Sustainable process synthesis–intensification

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Sustainable process synthesis–intensification Deenesh K. Babi a , Johannes Holtbruegge b , Philip Lutze b , Andrzej Gorak b , John M. Woodley a , 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 FVT, Department of Chemical and Biochemical Engineering, Technical University of Dortmund (TU Dortmund), Emil-Figge-Str. 70, D-44227 Dortmund, Germany

a r t i c l e

i n f o

Article history: Received 16 December 2014 Received in revised form 9 April 2015 Accepted 23 April 2015 Available online xxx Keywords: Process synthesis Process design Sustainable design Sustainability Process intensification Systematic framework

a b s t r a c t Chemical industry is facing global challenges such as the need to find sustainable production processes. Process intensification as part of process synthesis has the potential to find truly innovative and more sustainable solutions. In this paper, a computer-aided, multi-level, multi-scale framework for synthesis, design and intensification of processes, for identifying more sustainable alternatives is presented. Within the framework, a three stage work-flow has been implemented where, in the first “synthesis” stage an optimal processing route is synthesized through a network superstructure optimization approach and related synthesis tools. In the second, “design” stage, the processing route from the first stage is further developed and a base case design is established and analyzed. In the third, “innovation” stage, more sustainable innovative solutions are determined. The application of the framework is illustrated through a case study related to the production of di-methyl carbonate, which is an important bulk chemical due to its multiplicity of uses. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction In the chemical industry improvements related to the use of sustainable technologies and efficient use of resources are needed in order to achieve reductions in energy consumption, waste generation, environmental impact and cost. Process improvements are typically achieved through an evolutionary approach, where knowledge gained from process understanding together with expert knowledge on process engineering is applied. The limitation with this approach, however, is that new, innovative and more sustainable process designs may not be found because the search space employed is limited in size in the trial and error, experiment-based approaches. The same is true for model-based solution approaches where the models employed have limited application range. The objective of process synthesis should be to find the best processing route, from among numerous alternatives, to convert given raw materials to specific (desired) products, subject to predefined performance criteria. Hence, process synthesis involves analysis of the problem to be solved, and, generation, evaluation and screening of process alternatives so that the best process option can be identified. Process synthesis is usually performed through

∗ Corresponding author. Tel.: +45 45 25 28 82; fax: +45 45 93 29 06. E-mail address: [email protected] (R. Gani).

the following three classes of methods: (1) Rule based heuristic methods, which are defined from process insights and know-how; (2) Mathematical programming based methods, where the best flowsheet alternative is determined from network superstructure optimization. This class of method is useful when the system is well defined and many combination of alternatives are to be considered; (3) Hybrid methods that uses process insights, know-how, rules and mathematical programming. That is, models are used to obtain good physical insights that aid in reducing the search space of alternatives so that the synthesis problem to be solved will involve less alternatives. Process intensification (PI) has been defined as the improvement of a process through the targeted enhancement of performancelimiting phenomena (Lutze et al., 2013) at different scales. At the plant/process scale the entire process is considered. At the unit operations scale the individual unit operations that comprise the process are considered. At the task scale the functions performed by the unit operations are considered. A task is defined as the function performed by a unit operation, for example, a flash vessel or a distillation column represent separation tasks. At the phase/phenomena scale the phenomena building blocks (see Section 2.3.1) that satisfy and thereby, make a task feasible are considered, while, at the molecular scale (Freund, Sundmacher, Ullman, Lutze et al., 2010) which is mainly considered for reactive systems, the molecular behavior of the molecules that affect the phenomena are

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considered. According to Van Gerven and Stankiewicz (2009), this enhancement can be achieved within four domains: process structure, energy, synergy and time. One example of PI at the process/plant level is a hybrid distillation scheme which is an external integration of two or more different unit operations that include at least one operation, that is, a conventional distillation column, in order to satisfy a separation task (Babi and Gani, 2014). Here, the integration of membrane separations with distillation to overcome thermodynamic boundaries such as azeotropes (Lutze and Gorak, 2013) could be considered. Divided wall columns are examples of PI at the unit operation, task or functional scales (Asprion and Kaibel, 2010; Halvorsen and Skogestad, 2011; Madenoor Ramapriya et al., 2014) while, membrane reactors (Assabumrungrat et al., 2003; Van Baelen et al., 2005; Inoue et al., 2007) or reactive distillation columns (Agreda et al., 1990; Shah et al., 2013; Holtbruegge et al., 2014) are examples of PI that improve the conversion in a reaction through the in situ removal of a product. Similar to process synthesis, in principle, PI could also be performed using the same three classes of methods. However, rulebased heuristic methods and the mathematical programing based methods have not been developed for the intensification of entire processes. For the design of specific hybrid/intensified unit operations within a process several methods (Bessling et al., 1997; Amte, 2011; Caballero and Grossmann, 2004) have been reported. For hybrid methods, a scheme for systematically achieving process intensification has been proposed by Lutze et al. (2013). Also, other hybrid schemes have been developed for intensifying specific parts of a process, for example, at the phenomena and molecular level (Peschel et al., 2012; Rong et al., 2008). Therefore, since process intensification aims at increasing the efficiency of processes, performing process synthesis and intensification together, should lead to improved and more sustainable process deigns/operations. Sustainable process synthesis–intensification, employed in this paper, is defined (Babi et al., 2014a) as the generation of alternative processing routes that show improvements related to economic factors, sustainability metrics and LCA factors. Sustainable process synthesis-design can be achieved through the use of different methods (Halim et al., 2011; Smith et al., 2014; Tieri et al., 2014) that operate at the unit operations scale. However, three limitations exist. First, the use of hybrid/intensified unit operations is not considered. Second, the opportunity to innovate through the potential generation of novel unit operations is not provided because of the scale at which the methods operate. This opportunity is possible at the task scale (Siirola, 1996; Agreda et al., 1990) and phenomena scale (Lutze et al., 2013; Babi et al., 2014b, 2014c). Third, a comprehensive analysis, that is, an economic, sustainability and LCA analysis, are not used together for identifying design targets through the identification of process hot-spots. A process hot-spot are limitations/bottlenecks associated with tasks that may be targeted for overall process improvement. Therefore, by performing process synthesis–intensification, these three limitations can be overcome in a systematic manner. In this paper a systematic, computer-aided, multi-stage, multiscale framework for sustainable process synthesis–intensification that leads to the identification of more sustainable process design alternatives is presented. The framework is summarized in Fig. 1. In stage 1, that is, the synthesis stage, the problem is defined in terms of an objective function, subject to process constraints and performance criteria. A processing route is either found from a literature survey or generated from the application of the means-ends analysis (Siirola, 1996), thermodynamic insights (Jaksland et al., 1995) or superstructure network optimization (Zondervan et al., 2011; Grossmann, 2012). In stage 2, that is, the design stage, a base case design is first established and then analyzed in terms of economic factors, sustainability metrics and LCA factors for identification of process hot-spots. These process hot-spots are

