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Process synthesis, design and analysis using a process-group contribution method Anjan Kumar Tula a , Mario R. Eden b , Rafiqul Gani a,∗ a CAPEC-PROCESS Research Center, Department of Chemical and Bio-chemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, DK-2800 Kgs. Lyngby, Denmark b Department of Chemical Engineering, 210 Ross Hall, Auburn University, AL 36849, USA

a r t i c l e

i n f o

Article history: Received 15 December 2014 Received in revised form 9 April 2015 Accepted 17 April 2015 Available online xxx Keywords: Process synthesis Toluene hydrodealkylation Process-groups Group-contribution CAMD

a b s t r a c t This paper describes the development and application of a process-group contribution method to model, simulate and synthesize chemical processes. Process flowsheets are generated in the same way as atoms or groups of atoms are combined to form molecules in computer aided molecular design (CAMD) techniques. The fundamental pillars of this framework are the definition and use of functional process-groups (building blocks) representing a wide range of process operations, flowsheet connectivity rules to join the process-groups to generate all the feasible flowsheet alternatives and flowsheet property models like energy consumption, atom efficiency, environmental impact to evaluate the performance of the generated alternatives. In this way, a list of feasible flowsheets are quickly generated, screened and selected for further analysis. Since the flowsheet is synthesized and the operations in the flowsheet designed through predictive models to match a set of design targets, optimal solution of a given synthesis problem is guaranteed. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction

1.1. Heuristics or knowledge based methods

Process synthesis can be considered as the cornerstone of the process design activity (Nishida et al., 1981; Douglas, 1985; Barnicki and Siirola, 2004; Westerberg, 2004), it involves identification of the processing route to produce the desired product, investigation of chemical reactions needed, selection and design of the operations involved in the processing route, as well as calculations of utility requirements, the calculations of waste and emissions to the surroundings and many more. In chemicals-based process synthesis, two types of problems exist: in the first type, one seeks to improve an existing process flowsheet (also known as the retrofit problem), while in the second type; one seeks to find a completely new process flowsheet. Due to the fact that process synthesis problems are by nature combinatorial and open ended, a number of different approaches have been proposed. These approaches can be broadly classified into three main classes of methods: methods that employ heuristics or are knowledge based; methods that employ mathematical or optimization techniques; and, hybrid methods that combine different approaches into one method.

The most commonly used methods to solve the process synthesis/design problem are the heuristics based approaches due to their ease of application. These methods rely on a set of rules based on a combination of experience, insights and engineering knowledge (data) to come up with a feasible process alternative for a given synthesis problem. There are numerous examples in the literature of the use of heuristics to solve the synthesis and design problems from the chemical and related industries. Particularly, heuristics dealing with synthesis of separation processes in the chemical industry are fairly well developed. Siirola and Rudd (1971), developed a systematic heuristic approach for the synthesis of multicomponent separation sequences. Seader and Westerberg (1977) developed a method, which combines heuristics with evolutionary methods for synthesizing simple separation sequences. Douglas (1985) proposed a hierarchical heuristic procedure for synthesizing process flowsheets where a set of heuristic rules are applied at different levels to generate the alternatives. In general, knowledge based methods are structured around three models. First, the data model, which includes a structured framework capturing all the available knowledge. Second, the data mining model, it includes the procedures and rules to extract the knowledge from the data model to be applied to the synthesis problem. Third and

∗ Corresponding author. Tel.: +45 45252882; fax: +4545882258. E-mail address: [email protected] (R. Gani). http://dx.doi.org/10.1016/j.compchemeng.2015.04.019 0098-1354/© 2015 Elsevier Ltd. All rights reserved.

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last, the application model, which includes the rules and methods to apply the knowledge extracted through the data mining model to the synthesis problem. While this approach is relatively simple to implement, due to the nature of heuristics and the available knowledge base one can end up with sub-optimal designs. Also, since the heuristic rules are based on observations made on existing processes, the application of heuristic methods requires careful consideration as they may lead to the elimination of novel process flowsheets.

1.2. Mathematical or optimization methods This approach requires one to define a mathematical superstructure of all the feasible unit operations along with their interconnections and find an optimal flowsheet with respect to a pre-defined objective function such as energy minimization or profit maximization. A lot of studies have been carried out using this approach, and it has been applied in process synthesis and design for chemicals-based processes. Grossmann and Daichendt (1996) have published reviews on suitable optimization techniques for process synthesis. These techniques are easy to apply for homogeneous systems like heat exchanger networks, distillation sequences, and reactor networks, but more difficult to use for heterogeneous systems like generation of total flowsheets. There are two distinct problems that limit the use of mathematical optimization techniques for heterogeneous systems: (1) generation of the superstructure of all possible alternatives; (2) solving the large optimization problem which is inherent in process synthesis. Various numerical solutions methods (Gupta and Ravindran, 1985; Westerlund et al., 1998; Duran and Grossmann, 1986) have been proposed to solve the resulting MINLP problem, but these algorithms are usually limited to moderately sized problems as large number of integer variables and nonlinear equations may prevent not only in finding the optimal solution but even in obtaining a feasible solution.

1.3. Hybrid methods Since applications of heuristic or knowledge based methods do not necessarily lead to optimal flowsheets, while mathematical techniques are limited by the availability and application range of the model and/or the superstructure, hybrid methods combine different approaches to solve the synthesis problem more effectively. These methods use the physical insights from the knowledge based methods to narrow the search space and decompose the synthesis problem into a collection of related but smaller mathematical problems. This allows for keeping the simple structure of knowledge-based approaches but replace the fixed rules with guidelines based on physical insights generated through analysis of the behavior of the chemicals. Jaksland et al. (1995) developed a hybrid method for the synthesis of separation processes based on thermodynamic insights. This method uses knowledge obtained based on the physical properties of the mixtures involved in the problem. The calculations of the indicators for each mixture provide the user with guidance to find the matching separation task. These indicators are ratios of physical properties, for example, a high difference in relative volatility is an indication that a separation by distillation is a feasible separation method. Similarly Hostrup et al. (2001) presented a framework based on thermodynamic insights and mathematical programming for three-component distillation system synthesis. The approach proposed in this paper can also be classified as a hybrid method for solving process–synthesis problems.

