from year 2012–2015 [42] is used to estimate the ap for three periods, as 0.2821, 0.4615 and 0.2564 respectively. Subsequently, the optimisation objective can be set as the maximisation of the expression with integration of ap and lp, as shown in Eqs. (31) and (32). X ð31Þ Max ap lp p

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

Subject to X ap ¼ 1

ð32Þ

two concentration of tert-butanol are considered, i.e. 26.5% and 32.5% [34]. 2 Re CONV lpr ¼
0:0956tRe lpr þ 3:5863t lpr

p

In the next step, this work is continued with sensitivity analysis based on the variation of each uncertainty parameters. From the analysis, the impacts of operating condition and sizing of process modules, as well as the performance on IS, IH, IE and EP are evaluated.

þ 62:3550 ¼ 26:5%Þ

For enzymatic transesterification, lipase enzyme is used [8]. During the transesterification, one mole of oil (triglycerides), C3 H5 ðRCOOHÞ3 reacts with three moles of methanol, CH3 OH, to produce three moles of biodiesel (or called fatty acid methyl esters (FAME)), RCOCH3 Oand one mole of glycerol, C3 H5 ðOHÞ3 as byproduct (see Eq. (33)). In waste oil, the content of free fatty acids is converted through esterification reaction by methanol. As expressed in Eq. (34), one mole of fatty acid, R’(COOH) is reacted with one mole of methanol, CH3OH and produces one mole of FAME and one mole of water.

ðforC TB lpr

ð35Þ

2 Re CONV lpr ¼
0:0420tRe lpr þ 1:9394t lpr

þ 75:8440 ¼ 32:5%Þ

4. Case study: enzymatic transesterification using waste oil

C3 H5 ðRCOOHÞ3 þ3CH3 OH ! 3RCOCH3 O þC3 H5 ðOHÞ3

9

ðforC TB lpc

ð36Þ

Apart from the aforementioned manipulated variables related to operating condition, the sizing of the process modules is also manipulated to achieve the minimised purchased cost while fulfilling the process requirement. Refer to the previously discussed EP assessment model, the sizing variables to be Ves optimised are inclusive of diameter and height (DVes lpv and Llpv ) for pressure vessels (i.e. reactor, decanter, and storage tank), distillaDist tion column (i.e. DDist ldc and Lldc ), and heat transfer area for heat

exchanger (i.e. AHEX lhe ).

ð33Þ Table 4 Result of feasible operating condition and maximum sizing of process modules.

R0 ðCOOHÞ þCH3 OH ! R0 COOCH3 þH2 O

ð34Þ

4.1. Manipulated variables and uncertainties Enzymatic transesterification shows unique advantages, i.e. insensitive to free fatty acids and water content in oil, low energy consumption, mild operating condition, etc. Despite the aforementioned advantages of enzymatic transesterification, there are some issues associated with the productivity that need to be improved [6]. Firstly, the problem of slow reaction rate should be taken into account [26]. It was found that the reactor residence time could potentially be prolonged to 48 h, in order to achieve at least 90% of biodiesel yield [8]. Note that the residence time is important in determining the plant productivity, as well as the cost of process modules due to increased size [23]. Apart from that, deactivation or inhibition of lipase enzyme activity occurs when the presence of methanol increases the mass transfer limitation at surrounding of lipase enzyme. When the methanol-to-oil ratio is more than 1.5:1, the serious inhibition on the lipase enzyme could potentially occur [32]. Besides, the formation of glycerol during reaction would form the coating on the lipase and subsequently reduce the enzyme activity. In order to resolve this issue, tert-butanol is introduced as solvent in reaction [38] to reduce the limitation of mass transfer between the substrate and lipase enzyme. As discussed in literature [34], the introduction of tert-butanol have improved the reaction time to be within 24 h and the yield of FAME of close to 95%. Based on the aforementioned description on enzymatic transesterification, it is found that the influential factors to the reaction are greatly attributed to the residence time of reactor, as well as the added concentration of tert-butanol. Hence, in this work, both tertbutanol concentration in reactor, and the reactor residence time are taken as manipulated variables, while the optimum settings are determined through optimisation. The mathematical expressions involving those mentioned manipulated variables have been determined from literature [34]. As shown in Eqs. (35) and (36), the polynomial expressions are related to the relationship of tertRe butanol concentration, cTB lpr and the reactor residence time, tlpr towards the reaction conversion of oil feedstock, CONVlpr. Note that