then translated into design targets that are to be satisfied if more sustainable alternatives are to be determined. In stage 3, that is, the innovation stage, desired tasks, phenomena, and the phenomena search space are identified (defined as design targets) and those desirable tasks and phenomena that may assist in overcoming the process hot-spots are identified. Process synthesis is applied using an integrated task-phenomena based approach in order to generate alternatives that achieve the design targets. Mutli-scale synthesis is possible because the base case design, in principle, can be decomposed from the unit operations scale to the task scale (Siirola, 1996) and phenomena scale (Lutze et al., 2013; Babi et al., 2014b). In the integrated task-phenomena based approach for process synthesis, phenomena are combined (rule-based) in such a manner that they perform a task or a set of tasks. These combinations of phenomena and/or tasks are then translated into unit operations using a knowledge-based, thereby leading more sustainable process designs or flowsheet alternatives. These designs are analyzed and compared to the base case design with respect to preselected performance criteria in order to determine the best, more sustainable process design. Therefore multi-level synthesis is performed in the following manner. In stage 1, synthesis and design is performed in order to identify a feasible processing route that can be used as a base case in stages 2 and 3. In stage 2, task based synthesis is performed where, a task or set of tasks representing the function of a unit operation are identified and analyzed for generation of intensified flowsheet alternatives (task based). In stage 3, phenomena based synthesis is performed where, process phenomena are identified, analyzed and combined to generate flowsheet alternatives that are more sustainable and constitute of hybrid/intensified unit operations. In this paper, the detailed architecture of the framework together with the main actions needed for successful application of each step of the work-flow is presented. An overview of the algorithms used in each step and the necessary methods and tools embedded within the framework are presented. The framework is applied to a case study of industrial importance, that is, the production of dimethyl-carbonate, where important features of the method of solution are highlighted. 2. Process synthesis–intensification: solution approach and definitions The process synthesis–intensification problem is defined as follows (Babi et al., 2014b): For the production of a specified product, generate more sustainable process designs. These alternatives may include well-known, existing and novel hybrid/intensified unit operations that provide improvements in terms of efficient use of raw materials, sustainability metrics (impacts) as well as LCA factors compared to a reference (base case) design. The mathematical description is given in Section 2.1, the solution approach in Section 2.2, the concept of performing process intensification at different scales in Section 2.3 and the criteria for sustainability and LCA are explained in Section 2.4. 2.1. Mathematical formulation of the process synthesis–intensification problem The problem definition for process synthesis–intensification is translated into a mathematical form: o o min/max fobj = fobj (X- , Y- , d- , z- , - )

(1)

subject to: g(X- , z- , - )

(2)

f (X- , Y- , d- , z- , - ) = 0

(3)

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Stage 3 Innovation Design

Sustainable design

3

Flowsheet: Well-known + Hybrid/ Intensified + Innovative unit operations More Sustainable Designs Stage 1 + Stage 2 + Stage 3

Stage 2 Design (& Analysis)

Feasible design

Flowsheet: Well-known + Hybrid/ Intensified unit operations

Process Synthesis-Design Stage 1 + Stage 2

Search Space of Unit Operations

Stage 1 Synthesis

Knowledge-base Computer-aided

Tools

Methods

Models

Unit operations scale Task scale Phenomena scale

Fig. 1. Framework for more sustainable design through process intensification.

bL ≤ b1 X- + b2 Y- ≤ bU

(4)

2.2. Solution approach: decomposition based solution strategy

hL ≤ h(X- , Y- , d- , - ) ≤ hU

(5)

vL ≤ v(X- , Y- , d- , - ) ≤ vU

(6)

wL ≤ w(X- , Y- , d- , - ) ≤ wU

(7)

0 Y- = , X- ≥ 0 1

(8)

In order to manage the complexity related to the solution of the MINLP synthesis-intensification problem, an efficient and systematic solution approach is needed. One approach (Karunanithi et al., 2005), decomposes the problem into a set of sub-problems that are solved according to a pre-defined calculation order. Most of the sub-problems require bounded solution of a sub-set of equations and the final sub-problem is solved as a set of NLP or MILP. Therefore flowsheet alternatives are generated by simultaneously solving the process model equations, Eqs. (2) and (3), subject to the constraints defined in Eqs. (4) and (5). The objective function defined by Eq. (1) is calculated and ordered for the remaining feasible flowsheet alternatives. The generated alternatives are then assessed using a set of PI performance criteria specified in Eq. (7). The performance criteria are related to the performance and improvement in economic, sustainability and LCA factors, of the whole or part of the process through the application of hybrid/intensified unit operations (Lutze et al., 2012; Babi et al., 2014b). The flowsheet alternative(s) that give the best objective function value are selected as the more sustainable process designs. Note that a direct solution of the MINLP problem is also possible (Zondervan et al., 2011; Quaglia et al., 2014).