2. Overview of the process-group contribution framework The process-group concept introduced by d’Anterroches and Gani (2005) applies the principles of the group contribution approach (Marrero and Gani, 2001) used for chemical property estimation to the synthesis and design of chemical and bio chemical processes (Alvarado Morales et al., 2009; Bommareddy and Eden, 2011; Tula et al., 2014). In a group contribution method for estimating pure component/mixture properties of a molecule, the molecular identity is described by means of a set of functional groups of atoms bonded together to form a molecular structure. Once the molecular chemical structure is uniquely represented by the functional groups, the specific properties can be estimated from regressed contributions of the functional groups representing the molecule. Having the groups, their contributions and their interactions together with governing rules to combine the groups into a molecule, allows us to synthesize molecules and/or mixtures. This is known as CAMD, computer aided molecular design. Let us now imagine that each group used to represent a fraction of a molecule could also be used to represent a chemical process operation or a set of operations in a chemical process flowsheet. A functional process-group would represent either a unit operation (such as a reactor, a distillation column, or a flash), or a set of unit operations (such as, two distillation columns in extractive distillation, or pressure swing distillation). The bonds among the process-groups represent the streams connecting the unit operations, similar to the bonds combining (molecular) functional groups. In the same way as CAMD method (Harper and Gani, 2000) applies connectivity rules to combine the molecular functional groups to form feasible molecular structures, functional process-groups would have connectivity rules to combine process-groups to form structurally feasible process alternatives. Finally with flowsheet property model and corresponding process-group contributions it would be possible to predict various flowsheet properties which can be used as performance indicators for screening of alternatives. For example, in Fig. 1, a simple process flowsheet composed of a reactor, followed by a flash column, followed by distillation and membrane separation process, could be represented with process-groups. Consider the process flowsheet in Fig. 1. The feed streams to the reactor can be represented by two process-groups; one inlet process-group (iA) for reactant A, and an inlet process-group (iB) for reactant B. Similarly end products are represented by two outlet process-groups: (oC) and (oD). The reactor process-group (rAB/ABCD) representing the reactor has one inlet and one outlet. The process-groups representing a flash (fA/BCD) and a distillation (dCD/B) operation have one inlet and two outlets. Finally, the membrane separator is represented by a membrane processgroup (mC/D). From the list of process-groups available a feasible flowsheet structure can be created as shown in Fig. 1. As in group contribution based molecular property prediction (where the same molecular groups may be used to represent many molecules), same process-groups with different chemical species can also be used to represent different tasks in the flowsheet as long as the property of the task matches. This makes the process-groups property dependent but not chemical species dependent. Therefore, the use of the same process-groups to represent different chemical species having similar properties is also valid in the case of process flowsheets. Note, however, that the inlet and outlet streams (bonds) of processgroups must maintain the list of components present in them and that the path of a component through a process-group establishes the flowsheet structure. That is, process-groups (A/BC) and (B/C) can be connected to form [−(A/BC) − (B/C)−] without knowing the identities of the components A, B, and C. The identities of the chemicals (components) are only needed when the properties of the flowsheet need to be calculated.

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Fig. 1. Process-group representation of a chemical process flowsheet.

The process-group contribution framework (PGC), as shown in Fig. 2 is composed of seven main steps: the synthesis problem definition; the analysis of the problem; the selection of the matching process group building blocks; the generation of flowsheet alternatives; the ranking of the alternatives and selection of the most promising alternatives; the design of the selected alternatives; and the final verification.

2.1. Synthesis problem definition This step has two tasks: (i) definition of the raw materials (inputs) and desired products (outputs) of the process flowsheet and (ii) selection of the flowsheet property model. As only inputs and outputs materials are known for a new process synthesis problem, consequently only the terminal process groups, representing the inlet and outlet streams of the desired process, are known in the flowsheet structure that needs to be determined. Then the remaining task is to determine the correct sequence of unit process operations represented by process-groups that will produce the desired product matching the flowsheet property targets. On the other hand for retrofit problems where one seeks to improve upon an existing process flowsheet, task one includes definition of already interconnected unit operations together with the available raw materials inlets and desired product outlets.

2.2. Problem analysis This step is one of the most important of the total framework as the objective of this step is to generate the information required for solving the synthesis problem. Analysis of the process synthesis problem is performed to further define the problem through the use of knowledge bases and physical insight methods. The outcomes of the analysis include:

i. List of all chemical species in the synthesis problem, including reaction intermediates and/or any mass transfer agents. ii. Reactions, if needed, to convert the given raw materials to desired products. iii. List of all the possible process operation tasks.

This analysis is carried out through the following three tasks: (i) reaction analysis, (ii) mixture analysis and (iii) feasible separation task identification. In the reaction analysis task, the list of chemical species that should be formed by the reactions is identified by comparing the chemical species available as raw materials and products. Then for each chemical species identified, a database search is performed to find the reaction mechanisms yielding the identified compound. This approach is iterative as possible reactions may imply the need to provide reactants that are not currently specified. All new reactants or by-products from the matched reaction mechanisms are added to the synthesis problem. In the mixture analysis task the pure component and mixture property analysis is performed to obtain knowledge (data) that can be used for identification of feasible separation tasks in the next task of the problem analysis step. The pure component analysis is performed by retrieving a list of 22 pure component properties (see Table 1) from the ICAS database (Gani et al., 1997; Gani, 2014). For compounds missing data or for new compounds, the properties are calculated using ProPred (property prediction tool box) which is part of the Integrated Computer Aided System (ICAS) (Gani et al., 1997; Gani, 2014). The mixture property analysis is made in terms of the binary pairs of all the chemical species identified in the synthesis problem. For each binary component pair identified, analysis is performed to identify possible azeotropes, eutectic points or potential mass separation agents. Azeopro (Azeotrope analysis toolbox from ICAS) which is based on a hybrid approach of database search and calculations is used to identify azeotropes present in the system and also to provide potential solutions to separate them. The potential MSA

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Process synthesis Methods and Tools

Information flow S.1.Synthesis problem definition

CAPEC compound database, ICAS Reaction system database

S.2.Problem analysis S.2.1.Reaction analysis S.2.2.Mixture analysis S.2.3.Feasible separation task identification

ICAS pure components propeties database, Propred, Azeopro

Input compounds, output compounds, reaction data, flowrate, pressure, temperature. Collect information from step 1, user defined reactions List of all compounds to be separated. Computation of binary ratio matrix. Identification of azeotropes. Information from Step 1 and 2

S.3.Process-group selection Separation techniques identification method,Process groups database

S.4. Generation of flowsheets S.4.1 Superstructure generation S.4.2 Generation of SFILES

CAPEC combinato rial method, SFILES metho d

S.5.Ranking of flowsheets Flowsheet energy method

For each bin ary pair: Identification of all feasible separation techniques. Initialization of process groups . Initialized process-groups from step 3. System information from step 1: raw material , p roducts All feasible flowsheets. List of all flowsheets in th e form of SFILES strings. Information from previous steps Ranki ng of all feasible flowsheets based upon selected property model

S.6.Process design & Post analysis S.6.1 Mass balance S.6.2 Process design S.6.3 Post analysis

Reverse simul ation method, ECON, SFILES metho d, (ICAS-Mo T) for mass balance, Energy balance, LCSoft and SustainPro

S.7. Rigoruos simulation

Commercial simulator (Pro-2)

flo ws (pressure, temperature and individ ual flowrates) in all units and streams Unit operation design data ( number of stages, RR, feed stage) appended to SFILES string Design parameters, process conditions and flo w sheet structu re Initialization of rigourous simulation mod el

Fig. 2. Process-group contribution framework (PGC).