Parameters

Low Medium Demand High Demand Demand

Operating condition tert-butanol concentration (%) Reactor residence time (hr)

32.5 12.49

32.5 11.91

32.5 11.21
12.42

Process modulea Reactor, C-100 Diameter (m) Height (m)

N/A N/A

N/A N/A

13.89 15.00

Distillation column, T-100 Height (m) Diameter (m) Condenser heat transfer area (m2) Reboiler heat transfer area (m2)

N/A N/A N/A N/A

N/A N/A N/A N/A

8.00 2.53 575.4 245.8

Distillation column, T-101 Height (m) Diameter (m) Condenser heat transfer area (m2) Reboiler heat transfer area (m2)

N/A N/A N/A N/A

N/A N/A N/A N/A

13.00 3.00 148.51 69.65

Decanter, X-100 Height (m) Diameter (m)

N/A N/A

N/A N/A

5.00 11.98

Decanter, X-101 Height (m) Diameter (m)

N/A N/A

N/A N/A

5.00 7.16

Heat exchanger, E-100 Heat transfer area (m2)

N/A

N/A

32.00

Heat exchanger, E-101 Heat transfer area (m2)

N/A

N/A

27.53

Biodiesel storage tank Units Height (m) Diameter (m)

N/A N/A N/A

N/A N/A N/A

5 20.00 17.72

Glycerol storage tank Units Height (m) Diameter (m)

N/A N/A N/A

N/A N/A N/A

3 10.00 9.98

a

The largest sizes of the process modules are shown.

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

10

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

0.960

3.34

3.30

3.15

3.15 3.04

3.03

3.02

HQI Score (× 10-1 unitless)

IBI Score (unitless)

3.50

0.958

0.96

0.955

0.955

0.951

0.950

0.945

2.80 Low

Medium

Medium

Low

High

(a)

(b) 1.60 2.83

2.80 2.70 2.60 2.49

2.40

2.39

2.37

2.30

1.51

1.40 Eco Score (Unitless)

PEI Score (× 10-2 unitless)

2.90

2.50

High

Demand Period

Demand Period

1.20 1.00

0.99

0.80

0.76 0.66

0.60

0.72 0.50

0.40 0.20

2.20

0.00

2.10 Low

Medium

High

Low

Medium

Demand Period

Demand Period

(c)

(d)

High

Fig. 4. Result of assessment based on four individual objectives of (a) IS, (b) IH, (c) IE and (d) Eco.

Following the determination of manipulated variables, the uncertainty parameters are identified. In general, the uncertainty analysis on biodiesel production reported in the literature is mostly related to two categories. The first category is about the supply of raw materials, e.g. feed oil composition [24], waste cooking oil availability [30], etc. Besides, economic factors are also considered, e.g. enzyme price [38], raw material price [4], capital and operating cost [40], etc. Based on the reported works, the list of the uncertainties are summarised in Table 2, which consists of waste oil flow rate, waste oil price, and enzyme price. The waste oil flow rate is dependent on its availability which could be varied due to external factors [30]. In high demand period, it is fixed as 100,000 kg/h which is estimated based on the existing biodiesel plant with the largest capacity [27]. In this assessment, the variation of-30% based on 100,000 kg/h is taken for low demand period. Besides the waste oil availability, its price is somehow uncertained and influenced by the demand and weather condition [37]. The waste oil price could be increased for about 30–40% during high demand period [37]. In this case, the waste oil price of USD 0.59/kg is estimated for high demand period. For enzyme price, lower price is assumed during high demand period in future as the expected market availability of the enzyme would be higher [38]. The price of enzyme ranged from 100.000 to 846.27 USD/kg is assumed in this work [38]. Next, in order to increase the uncertainty level, the waste oil flow rate and price are set to have variation of 5%, while variation of 10% is applied to enzyme price. Note that enzyme price may subject to higher fluctuation in market [38]. 4.2. Process flow sheet description Refer to Fig. 3, the process flow diagram generated using Aspen HYSYS simulator (version 8.1) [21] is presented. The process is