The objective function (Eq. (1)) can be linear or non-linear, is dependent on a set of, design/optimization variables X- , binary (0,1) decision integer variables Y- , equipment (unit operations) parameters d- , thermodynamic variables z- , and process and product specifications - . Eq. (1) represents the objective function to be minimized or maximized subject to a set of linear and non-linear constraints (Eqs. (2)–(8)). Eqs. (2) and (3) represent a system of linear and non-linear equations (constraints), representing the process models. Here, the process models are considered at steady state conditions only, consisting of the phenomena as well as mass and energy balances. Eqs. (4) and (5) represent the flowsheet physical constraints and equipment design specifications, for example, the process flowsheet structure and equipment boundaries, respectively. Eqs. (6) and (7) represents PI constraints, that is, intensification design specifications and performance criteria that the feasible flowsheet alternatives must satisfy, for example, the inclusion of intensified (mature/novel) equipment within the search space of available unit operations and the improvement of sustainability/LCA factors, respectively. The process synthesis–intensification problem to be solved becomes a mixed integer non-linear programming (MINLP) problem because, as seen from Eqs. (1)–(7), the objective function and constraints can be linear and non-linear and binary decisions must be made in selection between different phenomena/tasks/equipment for the generation of feasible flowsheet alternatives (Papoulias and Grossmann, 1983; Quaglia et al., 2012; Lutze et al., 2013).

2.3. Phenomena-based synthesis and a comparison to CAMD Phenomena-based synthesis is defined as the generation of more sustainable designs from the combination of phenomena building blocks (PBBs) at the lowest scale (phenomena) that perform a task at the higher scale (task). Therefore, in performing phenomena-based synthesis, PBBs are combined to form simultaneous phenomena building blocks (SPBs), that are combined to form basic structures that perform a task or set of tasks, using pre-defined rules. These basic structures are then translated into unit operations (highest scale) that constitute the final flowsheet

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Fig. 2. A comparison of phenomena-based synthesis to CAMD.

alternatives. The combination of PBBs to generate basic structures is rule based and analogous to CAMD (Harper and Gani, 2000) where atoms are combined to generate functional groups that are combined to form molecules with a set of desired properties (performance criteria). The comparison of phenomena-based synthesis to CAMD is shown in Fig. 2. 2.3.1. Phenomena building blocks and simultaneous phenomena building blocks A phenomena building block (PBB) is considered in this work as the smallest unit that performs a task in a process. For example, a mixing PBB performs a mixing task. A simultaneous phenomena building block (SPB) is defined as the combination of one or more phenomena building blocks using predefined combination rules. Most chemical processes can be represented by different combinations of mass, energy and momentum transfer phenomena (Lutze et al., 2013) such as mixing (M), two-phase mixing (2phM), heating (H), cooling (C), reaction (R), phase contact (PC), phase transition (PT) phase separation (PS) and dividing (D). A “dividing” phenomena divides a stream into one or more streams. Each PBB contributes to mass and energy balances that are solved for the system boundary of the SPB. The inlet/outlet stream states of the PBBs are liquid (L), vapor (V), solid(S) and/or their combinations, for example, vapor and liquid (VL), liquid–liquid (LL), vapor–liquid–liquid (VLL), and solid–liquid (SL). It should be noted that all possible combinations of SPBs are obtained from the combinations of the 9 PPBs listed above. The 9 individual phenomena building blocks are used in the generation of feasible SPBs using the following rules: • M – If separation or reaction is occurring, mixing of the compounds in the separating mixture and/or mixing of the reactants occur therefore, a “M” PBB is required; • R – If a reaction is occurring, raw materials are converted to products, therefore, a “R” PBB is required; • 2phM – If separation or reaction is occurring in a two phase system, mixing of the two phases occur therefore, a “2phM” PBB is required;

• PC – If two phases are present then, contact of between the two phases occur therefore, a “PC” PBB is required; • PT – If two phases are present then transition from one phase to the other occur, for example, consider a vapor-liquid system where liquid “transitions” into vapor due to heating and vapor “transitions” into liquid due to cooling. When this occurs a “PT” PBB is required; • PS – If two phases are present then separation of the two phases occur therefore, a “PS” PBB is required; • H/C – If a single phase or multiple phases are present and there are changes in enthalpy due to internal and/or external energy sources then a “H” or “C” PBB is required; • D – If the dividing of streams is needed then a “D” PBB is required. Fig. 3(a)–(c) highlights the representation of three different unit operations in terms of SPBs that are formed by combination of PBBs. This is explained as follows: • Flash vessel (phases: vapor and liquid) – In the flash vessel the following are occurring simultaneously, mixing plus two phase mixing, phase contact between vapor and liquid, transition from one phase into another and the separation of two phases. Therefore, the following PBBs are required in order to generate feasible SPBs that represent the flash vessel, M, 2phM, PC(VL), PT(VL), PS(VL) as highlighted in Fig. 3(a); • Distillation column (phases: vapor and liquid) – In the distillation column, the same PBBs as the flash vessel are selected plus heating and cooling PBBs for properly representing the condenser and reboiler. The following PBBs are required in order to generate feasible SPBs that represent the distillation column, M, 2phM, PC(VL), PT(VL), PS(VL), H, C as highlighted in Fig. 3(b); • Reactive distillation (phases: vapor and liquid) – In the reactive distillation column, the same PBBs as the distillation column are selected plus reaction because reaction and separation are also occurring simultaneously. The following PBBs are required in order to generate feasible SPBs that represent the reactive distillation column, M, 2phM, PC(VL), PT(VL), PS(VL), H, C, R as highlighted in Fig. 3(c).