Table 1 Properties calculated for each pure component in the synthesis problem. Symbol

Pure component property

Mw ω Tc Pc Zc Vc Tb dm rg Tm Ttp Ptp Mv Hf Gf SIG Hfus Hcomb ı Vvw Avw Pnvap

Molecular weight (g/mol) Acentric factor Critical temperature (K) Critical pressure (bar) Critical compressibility factor Critical volume (m3 /kmol) Normal boiling point (K) Dipole moment (C m) Radius of gyration (nm) Melting point (K) Triple point temperature (K) Triple point pressure (Pa) Molar volume (m3 /kmol) Ideal gas heat of formation (kJ/kmol) Ideal gas Gibbs energy of formation (kJ/kmol) Ideal gas absolute entropy (kJ/(kmol K)) Heat of fusion at Tm (kJ/kmol) Standard net heat of combustion (MJ/kmol) Solubility parameter (kJ/m3 ) Van der Waals volume (m3 /kmol) Van der Waals area (m2 /kmol) Normal vapour pressure (Pa)

(mass transfer agents) are found by solubility analysis from the pure component property and from a list of commonly used solvents. In the third task feasible process operation tasks are identified using the physical insights based method for flowsheet synthesis developed by Jaksland et al. (1995). Jaksland’s method is based on the principle that every process operation task can be associated to one or more pure component property. According to the method, for a separation between two components and a given operation task, it is possible to assess if the operation task is feasible by comparing the ratio of the corresponding pure component properties. If the property ratio is greater than the threshold value corresponding to the feasibility of the separation task along with any additional property constraints required to be satisfied, the separation technique is considered as feasible. For example, a conventional distillation separation task (one feed and two products) is feasible if the following conditions are satisfied: the ratio of boiling points is greater than 1.02 and no azeotropes exist.

2.3. Process-group selection The selection and initialization of the process groups is based on the analysis of the synthesis problem carried out in step 2 of the

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Table 2 Initialization of a distillation process-group with a 5 components synthesis problem. Components in the synthesis problem Separation task Property dependence Separation technique Matching set of components Process-groups initialized

A, B, C, D, E B/C Boiling point Distillation separation between B and C 3 2 (BC) (ABC) (BCD) (dB/C) (dAB/C) (dB/CD)

framework. According to the process-group concept, the processgroups are component property dependent which means the same process-group can be used to represent the operation task with different sets of components as long as it satisfies the property constraints. This step of the framework involves two tasks: (i) selection and initialization of reaction process-groups and (ii) selection and initialization of separation process-groups. i. From the problem analysis step, the complete list of reactions along with chemical components involved, optional kinetic model parameters or conversion rates, are available. For each reaction (or set of reactions) if the kinetic model parameters are available, a kinetic model based reactor process-group is selected; otherwise a fixed conversion based reactor processgroup is selected. As the corresponding reaction process groups are selected, they are initialized with the components in the reaction and the reaction data available. ii. The separation process-groups are selected based on the identified separation task during the synthesis problem analysis. For each feasible separation task identified, the corresponding process-group is selected. The selected process-group can be initialized with different sets of components, if each set is matching the property dependence of the process-group. For example, consider a mixture of five components labeled as A, B, C, D, and E in the synthesis problem. Based on boiling point ratios, a feasible separation task is identified between components B and C. The corresponding separation technique associated with this set of properties is distillation and the process-group representing this separation technique is the distillation separation process-group. Therefore, the distillation process-group can be initialized with four different sets of components as shown in Table 2. This is based on the assumption of an ideal system, where no binary azeotropes exist, and assuming that the components are ordered according to decreasing relative volatility. Once the process-groups are initialized with all possible component combinations, they are added to the pool of process-groups to be used in the synthesis algorithm to generate the process alternatives. 2.4. Generation of flowsheets The objective in this step is to combine the process-groups selected in step 3 according to a set of connectivity rules and specifications to generate feasible alternative flowsheet structures. The main steps are: 2.4.1. Superstructure generation A combinatorial algorithm is employed to generate the superstructure of all flowsheet alternatives from the initialized process-groups. The combinatorial algorithm generates new flowsheet alternatives by combining process-groups according to a set of connectivity rules. The algorithm starts with processgroups representing the inlet streams as defined by the problem

4 (ABCD) (BCDE) (dAB/CD) (dB/CDE)

5 (ABCDE) (dAB/CDE)

definition step and keeps on adding the process groups whose inlet connection matches with the outlet connection of the selected process-group as represented in Fig. 3. Fig. 3a represents superstructure of feasible alternatives for a 4 component separation synthesis problem. Process flowsheets corresponding to different configurations as given by the superstructure can be represented using process as shown in Fig. 3b. The process-groups guarantee the recovery of the components in their outlets during the generation of the flowsheet structures. For instance, the process-group (A/BC) can be connected to the output of process-group (ABC/D) independently of the composition of the mixture of A, B, C and D entering the (ABC/D) process-group. In this case, the outputs of the process-group (ABC/D) are ensured to be on the one hand a mixture of A, B and C, and, on the other hand, a stream with a high purity and recovery of component D. However, if the number of chemical components present in the synthesis problem is high, there is high probability that the synthesis algorithm may encounter combinatorial explosion as the number of initialized process-groups increases exponentially. So in order to avoid such combinatorial explosion while combining processgroups, special rules are employed. The generated superstructure is systematically reduced to give a smaller set of feasible alternatives by checking successively against a set of logical decision rules. For example, if a two-phase system exists in the system then the first separation task will be a two-phase separator. Similarly, separation techniques that violate the feed condition and operational temperature limits as shown in Table 3 are also discarded. 2.4.2. Generation of SFILES Having a process flowsheet represented by process groups provides the possibility to employ simple notation systems for efficient storage of structural information of all the process alternatives generated. d’Anterroches (2006) introduced the SFILES (Simplified Flowsheet Input Line Entry System) method to store the structural information of process flowsheets. The SFILES method for flowsheets is similar to SMILES (Simplified Molecular Input Line Entry System) developed by Weininger (1988). SMILES are a form of line notations for describing the structure of chemical molecules using short ASCII strings. Fig. 4 gives the SFILES string of a simple flowsheet structure. SFILES string similar to SMILES is read from left to right. The process-groups representing specific tasks in the flowsheet are delimited by parenthesis, for example in Fig. 4 membrane Table 3 Rules with respect to feed conditions for different process-groups. Process-groups