started by pumping the feedstock of methanol, waste vegetable oil and tert-butanol at pump P-100, P-101 and P-102 to the discharge pressure of 250 kPa. Note that tert-butanol is introduced as solvent to accelerate the transesterification reaction. After discharging from those three pumps, three feedstock are mixed at mixer, MIX101, together with the recycled stream (Stream 120). At MIX-101, the molar ratio of methanol-to-oil is fixed as 4.83 [21], while molar ratio of tert-butanol could be taken as 26.5% or 32.5% (as discussed in previous section). After MIX-101, the Stream 108 is heated up at heater, E-100 to temperature of 60  C. The heated Stream 109 is then sent to reactor, C-100 which is embedded with immobilised lipase enzyme for transesterification reaction. The outlet stream from reactor (Stream 111) is routed to distillation column T-100 for separating the tert-butanol and methanol as top product (Stream 113) and biodiesel and glycerol as bottom product (Stream 114). Stream 113 is routed to decanter, X-100 where the top product (Stream 117) is recycled to MIX-101. On the other hand, Stream 114 is routed to a decanter, X-101 where the light phase is discharged as by-product glycerol (Stream 115). The heavy phase from X-101 (Stream 116) is channelled to distillation column, T-101. At T-101, the biodiesel is separated as top product (Stream 123), while the unreacted triolein is discharged as bottom product (Stream 124) which is to be reused as raw material. 5. Sustainability assessment Based on the proposed framework, the assessments with uncertainty analysis have been conducted through the proposed assessment framework. The result of the sensitivity analysis is summarised in Table 3. For the waste oil flow rate, it is noted that it incurs minor impact to the reactor residence time and the sizing of most of the process modules. Next, the costing factors in term of waste oil and enzyme price are analysed. For waste oil price, it is found that its impact is negligible for low and medium demand

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

Fig. 5. Comparison of sustainability level for three given periods.

period, while only minor impact is noted on certain sizing parameters at high demand period. On the other hand, for enzyme price, it has shown no impact to almost all parameters except the minor impact on reactor residence time. Based on both waste oil and enzyme price, it can be seen that the optimised operating condition in terms of tert-butanol concentration and reactor residence time and sizing of process modules are less affected by both mentioned economic factors. From those findings, it can be seen that fuzzy optimisation approach has effectively identified the most optimal solution that achieves the highest performance of all the objectives, and robust towards the external variation on economic aspect. In addition, it is shown that the process stream flow rate gives more direct impact to the sizing of process modules when compared with the impact from cost factors, even though the variation considered on flow rate change is just 5%. This is because the adjustment on the size or capacity of process module is certainly required when the process stream flow rate is increased. For instance, through this assessment, the sizing of process modules should be designed according to the highest possible flow rate of waste oil. Apart from identifying the impacts of the uncertainty parameters, the feasible range of tert-butanol concentration, cTB lpr and reactor residence time over all three considered periods is determined in this assessment. As seen in Table 4, higher value of cTB lpr i.e. 32.5% is maintained as the most optimum value for all three considered operational periods. In fact, higher cTB lpr would increase the chemical inventory in process and the energy consumption in separation section (e.g. distillation). But, in this assessment, note that higher value of cTB lpr is selected for sustaining higher reaction conversion and this contributes to higher level of sustainability. In terms of tRe lpr , the optimised setting is almost approximated for each period, which is ranged from 11.2 to 12.5 h. This optimised tRe lpr refers to reaction conversion of at least 92%. Based on the aforementioned result, it is noted that the operating condition is less influenced by those three considered uncertainties. On the other hand, the largest sizing of process modules has also been determined via sensitivity analysis. As shown in Table 4, the largest sizing obtained is associated with the high demand period. It is noted that this sizing is mainly attributed by the higher flow rate of waste oil (compared to during the low and medium demand period), which has concomitantly increased the sizing of the process modules. Apart from the result of the feasible operating condition and sizing of process modules, the optimised scorings for four individual objective indices of IS, IH, IE and Eco over three operational periods are obtained from the sensitivity analysis, as illustrated in Fig. 4. As mentioned in the previous section, the values of IS, IH and IE are minimised, while Eco is maximised. From