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Fig. 3. Basic structures representing three unit operations (a) a flash column, (b) distillation and (c) a single feed reactive distillation column with reaction only.

2.3.2. Basic structures A basic structure is defined as the combination of multiple SPBs using predefined combination rules (Babi et al., 2014b). A basic structure performs a targeted or set of targeted tasks and are represented by SPBs, which are classified as initiator, intermediate and terminator (for more details, see Table S1 in the supplementary material). An initiator SPB is one that fulfills the main objective of a task but may not fulfill the entire task. A terminator SPB represents the final task to be performed in an operation. An initiator SPB, when repeated more than once to represent an operation is classified as an intermediate SPB. These intermediate SPBs can be repeated multiple times to complete the tasks of an operation. A basic structure can then be expanded in order to fulfill a task. When a basic structure is expanded and fulfills a task, it is referred to as the completion of the desired (needed) operation which is then translated into a unit operation (see Fig. 3). The number of times an intermediate SPBs can be repeated is determined by using the extended Kremser method (Lutze et al., 2013), for example, the number of trays of a distillation column is equivalent to the number of repeated intermediate SPBs. For a flash vessel the basic structure is also an operation and as it performs (see Fig. 3(a)) the desired separation task. However, for the distillation column and reactive distillation column, intermediate SPBs must be added until

the process specifications (such as product purity, recovery, etc.) are matched (see Fig. 3(a)–(c)). 2.4. Criteria for evaluation In order to evaluate a more sustainable design compared to the base case design for identifying non-tradeoff designs, different performance criteria related to economic, sustainability/LCA factors are applied (Eq. (7)). These are categorized as follows: 1. Economic – Cost related, for example: a. Utility cost b. Operational cost c. Total annualized cost d. Profit 2. Sustainability metrics/LCA factors – Environmental related, for example a. Carbon footprint b. Environmental impacts (Kalakul et al., 2014): i. HTPI – Human Toxicity Potential by Ingestion ii. HTPE – Human Toxicity Potential by Exposure iii. GWP – Global Warming Potential

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iv. HTC – Human toxicity (carcinogenic impacts) v. HTNC – Human toxicity (non-carcinogenic impacts) 3. Sustainable process synthesis–intensification framework The detailed workflow for the systematic, computer-aided, multi-stage, multi-scale framework for sustainable process synthesis–intensification together with the different computeraided tools utilized by the framework is shown in Fig. 4. The framework is based on the proposal of Lutze et al. (2013) where the concept of phenomena based synthesis was proposed for intensification of an entire process. The ideas of Lutze et al. (2013) have been extended and briefly described by Babi et al. (2014a, 2014b, 2014c). In this paper, a detailed and full version of the multiscale framework is presented. The framework consists of 8 steps that operate at the unit operation-task scales and, 4 integrated task-phenomena-based synthesis (IT-PBS) steps that operate at the task-phenomena scales. According to the work-flow (see Fig. 4), in each step an objective must be accomplished in order to proceed to the next step. The information from a previous step is used as the input to the next step. Note, however, additional data/information may be needed as input to a step. To achieve the objective of each step, the user must perform a series of actions. Each step of the work-flow is presented together with a list of associated methods and tools. 3.1. Step 1 – problem definition Objective: To obtain production and cost information about the raw materials and products. Action 1.1 – Perform a literature/online search to find the main uses of the product (or products) to be produced. Action 1.2 – Perform a literature/online search in order to, estimate the annual production of the product. Note – The main uses of the product provide the information needed to establish the motivation for determining more sustainable flowsheet designs for production of this product. 3.2. Step 2 – problem (and Fobj ) definition Objective: To define the mathematical problem in terms of objective function and constraints. Action 2.1 – Define the problem, that is, whether a new process or an existing process retrofit is to be investigated. Action 2.2 – Define the objective function (Eq. (1)) to be maximized or minimized, for example, profit, utility cost, operational cost or total annualized cost. Action 2.3 – Define the constraints. The constraints are of four types, logical constraints (1 ), structural constraints (2 ), operational constraints (3 ) and performance criteria (ϕ). An example of each constraint is as follows:

The objective function is one of the means by which the generated flowsheet alternatives are quantitatively compared. 3.3. Step 3 – reaction identification/selection Objective: (1) To select the reaction pathway, raw materials state and catalyst; and (2) To determine the reaction type, that is, exothermic or endothermic. Action 3.1 – Perform a literature-search to find a feasible reaction pathway for producing the desired product. If more than one reaction pathway is found then a reaction pathway analysis is recommended, for example, that proposed by Kongpanna et al. (2014). If available, retrieve reaction equilibrium data (for example, equilibrium data), catalyst information (homogenous or heterogeneous, selectivity, deactivation, etc.) and reaction kinetics, for the selected reaction pathway. If no reaction information can be obtained then reaction path synthesis for identification of a feasible set of reaction paths, of which, one must be selected, can be performed (Voll and Marquardt, 2012). Action 3.2 – Identify the phase(s) of the reaction, that is, the phase(s) in which the reaction takes place. Determine the heat of reaction, identify if the reaction is reversible or irreversible, and, identify the reaction type, that is, exothermic or endothermic. The heat of reaction is determined from the reaction data and the heats of formation HFi , of the reactants and products. Note – The reaction information also provides important additional information – number of phases and phase types as well as information about the catalyst(s) that are used. These data are needed for rigorous simulation and modeling in steps 7–8 and ITPBS.3, respectively. 3.4. Step 4 – check for availability of the base case design Objective: To select a base case design (process route) based on a literature survey or on the process information on stored in a PI knowledge-base. Note, if a base case design has been user-specified, proceed to step 5. Action 4.1 – Perform a literature-search to check if an available design uses the reaction pathway selected in step 3. If yes, then it is pre-selected as a base case design and one proceeds to step 5, else, proceed to step 6. Note – If a base case design is available then the design needs to be verified (see step 5). However, if a base case design is not available then synthesis, design and innovation design is performed to generate one (see steps 6–7). 3.5. Step 5 – check for base case feasibility Objective: To verify the feasibility of the base case design obtained in step 4. Action 5.1 – Apply the process synthesis method of Douglas (1985). If the base case design does not pass the verification test, then generate a new design (go to step 6). Note – To pass the verification test, the pre-selected design must satisfy the rules given in process synthesis method of Douglas (1985) with respect to the inputs/outputs of the process, the separation tasks structure and the energy structure. If the design passes the verification test, it is listed as the base case (reference) design.

1. 1 – The product (and by-product) purity; 2. 2 – PBBs are connected to form SPBs based on combination rules; 3. 3 – Raw materials are assumed to be in their pure state except if otherwise defined; 4. ϕ – Sustainability and LCA factors must be the same or better. -

3.6. Step 6 – generate a base case design

The full list of the constraints mentioned above is given as supplementary material in Table S2. Note – The problem statement provides the scope of the synthesis, design and more sustainable design problem, related to the design of a new process or the retrofitting of an existing process.

Objective: To generate a processing route to be used as a reference (base case) design for the production of the product from the raw materials. Action 6.1 – Generate a processing route using one of the following three methods (or a combination of them): network

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Framework Work-flow & Architecture

Part II-Innovative Design Stage 3

Part I-Synthesis and Design Stage 1 and Stage 2

C o m p l e x i t y

Computer-Aided Tools ASPEN/ PROII

Database

Integrated Task-Phenomena based Synthesis Step 7

Perform Rigourous simulation

IT-PBS.1

Step 6

Generate a base case design

No

Step 5

Base Case Feasibility?

No

Step 2

Problem Definition

MoT

Process Analysis

ECON SustainPro LCSoft

IT-PBS.3

Step 8

IT-PBS.2

Economic, Sustainability & LCA Analysis

Identification of desirable tasks & phenomena

Generation of feasible flowsheet alternatives

IT-PBS.4

ECON LCSoft

Perform EconomicSustainability analysis

Selection of best flowsheet

Y es

Step 3

Step 4

Reaction identification/ selection

Availability-Base Case?

Final flowsheet inclusive of PI

Step 1

Need Identification

Data/Information Fig. 4. Framework workflow and architecture for sustainable process synthesis–intensification.

superstructure optimization (Grossmann, 2012; Quaglia et al., 2014), the means-ends analysis (Siirola, 1996) and thermodynamic insights (Jaksland et al., 1995). Identify feasible tasks for reaction and separation based on pure compound and mixture property analyses (thermodynamic insights). From these results, unit operations that fulfill the identified tasks are identified (Jaksland et al., 1995). Note – The base case (processing route) generated from this step does not include the use of hybrid/intensified unit operations, but only well-known unit operations. 3.7. Step 7 – perform rigorous simulation Objective: To rigorously simulate the base case (reference) design. Note – The rigorous simulation of the base case (reference) process design provides the data (properties of all streams in the flowsheet obtained from mass and energy balance) needed to perform the economic, sustainability and LCA analyses. The data needed to set-up a rigorous simulation are, number of unit operations, number of streams, stream properties, number of compounds, number of reactions, and unit operations specific data, for example, the number of trays, reflux ratio and product recovery.

Action 8.3 – Perform a LCA analysis. A LCA analysis based on an indicator based method using a cradle to the gate concept and implemented in a computer-aided tool, LCSoft (Kalakul et al., 2014), is recommended. Retrieve the carbon footprint (CO2 footprint) and environmental impact for each unit operation. Action 8.4 – Translate the indicator values from the analyses into targets process hot-spots elimination using Table 1. Action 8.5 – Translate the process hot-spots into design targets using Table 2. Note – The economic analysis provides an indicator related to the distribution of utility costs and operating costs. The sustainability analysis provides information on where in the process economic value is being lost, for example, raw material loss in a waste stream. The LCA analysis provides environmental indicators related to how sustainable the process is with respect to the environment, for example, through the calculation of the carbon footprint. The method and tools used for these analyses are explained in Section 4. The results from the economic, sustainability and LCA analyses are translated into process hot-spots, from which the most sensitive are selected as design targets. Matching of these design targets minimizes/eliminates the process hot-spots and therefore, generates non-tradeoff process designs. 3.9. IT-PBS.1 – process analysis

3.8. Step 8 – economic, sustainability and LCA analysis Objective: To identify process hot-spots as targets for sustainable design through rigorous; economic, sustainability and LCA analyses. Action 8.1 – Preform an economic analysis. Models available in Peters et al. (2003) were used (supplemented by Biegler et al., 1997). The software, ECON (Kalakul et al., 2014), which has these models has been used. Retrieve the utility costs and the capital costs for each unit operation. Action 8.2 – Perform a sustainability analysis (see Section 4.2). A sustainability analysis based on the method of Carvalho et al. (2009) and implemented in a computer-aided tool, SustianPro (Carvalho et al., 2013), is recommended.