Feed conditions

Gas membrane PG Liquid membrane PG Distillation PG Molecular sieve PG Crystallization PG Absorption PG PS distillation PG

V L V, L, VL L L V, L V, L, VL

Operating temperature <50 ◦ C <50 ◦ C <100 ◦ C >0 ◦ C

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A/B lmemA/B gmemA/B msA/B

(A) AB/CD AB/CD msAB/CD absAB/CD A/BC lmemA/BC absA/BC A/BC

ABC/D ABC/D gmemABC/D msABC/D

iABCD

B/C lmemB/C B/C msB/C

BC/D crsBC/D BC/D msBC/D

A/BCD crsA/BCD lmemA/BCD A/BCD

oA oB oC oD

C/D absC/D crsC/D C/D

Feasibility wrt Logical decisions Feasible Not feasible

(B)

(oA) (oA)

(oB)

AB

gmem

ABC (oB)

(C/D)

(iABCD)

(AB/CD)

(B/C)

(A/BC)

(iABCD)

(ABC/D)

(oC)

BC CD

(oD)

(oC) (oD)

Fig. 3. (a) Superstructure of feasible alternatives for 4-component separation synthesis problem. (b) Process-group representation of alternatives.

B A

iA iB (rAB/pABCD)

A/BC)

(gmemABC/D

B/C

oC

oD

• (iA)(rAB/pABCD)<1<2[<(iB)](gmemABC/D)[(oD)](A/BC)1(B/C)2(oC) Fig. 4. SFILES representation for a process alternative.

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separation task is represented by (gmemABC/D) in the given flowsheet SFILE. Two consecutive process-groups represent a connection from the first process-group to the second process-group, for example (iA)(rAB/pABCD) represents an inlet process-group connected to a reactor process-group. In SFILES notation branches are represented by square brackets and recycles using numbers similar to SMILES notation. The “smaller than” symbol is used to specify the direction of reading when it is not from left to right. In Fig. 4 SFILE notation “[<(iB)] “represent that process-group (iB) is connected as inlet to reactor process-group but not as an outlet. Recycles in the flowsheet are represented by numbers, one for each recycle present. For example, the number 1 in the above example indicates that one outlet of the distillation process.group (A/BC)1 is connected to the inlet of the reactor process-group (rAB/pABCD). Having all the alternatives in the form of SFILES strings will give computational edge to quickly construct the two-dimensional process flow diagram of a given process alternative and can become a universal way to exchange flowsheet data between process simulation and flowsheet synthesis tools.

2.5. Ranking of flowsheets Once all the feasible process alternatives are generated for the synthesis problem, ranking or benchmarking of the feasible alternatives is performed to select the most promising alternatives. Ranking of the alternatives can be done based on a single property, or by minimizing a weighted sum of objectives depending upon the property targets selected during the synthesis problem definition.

2.5.1. Flowsheet property models The main objective of the process flowsheet property is to describe the impact generated by the process as a sum of contributions of the unit operations which can be used as a performance indicator to benchmark the generated process alternatives. As a process-group represents a unit operation or set of unit operations, if a flowsheet property can be represented as a summation of process-group contributions, a flowsheet group contribution based property model can be derived as shown by Eq. (1).

f (P) =

NG 

posk ∗ ak

(1)

k=1

where f(P) is the flowsheet property function, NG: number of process groups, ak : regressed contribution of group k, and posk : topology factor.

2.5.1.1. Flowsheet energy consumption index property model. Energy consumption property model introduced by d’Anterroches and Gani (2005) predicts the energy consumption of a unit operation (in this case, for various process-groups involving distillation columns) given the corresponding process-groups employed, the driving force (related to the key compounds) and the process-group property contributions.



n=NG

Ex =

k=1

 (1 + p ) k

n=NG

Qk =

k=1

dijk

∗ ak

(2)

where Ex = energy consumption of the flowsheet (MkJ/h for Mmoles/h of feed); Qk = energy contribution of each processgroup; NG = number of process-groups; dk,ij = maximum driving force of the process-group k with respect to components i and j;

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ak = contribution of the process-group k and; pk = topology factor of process-group k. The topology factor is defined as pk =

nt 

¯i D

(3)

i=1

where nt is the number of separation tasks that should be performed before the task k in the ideal case i.e. separation tasks involving other binary pairs having higher driving force than the selected binary pair and Di the maximum driving force of task i. For example, in a separation of a ternary mixture into two products (oA) and (oBC) where the maximum driving force of A/B = 0.12 and B/C = 0.25, nt = 1 (since only 1 task should have been performed before that is separating BC mixture) and Di = 0.25 (the maximum driving force of this task). 2.5.1.2. Parameter estimation for energy index property model. In any group contribution method for estimating molecular properties, the contributions of the individual functional groups are regressed through fitting of the experimental data. Similarly contributions of individual process-groups to energy index model are regressed from simulation data involving columns separating different (from binary to 8-component) mixtures into different product specifications. For the data regression stage, since the identity of the compounds are known the driving force and the corresponding process-groups are also known. Consequently, the model parameter ak is estimated through data regression by matching the energy consumption (Ex ) obtained from simulation. The data used for regression covered a range of dk,ij between 0.0168 (lower limit) and 0.2199 (upper limit). For the list of regressed values refer S1 of supplementary material. 2.5.1.3. Calculation procedure. To use the energy index model, the following steps are needed: for a given separation process flowsheet, chemical compounds in the mixture and product specifications for the separation unit operation. i. Identify all distillation separation tasks from the process flowsheet. ii. For identified separation task, calculate the corresponding dk,ij for the separation task. iii. For the identified separation task, the product specifications and dk,ij , identify the corresponding process groups needed to represent the specified flowsheet. iv. Retrieve the process-group contributions from the parameter table and use Eq. (2) to estimate the energy consumption. 2.6. Process design and post analysis This step of the framework has three tasks: (i) the resolution of the mass balance through each process-group in the selected alternative, (ii) calculation of flowsheet design parameters of the process unit operations in the flowsheet structure through reverse simulation, and (iii) post analysis of the selected alternatives to further screen and find the best solution. 2.6.1. Resolution of mass balance The mass balances for the alternatives are performed through definition of each process-group present in the generated process alternative. Along with recoveries, the operating conditions, such as pressure and temperature of the outlet streams can be estimated from the process-group information. For instance, in the case of the distillation process-group, the recovery of the components lighter than the light key is equal to 100% in the overhead product and the recovery of the components heavier than the heavy key is equal to 100% in the bottom product. The recovery of the key components is