11

Fig. 4, it is observed that for each individual objective of IS, IH and IE, there is no obvious deviation shown over each operational period. This would be mainly due to the computed tert-butanol concentration and the reactor residence time amongst the periods are approximate to each other (refer to Table 4). As consequence, the process parameters, i.e. reaction yield, stream composition and process inventory, are not differed too much in each demand period. Nonetheless, for the scoring value of Eco, higher ROI is found on low demand period, and it is reducing from medium to high demand period. In high demand period, the waste oil price is much higher and this increases the production cost. Besides, the sizing of process modules is increased and this has therefore elevated the total capital investment cost. In this case, the lower cost of enzyme does not provide much advantage to the ROI and as result, the ROI in high demand period is decreased. Lastly, the optimised sustainability level based on each operational period is finalised in Fig. 5. The sustainability for medium demand period, i.e. lp = 0.6139, is found as the highest value. In overall, the values of lp for low and medium period are close to each other. This can be seen that, higher impact of IH but better EP is found on low demand period, while lower impact of IH but poorer EP is seen during medium demand period. For high demand period, its sustainability level is lower than both low and medium demand period, and this could be resulted by the slightly higher impact of IE, and poorer EP. In summary, the optimised operating condition and sizing of process modules have been effectively identified using the proposed framework. Besides, the consideration of uncertainties in chemical process design together with multi-objective of inherent SHE and EP is noted as a potential way to generate a more sustainable chemical production pathway. 6. Conclusions A systematic framework for sustainability assessment on chemical production pathway with consideration of multi-objective of inherent SHE and EP, and the presence of uncertainties, has been proposed. In this framework, the holistic approach of performing the assessment is included. To support the decisionmaking, mathematical optimisation approach is adapted into the framework, i.e. fuzzy optimisation used for multi-objective analysis, and multi-period optimisation applied to address the multiple operational periods. Besides, the mathematical optimisation model is developed to solve this design problem. By using the developed model, the sustainability level of the chemical production pathway, i.e. l, is computed and maximised. As proposed in this framework, the approach of sensitivity analysis is included for determining the optimal values of the key process operating variables and the sizing of process modules. This sensitivity analysis is performed by considering multiple periods and based on the variation of the uncertainty parameters. In this work, the assessment on biodiesel production through enzymatic transesterification using waste oil is performed as case study using the proposed framework. From the assessment, the process variables i.e. reaction time and reaction conversion, and the sizing of process modules, have been optimised. In summary, it is concluded that the developed framework provides simplified and effective guidelines for the design of sustainable chemical production pathway during early design stage. Acknowledgement The authors would like to express their sincere gratitude to the Universiti Teknologi Malaysia and the Ministry of Higher Education (MOHE), Malaysia for supporting the project under the Research University Grant (RUG) R.J130000.7844.4L106.

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003

G Model JECE 1093 No. of Pages 12

12

W.H. Liew et al. / Journal of Environmental Chemical Engineering xxx (2015) xxx–xxx