Objective: Translate the base case flowsheet into tasks, PBBs and the PBBs associated with each hot-spot. Action IT-PBS.1.1 – Translate the base case flowsheet into a taskbased flowsheet by applying Algorithm I.1 (see Section 4.1.1). Action IT-PBS.1.2 – Translate the task-based flowsheet into phenomena based flowsheet using Algorithm I.2 (see Section 4.1.1) and store the identified PBBs. Action IT-PBS.1.3 – Retrieve pure compound data (see Table 14) from any appropriate properties database, for example the CAPEC database (Gani et al., 1997) and perform a mixture analysis (Jaksland et al., 1995): (1) analysis of pure compound properties using a binary ratio matrix, (2) azeotropic analysis including its pressure dependency and (3) miscibility analysis.

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Table 1 List of established relations between indicator values and the process hot-spots. Indicator values

Base case property

Cause

Identified process hot-spot

˛1 , raw material recycle/cost ˇ1 -MVA

Un-reacted raw materials

Equilibrium reaction

˛2 -utility cost ˇ2 -EWC  1 -CO2 equivalent ˛2 -utility cost ˇ2 -EWC  1 -CO2 equivalent ˛2 -utility cost ˛3 -capital cost

Hrxn < 0 reactor cooling

Exothermic reaction

-Activation problems -Limiting equilibrium/raw material loss -Contact problems of raw materials/limited mass transfer -Limited heat transfer -Highly exothermic reaction

Hrxn < 0 reactor heating

Endothermic reaction

-Highly endothermic reaction

Reactor operating conditions

˛4 -product sale  2 -PEI

Formation of by-product(s)

-Explosive mixture -Product degradation by temperature -Formation of undesired side-products

˛2 -utility cost ˇ1 -MVA ˇ2 -EWC  1 -CO2 equivalent  2 -PEI

Un-reacted raw materials and products recovery

Temperature and pressure operating window for the reactor NOP, number of desired products plus number of undesired products -Presence of azeotrope(s) -High energy usage-heating/cooling

-Azeotrope -Difficult separation due to low driving force -High energy consumption/demand

Note: Economic (˛ ) and LCA ( ) analysis, MVA – mass value added, EWC – Energy to waste cost, CO2 equivalent – carbon footprint, PEI – potential - ), sustainability (ˇ environmental impact, NOP – number of products.

The binary ratio matrix (Jaksland et al., 1995) is calculated as the ratio between pure compound properties for all binary pairs formed by the mixture compounds. The number of binary pairs, NB, is given by NB = NC(NC − 1)/2, where NC is the number of compounds in the mixture. The binary ratio, rij , for a binary pair is calculated as, rij = pAi /pBj , where pkij is the pure compound property i of compound k in mixture j; A and B indicate the compounds in the binary pair. The azeotrope analysis is performed by investigating the effect of pressure on the change in the azeotropic composition and temperature. A miscibility analysis is performed through liquid miscibility (stability of the liquid phase) calculations. This would indicate the presence of more than one liquid phase as well as mutual solubilities of the mixture compounds. Note – The base case flowsheet is transformed from higher scales (unit operations scale) to lower scale (task and phenomena scale). Through analysis of the stream (mixture) and unit operation, a set of feasible tasks and phenomena are identified.

3.10. IT-PBS.2 – identification of desirable tasks and phenomena Objective: To identify desirable tasks and PBBs for overcoming the identified process hot-spots. Action IT-PBS.2.1 – Identify the tasks and their corresponding PBBs needed to overcome the hot-spots by applying Algorithm I.3. Add to this list, the PBBs identified in IT-PBS.I.2 (see Section 4.1.1), defining thereby, the PBB-based search space before screening of alternatives. Action IT-PBS.2.2 – Reduce the identified PBB based search space using the structural constraints 2 and performance criteria ϕ defined in step 2. For example, if the use of a mass separating agent to minimize waste is not to be considered, then 2 is adjusted to reflect this constraint. That is, PBBs that require the use/addition of a mass separating agent (for example PT(LL)) is removed.

Action IT-PBS.2.3 – Identify the operating window of each identified PBB using the relationships given in Table 3. Through analysis of the properties (for example, boiling point, melting point, reaction pressure, etc.), the feasible range of the operating variables (for example, temperature, pressure) are established. Note – The identified desirable tasks are translated into PBBs and added to the initial list of PBBs from IT-PBS.1. A desirable task is defined as a task that if performed has the potential to minimize/eliminate a process hot-spot. New PBBs may now be included. The properties involved with each PBB helps to define the operational boundary for each PBB with respect to temperature and/or pressure. Properties such as pH, viscosities, etc. are not considered at this point. 3.11. IT-PBS.3 – generation of feasible flowsheet alternatives Objective: To generate feasible intensified flowsheet alternatives using an integrated, task-phenomena-based approach. Action IT-PBS.3.1 – Generate feasible SPBs by applying Algorithm II.1 (see Section 4.1.2). Action IT-PBS.3.2 – Generate a task-based superstructure of alternatives by applying Algorithm II.2 (see Section 4.1.2). Action IT-PBS.3.3 – Identify and verify feasible tasks from the task-based superstructure that must be performed by applying Algorithm II.3 (see Section 4.1.2). Action IT-PBS.3.4 – Identify the basic structures that perform a desired task by applying Algorithm II.4 (see Section 4.1.2). An excerpt of the generated basic structures is given as supplementary material (see Table S3). Action IT-PBS.3.5 – Identify the task-based flowsheets from the basic structures by applying Algorithm II.5. Action IT-PBS.3.6 – Translate the task-based flowsheets into process flowsheet alternatives at the unit operations scale by applying Algorithm II.6. Action IT-PBS.3.7 – Perform a model based analysis of the generated flowsheet alternatives to analyze/understand the performance