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greater than or equal to 99.5%. The resolution of the mass balance can be performed by using simple models like mixers, splitters and separators. The mass balance provides the definition (individual molar flow rates, pressure and temperature) of all the inlet and outlet streams of the process groups. 2.6.2. Process design The objective of this task is to calculate all the design parameters required to fully define the flowsheet for a possible rigorous simulation (for all the unit operations present in the flowsheet alternatives). For this the reverse simulation procedure for separation process-groups (such as distillation, extractive distillation, and flash), which is based on the driving force (DF) concept introduced by Bek-Pedersen and Gani (2004) is used. The procedure to determine the design parameters of the distillation columns in the simple distillation process groups is as follows: i. Given a NC component process group. ii. Order the components with respect to relative volatility and identify the key components. iii. Either from VLE calculation or experimental data retrieve the maximum driving force between the key components FDi|Max and the composition of the light key at its maximum Dx . iv. Based upon the process-group definition select the recoveries of the key components, 99.5% in this case. v. If the inlet composition is between the requested purities for the bottom and top product, the ideal number of stages Nideal for the column is retrieved from the pre-calculated values table published by Bek-Pedersen and Gani (2004). vi. Feed plate location of the column is calculated as NF = (1 − Dx )Nideal . Similarly, the reverse simulation for the kinetic model based reactor process-group is based on the attainable region theory introduced by Horn (1964). 2.6.3. Post analysis The objective of this task is to further analyze the selected process alternatives and find the most optimal alternative based on various indicators related to environmental impact, process safety and efficiency. The environmental impact of the selected process alternatives are computed using the WAR (waste reduction) algorithm introduced by Young et al. (2000). This algorithm defines six potential environmental impact indices that characterize the generation of potential impact within a process, and the output of potential impact from a process. Potential environmental impacts (Kalakul et al., 2014) are calculated using mass flow rates; stream composition calculated from the mass resolution task and pre-calculated potential environmental impact scores for each chemical. A comprehensive set of eight environmental impact indicators are calculated in this task:

Toluene Processing route

Benzene

Methane + Hydrogen Fig. 5. Structural definition of the synthesis problem.

atom efficiency, carbon footprint and energy consumption based on enthalpy balances across each process-group. 2.7. Rigorous simulation At this step of the methodology, all the necessary information to perform the final verification through rigorous simulation is available. Rigorous simulators like PROII (PROII, 2014), ICASSim (Gani et al., 1997; Gani, 2014) or any other process simulator can be used to further refine the most promising process flowsheets and to perform optimization of the design parameters in this step. 3. Case study: production of benzene from toluene and hydrogen Each step of the framework is highlighted through a case study involving the well-known production of benzene through hydrodealkylation of toluene. This case study has been selected to highlight the application of the framework with respect to the representation of a process flowsheet with process groups, and for generating new as well as existing flowsheet alternatives for producing benzene from toluene and hydrogen as raw materials. Also, since a number of alternative designs for this process are reported, it serves as a bench-mark against the new results. The numbering of the steps corresponds to the steps in the work flow of the framework presented in Fig. 2. 3.1. Synthesis problem definition The synthesis problem definition is to produce benzene from toluene and hydrogen. This toluene hydrodealkylation is highly exothermic and typical operating conditions are from 700 K to 850 K, and around 40 bar. The structural definition of the synthesis problem is as follows: 2 inlets of toluene and hydrogen, 1 outlet of benzene. As hydrogen is only available with methane impurities, the inlet definition for hydrogen stream also includes methane (dilute component) (Fig. 5). The flowsheet property specification is the energy consumption per kg of product produced by the process and the design objective (target) is to minimize this value. 3.2. Problem analysis

i. ii. iii. iv. v. vi. vii. viii.

HTPI: Human Toxicity Potential by Ingestion HTPE: Human Toxicity Potential by Exposure ATP: Aquatic Toxicity Potential TTP: Terrestrial Toxicity Potential GWP: Global Warming Potential ODP: Ozone Depletion Potential PCOP: Photochemical Oxidation Potential AP: Acidification Potential

The process safety indicator used to screen the alternatives in this framework is the process inherent safety index (Carvalho et al., 2008, 2013) which is based on temperature and pressure of the process. Other performance indicators that can also be used include

Next step is generating all the necessary information required to solve the synthesis problem. This is carried out in 3 tasks: (i) reaction analysis, (ii) mixture analysis and (iii) feasible separation task identification. 3.2.1. Reaction analysis The reaction analysis has confirmed the possibility to produce benzene by toluene hydrodealkylation (see Eq. (4)). The search in the CAPEC reaction database also confirmed the secondary reaction of producing biphenyl from benzene (see Eq. (5)). The reaction database provides conversion rates for the reactions. As the biphenyl is found to have financial value, the structural problem

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Table 4 Pure component properties in the synthesis problem. Refer to Table 1 for annotations. Property

Toluene

Hydrogen

Biphenyl

Methane

Benzene

Mw (g/mol) ω Tc (K) Pc (bar) Zc Vc (m3 /kmol) Tb (K) dm ×1 × 10−30 (C m) ˚ rg (A)

92 0.26 592 41 0.26 0.32 384 0.36 3.47 178.2 178 4.2E−07 1.1E−01 50170 122,200 321 6636 −3,734,000 18 6.0E−02 7.42E+08 0.38E+04

2 −0.22 33 13 0.31 0.06 20 0 0.37 14 14 7.1E−02 2.9E−02 0 0 131 117 −241,820 7 6.3E − 03 1.43E + 08 Very high

154 0.37 789 38 0.30 0.50 528 0 4.83 342 342 9.3E−04 1.6E−01 182,420 280,230 394 18,580 −6,031,700 19 9.2E−02 10.70E+08 Very low

16 0.01 191 45 0.29 0.10 112 0 1.12 91 91 1.2E−01 3.8E−02 −74,520 −50,490 186 941 −802,620 12 1.7E−02 2.88E+08 6180.23E+04

78 0.21 562 48 0.27 0.26 353 0 3.00 279 279 4.7E−02 8.9E−02 82,880 129,600 269 9866 −3,136,000 19 4.8E−02 6.00E+08 1.26E+04

Tm (K) Ttp (K) Ptp (Pa) Mv (m3 /kmol) Hf (kJ/kmol) Gf (kJ/kmol) SIG (kJ/kmol K) Hfus (kJ/kmol) Hcomb (kJ/kmol) √ ı ( kJ/m3 ) Vvw (m3 /kmol) Avw (m2 /kmol) Pnvap (Pa)

Table 5 Binary ratio matrix for a select set of properties. Binary pair Toluene Hydrogen Biphenyl Methane Benzene Hydrogen Biphenyl Methane Benzene Biphenyl Methane Benzene Methane Benzene