References [1] V. Andiappan, D.K.S. Ng, S. Bandyopadhyay, Synthesis of biomass-based trigeneration systems with uncertainties, Ind. Eng. Chem. Res. 53 (46) (2014) 18016–18028. [2] A. Avinash, D. Subramaniam, A. Murugesan, Bio-diesel
a global scenario, Renew. Sustain. Energy Rev. 29 (2014) 517–527. [3] R.E. Bellman, L.A. Zadeh, Decision-making in a fuzzy environment, Manage. Sci. 17 (1970) 141–164. [4] G. Brownbridge, P. Azadi, A. Smallbone, A. Bhave, B. Taylor, M. Kraft, The future viability of algae-derived biodiesel under economic and technical uncertainties, Bioresour. Technol. 151 (2014) 166–173. [5] H. Cabezas, C.J. Bare, S.K. Mallick, Pollution prevention with chemical process simulators: the generalized waste reduction (WAR) algorithm, Comput. Chem. Eng. 21 (1997) S305–S310. [6] L.P. Christopher, H. Kumar, V.P. Zambare, Enzymatic biodiesel: challenges and opportunities, Appl. Energy 119 (2014) 497–520. [7] S. Fazlollahi, G. Becker, F. Maréchal, Multi-objectives, multi-period optimization of district energy systems: II
daily thermal storage, Comput. Chem. Eng. 71 (2014) 648–662. [8] S. Hama, A. Kondo, Enzymatic biodiesel production: an overview of potential feedstocks and process development, Bioresour. Technol. 135 (2013) 386–395. [9] M.H. Hassim, M. Hurme, Inherent occupational health assessment during preliminary, J. Loss Prev. Process Ind. Ind. 23 (2010) 476–482. [10] M.H. Hassim, A.L. Pérez, M. Hurme, Estimation of chemical concentration due to fugitive emissions during chemical process design, Process Saf. Environ. Prot. 88 (2010) 173–184. [11] A.M. Heikkilä, Inherent safety in process plant design, Ph.D. Thesis, Helsinki University of Technology, Finland, 1999. [12] G. Hook, Responsible care and credibility, Environ. Health Perspect. 104 (1996) 1138–1139. [13] M. Hurme, M. Rahman, Implementing inherent safety throughout process lifecycle, J. Loss Prev. Process Ind. 18 (4–6) (2005) 238–244. [14] D. Kim, J. Kim, I. Moon, Integration of accident scenario generation and multiobjective optimization for safety-cost decision making in chemical processes, J. Loss Prev. Process Ind. 19 (6) (2006) 705–713. [15] T.A. Kletz, Cheaper, Safer Plants, or Wealth and Safety at Work, Institution of Chemical Engineers, Rugby, United Kingdom, Rugby, 1984. [16] G. Koller, U. Fischer, K. Hungerbühler, Assessment of environment-, healthand safety aspects of fine chemical processes during early design phases, Comput. Chem. Eng. Suppl. 23 (1999) S63–S66. [17] D. Lawrence, Quantifying inherent safety of chemical process routes, Ph.D. Thesis, Loughborough University, 1996. [18] D.Y.C. Leung, X. Wu, M.K.H. Leung, A review on biodiesel production using catalyzed transesterification, Appl. Energy 87 (4) (2010) 1083–1095. [19] W.H. Liew, M.H. Hassim, D.K.S. Ng, Review of evolution, technology and sustainability assessments of biofuel production, J. Clean. Prod. 71 (2014) 11–29. [20] W.H. Liew, M.H. Hassim, D.K.S. Ng, Sustainability assessment for biodiesel production via fuzzy optimisation during research and development (R&D) stage, Clean Technol. Environ. Policy 16 (2014) 1431–1444. [21] W.H. Liew, M.H. Hassim, D.K.S. Ng, N. Chemmangattuvalappil, Systematic framework for sustainability assessment on biodiesel production: preliminary engineering stage, Ind. Eng. Chem. Res. (2015), doi:http://dx.doi.org/10.1021/ acs.iecr.5b02894. [22] Lindo Systems, LINGO: The Modelling Language and Optimizer, Lindo Systems, Inc., 582 Chicago, 2013. [23] P. Lisboa, A.R. Rodrigues, J.L. Martín, P. Simões, S. Barreiros, A. Paiva, Economic analysis of a plant for biodiesel production from waste cooking oil via enzymatic transesterification using supercritical carbon dioxide, J. Supercrit. Fluids 85 (2014) 31–40. [24] M.F. Luna, E.C. Martinez, Model-based run-to-run optimization under uncertainty of biodiesel production, Proceedings of the 23rd European Symposium on Computer Aided Process Engineering – ESCAPE 23, Finland, 2013.