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Design (Intensification) targets

Activation problems

Limiting equilibrium

Contact problems of raw materials/limited mass transfer

Limited heat transfer

*

*

*

*

* * *

* * * * *

* * * *

*

Highly exothermic reaction

*

Highly endothermic reaction

Explosive mixture

Degradation by temperature

*

* *

*

*

*

* *

Formation of undesired sideproducts

Azeotrope

Difficult separation due to low driving force

High energy consumption/ demand

*

*

*

* * *

* * *

* * *

* * *

*

* * *

* * *

* * *

*

*

*

* *

* *

* *

* *

* *

*

* *

* *

* *

* * * * *

* * * * *

* * * * *

* * * * *

* * * * *

* * * * *

* * * * *

* * * * *

* * * * *

* * * * *

* * * * *

* * * * *

Note: “*” represents the corresponding design target for a given process hot-spot.

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Increase raw material conversion Reaction-change in catalyst Reaction-use of a solvent Reaction-new Reaction-mixing Reduce raw material loss Reduce product loss Reduce energy consumption Reduce utility cost Improvements in LCA/Sustainability indicators Unit operations reduction Product purity Production target Reduce operational cost Waste minimization

Process hot-spots D.K. Babi et al. / Computers and Chemical Engineering xxx (2015) xxx–xxx

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Table 2 Translation of process hot-spots into design targets.

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Table 3 List of relationships between tasks, PBB, operating variables and properties. Task

PBB

Operating variables

Properties to be checked

Example

Reaction (single phase) Concentrations below the dew point line

R

T, P

TB , TM , Tazeotrope

Single phase is liquid-P-Reaction pressure (reported in literature) T – lowest boiling compound or azeotrope T – lowest highest melting compound

Reaction (two phase)

R

T, P

TB , TM , Tazeotrope

Mixing (single phase) Concentrations below the dew point line

M

T, P

TB , TM , Tazeotrope

Phases: vapor/liquid P – reaction pressure T – highest boiling compound in the liquid phase T – lowest melting compound Ideal mixing: T – lowest melting compound

Ideal mixing: T – highest boiling compound Vapour mixing: T – lowest boiling compound or minimum boiling azeotrope Two-phase mixing Concentrations in V–L equilibrium regions are between the dew and bubble point line

2phM

T, P

TB , TM , Tazeotrope

T – lowest melting compound

T – 2nd highest boiling compound or minimum boiling azeotrope Heating/cooling

H/C

Phase contact

PC

Phase transition Concentrations in V–L equilibrium regions are between the dew and bubble point line

PT

T

TST , TTD

NA

–

–

VL: NA LL: NA SL: NA

T, P

TB , Tazeotrope

T – lowest boiling compound or minimum boiling azeotrope

T – highest boiling compound or maximum boiling azeotrope Phase Separation

PS

–

–

VL: NA LL: NA SL: NA

Note: NA – not applicable, T – temperature, P – pressure, TB – boiling point, TM – melting point, TST – thermal stability and TTD – thermal decomposition.

of hybrid/intensified unit operations that may constitute the flowsheet alternatives investigated in IT-PBS.4. First, retrieve models from a model library incorporated in ICAS-MOT (Heitzig et al., 2011) for hybrid/intensified unit operations, for example, reactive distillation and membrane reactor. Second, perform model-based studies, for example, conversion and product purity achieved for reaction plus separation occurring simultaneously. Third, screen the flowsheet alternatives using the defined logical constraints (1 ), structural constraints (2 ), operational constraints (3 ) for further investigation in IT-PBS.4. Note – Multiple basic structures may perform the same task, thereby, expanding the search space for unit operations inclusive of hybrid/intensified unit operations. Also, the same basic structure may perform multiple tasks, thereby, reducing the number of unit operations in a generated flowsheet alternative. Basic structures are used to identify feasible task-based flowsheet alternatives from a task-based superstructure. The basic structures are translated into unit operations that includes well-known plus novel/mature hybrid/intensified unit operations (where applicable). 3.12. IT-PBS.4 – comparison and selection of the best flowsheet alternatives Objective: To perform economic, sustainability and LCA analyses and calculate the objective function for selection of the best flowsheet alternative.

Action IT-PBS.4.1 – Calculate the economic, sustainability and LCA indicators by applying actions 1–3 from step 8. Action IT-PBS.4.2 – Calculate the objective function value for each feasible flowsheet alternative and select the best flowsheet alternative by ordering them according to objective function values. Each feasible flowsheet alternative must also satisfy the following constraints: logical (1 ), structural (2 ), operational (3 ), performance criteria (ϕ, step 2) and design targets (see step 8). The improvements related to sustainability/LCA factors are part of the performance criteria and are simultaneously checked and compared to the base case design. In non-tradeoff solutions, the selected alternatives must show improvements (or no change) with respect to all considered performance criteria. 4. Process synthesis–intensification: algorithms, supporting methods and tools 4.1. Process synthesis–intensification algorithms Application of the process synthesis–intensification framework requires the use of different algorithms, as highlighted above. The algorithms are grouped into two sets: set-I correspond to algorithms that are needed to decompose the problem from the largest scale (unit operation) to the smallest scale (phenomena); set-II correspond to those that are needed to aggregate from the smallest scale to the largest scale. Set-I consists of three sub-algorithms

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D.K. Babi et al. / Computers and Chemical Engineering xxx (2015) xxx–xxx Table 4 List of objectives and outcomes of each sub-algorithm employed in the process synthesis–intensification framework. Sub-algorithm