Tb

RG

Tm

Mv

SolPar

Vvw

Pnvap

3.13 2.93 1.19 1.06

4.93 7.83 5.09 2.63

18.82 25.90 4.73 3.16

12.78 24.54 3.77 3.07

2.46 3.02 2.11 1.45

56.67 158.67 19.74 10.48

1.8E+17 1.0E+03 1.6E+04 3.3E+00

25.90 5.48 17.32

13.03 3.01 8.10

24.54 6.50 19.98

5.44 1.33 3.13

2.90 1.74 2.82

14.51 2.70 7.66

1.9E+20 1.1E+13 5.4E+16

4.73 1.50

4.32 1.61

3.78 1.23

4.09 1.74

1.66 1.03

5.38 1.89

1.7E+07 3.4E+03

3.16

2.69

3.07

2.36

1.61

2.84

4.9E+03

Tb , normal boiling point; RG, radius of gyration, Tm , normal melting point; Mv , molar volume; SolPar, solubility parameter; Vvw , Van der Waal volume; Pnvap , vapor pressure.

definition of the synthesis problem is refined to include a second outlet for biphenyl recovery (Fig. 6). Hydrogen + Toluene  Methane + Benzene

(4)

2 Benzene  Hydrogen + Biphenyl

(5)

3.2.2. Mixture analysis The analysis of the pure component properties in the process design problem is performed after retrieving the 22 pure component properties of the chemical components from the ICAS property database. The pure components properties of all the compounds identified in the synthesis problem are listed in Table 4. The five compounds in the system forms 10 binary pairs. No binary azeotropes are detected in the azeotropic analysis carried out using ICAS tool box (Gani et al., 1997; Gani, 2014). The binary ratio matrix of pure component properties for all binary pairs is calculated and selected properties are shown in Table 5.

Toluene

Benzene

3.2.3. Feasible separation task identification Feasible process operation tasks to separate each of the binary pairs are identified using Jaksland et al. (1995) physical insights based method. Table 6 gives information on the feasible separation

Table 6 Separation techniques identified for each binary pair in the synthesis problem. Binary pair

Separation tasks identified

Hydrogen–Methane

Gas adsorption, flash, gas separation membranes, partial condensation Absorption, gas adsorption, flash, partial condensation Absorption, gas adsorption, flash, partial condensation Absorption, flash, partial condensation Gas adsorption, flash, partial condensation, stripping Gas adsorption, flash, partial condensation, stripping Flash, partial condensation Gas adsorption, crystallization, distillation, extractive distillation, liquid membrane separation, pervaporation, liquid adsorption Flash, liquid membrane separation, pervaporation, liquid adsorption Crystallization, flash, liquid membrane separation, pervaporation, liquid adsorption

Hydrogen–Benzene Hydrogen–Toluene Hydrogen–Biphenyl Methane–Benzene Methane–Toluene Methane–Biphenyl Benzene–Toluene

Processing route Methane + Hydrogen

Biphenyl

Benzene–Biphenyl Toluene–Biphenyl

Fig. 6. Updated structural definition of the synthesis problem.

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Table 7 Initialized process groups for the synthesis problem. Operation type

Gas membrane separation

Molecular-seive separation

Distillation

Crystallization

Liquid membrane separation

Inlet/outlet

Reactor

Process groups

gmemE/DCAB gmemD/CAB gmemC/AB gmemED/CAB gmemE/D gmemDC/AB gmemED/C gmemD/C gmemE/C gmemEDC/AB gmemE/DC

msE/DCAB msD/CAB msC/AB msE/D msED/C msDC/AB msED/CAB msD/C msE/DC msE/C msEDC/AB

ABCD/E ABC/D AB/C ABC/DE AB/CDE C/D AB/CD C/DE D/E C/E

crsE/C crsE/D crsE/CD crsEC/D crsC/D

lmemE/C lmemD/C lmemED/C lmemE/D lmemE/DC

iAB iD oC oE

rABD/ABCDE

tasks identified for each of the binary pairs present in the synthesis problem. 3.3. Process-group selection From the design problem analysis, all the needed information is available to represent the process flowsheet with process groups. For the list of available process-groups refer S2 of supplementary material. 3.3.1. Reactor PG’s The hydrodealkylation reaction of toluene in presence of hydrogen to produce benzene has been identified. The reaction is performed in a single reactor. From the problem analysis, the reaction, together with the conversion, is known. The reactor can be represented with a fixed conversion based reactor process group. The inlet stream of the process group is a mixture of toluene and hydrogen along with methane as impurity, and, the outlet stream is a mixture of unreacted toluene and hydrogen along with the benzene and biphenyl products. 3.3.2. Separation PG’s From the separation task identification analysis carried out in Section 3.3.2, all feasible separation tasks as applicable for the synthesis problem is identified as shown in Table 6. Using this information process groups representing the separation tasks are retrieved from the process-groups database. These selected process groups are initialized with all possible combinations of chemical species as explained in Section 2.3. Table 7 shows all the initialized process groups for production of Benzene from Toluene and Hydrogen. Along with separation and reactor process groups, separate process groups are initialized for two inlet streams representing, hydrogen along with methane and pure toluene streams. Two outlet process groups are also initialized for the benzene and biphenyl product streams, respectively. Since in the problem definition step methane is considered as impurity (dilute component), there is no separation task identified for separating methane and hydrogen. If methane is not considered dilute impurity then additionally one more process group (gmemA/B) is added to the list to separate hydrogen and methane and recycle back hydrogen to reactor.

Around 74,000 flowsheet combinations are available for the given synthesis problem, but after applying the combinatorial algorithm, where structurally infeasible alternatives are discarded, the number of process alternatives available was reduced to 272. These structurally feasible alternatives are further reduced by using the reduction algorithm based on logical rules. The statistics of the method are given in Table 8. Refer S3 of supplementary material for combinatorial algorithm. Fig. 7 represents all 272 structurally feasible flowsheets generated for production of benzene from toluene and hydrogen. The 32 process alternatives identified by the reduction algorithm are represented by blue arrows. The process alternatives will be 64 if we consider both the cases where in one case methane is considered as dilute impurity and in another case non dilute component.

3.4.2. Generation of SFILES All the identified process alternatives are converted into SFLIES, which stores the structural information of the alternatives. SFILES representations of all the 64 flowsheets corresponding to both the cases are listed in Table 9. Some of the designs generated using the group-contribution method can be found in literature. For example design 2 is proposed by Douglas (1985) and design 34 is proposed by Bouton and Luyben (2008), also 2 more designs resembling alternative 34 with minor modifications is proposed Konda et al. (2006).