[25] F. Ma, M.A. Hanna, Biodiesel production: a review, Bioresour. Technol. 70 (1) (1999) 1–15. [26] R.W. Moussavou Mounguengui, C. Brunschwig, B. Baréa, Are plant lipases a promising alternative to catalyze transesterification for biodiesel production? Prog. Energy Combust. Sci. 39 (5) (2013) 441–456. [27] Neste Oil, Neste Oil Starts up Its New Renewable Diesel Plant in Singapore, (2010) . (accessed 31.05.13.) www.nesteoil.com/? path=1;41;540;1259;1261;13291;16384. [28] L.Y. Ng, N.G. Chemmangattuvalappil, D.K.S. Ng, Robust chemical product design via fuzzy optimisation approach, Comput. Aided Chem. Eng. 34 (2014) 387–392. [29] R.T.L. Ng, D.K.S. Ng, R.R. Tan, M.M. El-Halwagi, Disjunctive fuzzy optimisation for planning and synthesis of bioenergy-based industrial symbiosis, J. Environ. Chem. Eng. 2 (2014) 652–664. [30] D.S. Patle, S. Sharma, Z. Ahmad, G.P. Rangaiah, Multi-objective optimization of two alkali catalyzed processes for biodiesel from waste cooking oil, Energy Convers. Manage. 85 (2014) 361–372. [31] M.S. Peters, K.D. Timmerhaus, R.E. West, Plant Design and Economics for Chemical Engineers, 5th edition, McGraw-Hill Companies, Inc., New York, United States, 2003 (chapter 6). [32] S.V. Ranganathan, S.L. Narasimhan, K. Muthukumar, An overview of enzymatic production of biodiesel, Bioresour. Technol. 99 (10) (2008) 3975–3981. [33] H.J. Rommelfanger, The advantages of fuzzy optimization models in practical use, Fuzzy Optim. Decision Making 3 (4) (2004) 295–309. [34] D. Royon, M. Daz, G. Ellenrieder, S. Locatelli, Enzymatic transesterification of biodiesel from cotton seed oil using t-butanol as a solvent, Bioresour. Technol. 98 (3) (2007) 648–653. [35] G.J. Ruiz-Merado, M.A. Gonzalez, R.L. Smith, Sustainability indicators for chemical processes: III. Biodiesel case study, Ind. Eng. Chem. Res. 52 (20) (2013) 6747–6760. [36] W.D. Seider, J.D. Seader, D.R. Lewin, Product and Process Design Principles, 2nd edition, John Wiley and Sons, Inc., New York, United States, 2004 (chapter 17). [37] H. Smith, J. Winfield, L. Thompson, The Market for Biodiesel Production from Used Cooking Oils and Fats, Oils and Greases in London, Leading Resource Sustainability (LRS) Consultancy, United Kingdom, 2013. (accessed 11.01.15.) https://www.london.gov.uk/sites/default/files/The%20market%20for%20biodiesel%20production%20from%20UCOs%20and%20FOGs%20in%20London% 20-%20September%202013.pdf. [38] L.F. Sotoft, B.-G. Rong, K.V. Christense, B. Norddahl, Process simulation and economical evaluation of enzymatic biodiesel production plant, Bioresour. Technol. 101 (14) (2010) 5266–5274. [39] R. Srinivasan, N.T. Nhan, A statistical approach for evaluating inherent benignness of chemical process routes in early design stages, Process Saf. Environ. Protect. 86 (3) (2008) 163–174. [40] Z.-C. Tang, Z. Lu, Z. Liu, N. Xiao, Uncertainty analysis and global sensitivity analysis of techno-economic assessments for biodiesel production, Bioresour. Technol. 175 (2015) 502–508. [41] J.F.D. Tapia, R.R. Tan, Fuzzy optimisation of multi-period carbon capture and storage systems with parametric uncertainties, Process Saf. Environ. Protect. 92 (6) (2014) 545–554. [42] U.S. EIA, Monthly Biodiesel Production Report, U.S. Energy Information Administration, 2015. (accessed 15.05.15.) http://www.eia.gov/biofuels/biodiesel/production/. [43] P.T. Vasudevan, F. Boyi, Environmentally sustainable biofuels: advances in biodiesel research, Waste Biomass Valorization 1 (1) (2010) 47–63. [44] Q. Yi, W. Li, X. Zhang, J. Feng, J. Zhang, J. Wu, Tech-economic evaluation of waste cooking oil to bio-flotation agent technology in the coal flotation industry, J. Clean. Prod. 95 (2015) 131–141. [45] H.-J. Zimmermann, Fuzzy programming and linear programming with multiple objective functions, Fuzzy Sets Syst. 1 (1978) 45–55.

Please cite this article in press as: W.H. Liew, et al., Sustainability assessment framework for chemical production pathway: Uncertainty analysis, J. Environ. Chem. Eng. (2016), http://dx.doi.org/10.1016/j.jece.2016.05.003