3.5. Ranking flowsheets Table 10 provides the top five process alternatives ranked with respect to energy consumption of the process. Energy consumption for the alternatives is calculated using energy index flowsheet property model as described in Section 2.5.1. In addition to using the energy index, the minimum purity of both product streams is also estimated from the process-groups definition. The first two designs reported in Table 10 have same energy index. This is explained by the fact that the energy index Ex is only calculated for the distillation process groups, the other process groups do not contribute to the energy index (contribution equals to 0.0). For regression parameters for distillation energy index model refer S1 of supplementary material.

3.4. Flowsheet generation In this step firstly the superstructure of all feasible alternatives are generated and corresponding SFILES are generated for all the identified process alternatives. 3.4.1. Superstructure generation A combinatorial algorithm is used to generate a superstructure of all possible flowsheets from the 47 initialized process-groups.

Table 8 Computational statistics for the synthesis problem. Statistics Number of process groups Flow sheet combinations Structurally feasible combinations Process alternatives after reduction algorithm

47 74,046 272 32

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Table 9 SFILES of generated process alternatives. Sl. no.

SFILES

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

(iAB)(rAD/ABCDE)<1<2[(
Table 10 Top ranked process alternatives for the synthesis problem. Design no.

1 2 3 4 5

Initial screening

Post analysis

Energy index

Benzene purity

Biphenyl purity

Energy (M kJ/h)

Atom efficiency

Purity

Benzene (kmol/h)

0.0539 0.0539 0.0641 0.0670 0.0772

99.5 99.5 99.5 99.5 99.5

99.9 99.0 99.5 99.9 99.5

27.87 27.71 28.57 28.58 29.03

81.57 81.52 79.90 81.61 81.52

99.8 99.8 99.8 99.8 99.8

127.8 127.5 127.3 127.8 127.5

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ABCD/E gmemE/DCAB msE/DCAB ABCD/E

ABC/D gmem D/CAB ms D/CAB ABC/D AB/CD gmem DC/AB ms DC/AB AB/CD DC/E crsE/CD gmemE/DC msE/DC CD/E lmemE/DC CE/D crsEC/D DE/C gmemED/C msED/C C/DE lmemED/C

AB/CDE gmem EDC/AB ms EDC/AB AB/CDE

iAB ABCDE rABD/ABCDE iD

AB/C gmem C/AB ms C/AB AB/C C/D crsC/D gmemD/C msD/C C/D lmemD/C E/C crsE/C gmemE/C msE/C C/E lmemE/C

oAB oC oD oE

E/D crsE/D gmemE/D msE/D D/E lmemE/D

ABC/DE gmem ED/CAB ms ED/CAB ABC/DE

Fig. 7. Superstructure of structurally feasible flow sheets for HDA process.

3.6. Process design and post analysis The first task is to resolve the mass balance of the top ranked flowsheets in order to calculate the design parameters and to conduct post analysis. Using simple models the mass balance is resolved and the overall benzene production, purity and atom efficiency of each alternative is calculated. Along with mass resolution, as the composition across all process groups is now known, it is possible to estimate the energy required by each unit operation of the flowsheet and the corresponding energy requirements are shown in Table 10. Design parameters of the unit operations present in the process alternatives are calculated using the reverse simulation method as explained in Section 2.6.2. Table 11 gives the design data for the distillation columns present in the second process alternative from Table 9. Environmental impact generated by the process alternatives calculated by WAR (waste reduction algorithm) along with process safety factors are tabulated in Table 12. Since process safety factors are calculated based on maximum pressure and temperature

attained in the entire process and in all the process alternatives the same process-group (reactor process-group) has the maximum temperature and pressure, all the evaluated alternatives generated same process safety factors. 3.7. Rigorous simulation The process alternatives 1 and 2 are selected for rigorous simulation by means of a commercial process simulator (PROII). The first process alternative is a novel processing route for production of benzene through toluene hydrodealkylation, whereas the second alternative obtained through this method is similar to the processing route proposed by Douglas (1985). 3.7.1. Process alternative 1 In this alternative, the benzene production from toluene hydrodealkyaltion resembles the flowsheet proposed by Douglas (1985) except the last distillation column is replaced by a crystallization unit to separate biphenyl (Fig. 8). Using crystallization to

Table 11 Design of distillation column using driving force method. Distillation column design (driving force method)

Stabilizer

Benzene column

Toluene column

Given a NC component process group Order the components with respect to relative volatility Driving force between the key components FDimax , Di Recovery of light key Recovery of heavy key Nideal*1.5 Nf feed location

5 AB/CDE >0.75 0.999 0.999 5 2

3 C/DE 0.22, 0.4 0.995 0.995 31 19

2 D/E 0.6, 0.25 0.995 0.995 15 12

Table 12 Environmental impact and process safety factors for selected process alternatives. Process alternative

1 2 3 4 5

Environmental impact factors

Process safety

HTPI

HTPE

ATP

TTP

GWP

ODP

PCOP

AP

IT

IP

81.95 81.91 80.29 81.95 81.91

349.59 349.41 342.42 349.59 349.41

44.02 44.00 43.11 44.02 44.00

81.95 81.91 80.29 81.95 81.91

3.12 3.12 3.06 3.12 3.12

0 0 0 0 0

−3801.48 −3799.48 −3723.10 −3801.48 −3799.48

0 0 0 0 0

3 3 3 3 3

2 2 2 2 2

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8 Flue gas

13 2

13

9 Benzene

6 Hydrogen+Methane

Benzene

Hydrogen+Methane Distillation

Distillation 1 Toluene

4

3 Mixer

Flash system Crystallizer

Toluene Biphenyl

Reactor 7

12 Biphenyl

10

5

11 Toluene Fig. 8. Process flowsheet for generated alternative 1.

Temperature (K)

Solid Liquid Equilibrrium Curve for Toluene and Biphen p yl 340 330 320 310 300 290 280 270 260 250 240 230 220 210 200

SLE

0

0 0.2

0.4 0.6 Mole M fraction n of Biphenyl

0.8

1

Fig. 9. Solid liquid equilibrium curve for toluene and biphenyl.

recover biphenyl from HDA process has been reported and patented by Eugene (1968). Solid liquid equilibrium simulation data for Toluene and Biphenyl shown in Fig. 9 which is generated using ICAS (Gani et al., 1997; Gani, 2014) shows possibility of recovery of biphenyl. This information is used in the process simulator to determine the

recovery of Biphenyl. The mass balance results obtained from rigorous simulations are given in Table 13. 3.7.2. Process alternative 2 This processing route is same as the one proposed by Douglas (1985) for production of benzene from toluene (Fig. 10). In this

Table 13 Mass balance results for generated alternative 1. Stream (summary) Phase Total molar rate Temperature Pressure Component rates (kg mol/h) H2 Methane Toluene Benzene Biphenyl Component fractions (fraction) H2 Methane Toluene Benzene Biphenyl

UOM

S2

S1

S6

S9

S11

S12

kg mol/h K Atm

Vapor 299.8 298.0 40.0

Liquid 135.9 298.0 40.0

Vapor 1443.3 305.0 40.0

Liquid 126.6 353.3 1.0

Liquid 66.2 295.0 1.0

Solid 3.5 295.0 1.0

290.82 8.99 0.00 0.00 0.00

0.00 0.00 135.90 0.00 0.00

767.08 672.17 0.39 3.70 0.00

0.00 0.00 0.22 126.37 0.00

0.00 0.00 44.57 0.64 21.03

0.00 0.00 0.00 0.00 3.48

0.970 0.030 0.000 0.000 0.000

0.000 0.000 1.000 0.000 0.000

0.531 0.466 0.000 0.003 0.000

0.000 0.000 0.002 0.998 0.000

0.000 0.000 0.673 0.010 0.318

0.000 0.000 0.000 0.000 1.000

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Fig. 10. Process flowsheet for generated alternative 2.

Table 14 Mass balance results for benzene production by process alternative 2. Stream (summary) Phase Total molar rate Temperature Pressure Component rates (kg mol/h) H2 Methane Toluene Benzene Biphenyl Component fractions (fraction) H2 Methane Toluene Benzene Biphenyl

UOM

S2

S1

S6

S9

S11

S12

kg mol/h K Atm

Vapor 299.0 298.0 40.0

Liquid 135.9 298.0 40.0

Vapor 1448.2 305.0 40.0

Liquid 126.6 353.3 1.0

Vapor 45.1 383.7 1.0

Liquid 3.5 524.8 1.0

290.00 8.97 0.00 0.00 0.00

0.00 0.00 135.90 0.00 0.00

766.23 677.39 0.44 4.14 0.00

0.00 0.00 0.22 126.41 0.00

0.00 0.00 44.46 0.64 0.00

0.00 0.00 0.02 0.00 3.49

0.970 0.030 0.000 0.000 0.000

0.000 0.000 1.000 0.000 0.000

0.529 0.468 0.000 0.003 0.000

0.000 0.000 0.002 0.998 0.000

0.000 0.000 0.986 0.014 0.000

0.000 0.000 0.005 0.000 0.995

Table 15 Comparison of energy requirements and production rates for the two process alternatives.

Process alternative 1 Process alternative 2

Heating (M KJ/h)

Cooling (M kJ/h)

Benzene (kg mol/h)

Biphenyl (kg mol/h)

34.57 34.78

−43.67 −39.98

126.37 126.41

3.48 3.49

particular alternative, the downstream separation consists of 3 distillation columns to separate the unreacted raw materials and the products. The stream summary of this alternative obtained from rigorous simulation is given in Table 14. The first process alternative obtained from the framework has slightly improved heating energy efficiency with respect to the alternative proposed in the literature (Table 15). But due to the fact that biphenyl is removed using crystallization in the last separation step the cooling utilities increased. It is also observed that the compressor duty increased in alternative 1 as some of the biphenyl is recycled along with toluene. 4. Conclusions The main achievement of this work is the development of a generic framework to systematically solve the complex process synthesis and design problem, which facilitates more efficient and innovative solutions. The developed framework differs significantly from conventional synthesis-design methods as it is not iterative nor is it based solely on mathematical optimization techniques to synthesize an optimal solution.

Since the process alternatives are generated by combining the process groups and do not require resolution of the heat and mass balance at each synthesis step, numerous feasible process alternatives can be quickly generated for a given synthesis problem. Unfortunately, any enumeration technique suffers from combinatorial problems as the number of candidate constituents increases. Thus there is a need for fast, reliable and systematic screening methods capable of identifying and ranking the generated process alternatives. In this work, a combinatorial algorithm constituting connectivity and logical decision rules is used to identify the structural feasibility of the process alternatives and generate only feasible solutions. Apart from generating only feasible alternatives, the performance of the generated flowsheet alternatives is quickly tested through flowsheet property models which are estimated from previously regressed contributions of the process-groups involved in the process alternative. These flowsheet property models are truly predictive and component independent, in the sense that it can be applied to any component system as long as the property matches. Apart from energy consumption analysis, sustainability analysis in the form of evaluating environmental impact and process safety

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factors of process alternatives is carried out in the early stages of the process development to ensure the generated solution is not only an energy optimal solution but also sustainable/green solution. The successful application of this framework to benzene production from hydrodealkylation of toluene has demonstrated that synthesis of flowsheets through this group contribution approach quickly generates numerous process alternatives that are truly innovative and that have not been reported in the literature. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.compchemeng. 2015.04.019 References Alvarado Morales M, Terra J, Gernaey KV, Woodley JM, Gani R. Biorefining: computer aided tools for sustainable design and analysis of bioethanol production. Chem Eng Res Des 2009;87:1171–83. Barnicki SD, Siirola JJ. Process synthesis prospective. Comput Chem Eng 2004;28:441–6. Bek-Pedersen E, Gani R. Design and synthesis of distillation systems using a drivingforce-based approach. Chem Eng Process 2004;43:251–62. Bommareddy S, Eden MR, Gani R. Computer aided flowsheet design using group contribution methods. Comput Aided Chem Eng 2011;29:321–5. Bouton GR, Luyben WL. optimum economic design and control of a gas permeation membrane coupled with the hydrodealkylation (HDA) process. Ind Eng Chem Res 2008;47:1221–37. Carvalho A, Gani R, Matos HM. Design of sustainable chemical processes: systematic retrofit analysis generation and evaluation of alternatives. Process Saf Environ Prot 2008;86:328–46. Carvalho A, Matos HM, Gani R. SustainPro – a tool for systematic process analysis, generation and evaluation of sustainable design alternatives. Comput Chem Eng 2013;50:8–27. d’Anterroches L. (Ph.D. thesis) Group contribution based process flowsheet synthesis, design and modelling (Ph.D. thesis). Department of Chemical and Biochemical Engineering, Technical University of Denmark; 2006. d’Anterroches L, Gani R. Group contribution based process flowsheet synthesis, design and modelling. Fluid Phase Equilib 2005;228–229:141–6. Douglas JM. Hierarchical decision procedure for process synthesis. AIChE J 1985;31:353–62.

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Please cite this article in press as: Tula AK, et al. Process synthesis, design and analysis using a process-group contribution method. Computers and Chemical Engineering (2015), http://dx.doi.org/10.1016/j.compchemeng.2015.04.019