Objective

Algorithm I.1

To transform a base case flowsheet into a task-based flowsheet Identification of the PBBs in the base case flowsheet To transform that task-based, base case flowsheet, to a phenomena-based flowsheet Identification of desirable task and PBBs for overcoming the identified process hot-spots Identification of the PBB search space Generation of feasible SPBs using combination rules Generation of a task-based superstructure for identifying feasible task-based flowsheets Identification of feasible tasks to be performed Generation of basic structures Generation of task-based flowsheets Translation of basic structures into unit operations

Algorithm I.2

Algorithm I.3

Algorithm II.1 Algorithm II.2 Algorithm II.3 Algorithm II.4 Algorithm II.5 Algorithm II.6

Table 5 Generation of feasible SPBs using SPB building blocks. Inlet

Rule

SPB building block M=C

1 . . . n(L, V, VL)

M = 2phM

1 . . . n(L, V, LL, VL)

M=R

1 . . . n(L, V, VL)

PC = PT

1 . . . n(VL, LL)

PC = PT = PS

1 . . . n(VL, LL)

Performs cooling of a stream Mixing of a stream with two phases Preforms a reaction without an external energy source Performs the contact of two phases Preforms the separation of two phases

SPB (feasible) M = 2phM = PC = PT

1 . . . n(LL,VL)

M=R=C

1 . . . n(L,V,VL)

M = R = 2phM = PC = PT = PS

1 . . . n(LL,VL)

while set-II consists of six sub-algorithms. A summary of the objective of each sub-algorithm is given in Table 4. 4.1.1. Set-I algorithms For the application of set-I algorithms, the following problem definition is used: Given: a base case design and identified process hot-spots that have been translated into design targets; Identify: a set of desirable tasks and the PBBs representing these tasks. Algorithm I.1. Identify each unit operation; find their corresponding tasks (from a database of unit operation versus tasks); replace each unit operation by a single or multiple tasks to obtain a task-based flowsheet. Algorithm I.2. For each task, find the corresponding PBBs (from a database of tasks versus PBBs); replace each task with their corresponding PBBs to obtain a PBB-based flowsheet. The database used for the identification of PBBs based on tasks and unit operations is given as supplementary material in Table S4. An example of the application of Algorithm I.1 and Algorithm I.2 is shown in Fig. 5. Algorithm I.3. Identify alternative tasks and their corresponding PBBs from the identified hot-spots (from a database of known hotspots, tasks and PBBs linked to property ratio matrices of binary mixtures); add these PBBs to the original list of PBBs. The database used for the identification of PBBs based on tasks and unit operations is given as supplementary material in Table S5. 4.1.2. Set-II algorithms For the application of set-II algorithms, the following problem definition is used: Given: a set of phenomena building blocks and design targets; Identify: generate feasible sustainable designs that minimizes/eliminates process hot-spots and satisfies the design targets. Algorithm II.1. • Calculate the number of possible SPBs using Eq. (9) (Lutze et al., 2013) where nPBBE , nPBBM and nPBBD are the number (n) of energy (E, that is, heating and cooling), mixing (M) and dividing (D) PBBs, respectively.

 

nPBB,max

NSPBmax =

k=1



(nPBB − 1)! +1 (nPBB − k − 1)!k!

nPBB,Max = nPBB − (nPBBE − 1) − (nPBBM − 1) − nPBBD

(9)

11

Performs the mixing of two phases Preforms a reaction with external energy source-cooling Performs a reaction, phase creation and phase separation

• From the total number of possible SPBs, identify the feasible SPBs using pre-defined SPB building blocks and combination rules. An example is given in Table 5. An SPB building block is a pre-defined feasible SPB as is or it can further be combined with other SPB building blocks for generating more SPBs Algorithm II.2. • Generate the task-based superstructure: Identify the minimum number of separation tasks that need to be performed and sequence the tasks starting from all possible reaction tasks to separation tasks (level 1) • Consider merging of adjacent reaction-separation tasks and update the task-based superstructure (level 2) Algorithm II.3. Check the feasibility of the identified tasks through mixture property analyses (Lutze et al., 2013; Babi et al., 2014b). Algorithm II.4. Identify the basic structures that are able to perform a task; list this task as a feasible task with a corresponding basic (SPB) structures. Algorithm II.5. Generate task-based flowsheets consisting of basic (SPB) structures. As an example, consider the reaction (exothermic, single liquid phase) where A and B react to produce C. The order of the boiling points of the compounds A, B and C are as follows: A < B < C. The following scenarios are considered: An azeotrope exist between B and C. Applying Algorithm II.3 and Algorithm II.4, the task-based superstructure and feasible basic structures are given in Fig. 6 and Table 6, respectively. Applying Algorithm II.5, the feasible task-based flowsheet is shown in Fig. 7. It should be noted that one task based flowsheet is feasible from the task-based superstructure, however, by using the concept of basic structures, two feasible basic structures are feasible with respect to performing separation task 2. Therefore, at the unit operations scale this will lead to the generation of 2 flowsheet alternatives compared to 1, if one operated at the task scale. Algorithm II.6. Use a database to translate the basic structures to tasks and then to unit operations. An example of a database used for the translation of basic structures into unit operations is given as supplementary material in Table S6. If the basic structure and

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Fig. 5. Moving toward lower scales, that is, unit operations scale to phenomena scale.

Fig. 6. Task-based superstructure considering task merging (shaded). R – task-reaction task, S – task-separation task.

Fig. 7. Task-based flowsheet (shaded) from the task based superstructure presented in Fig. 6. R – task-reaction task, S – task-separation task.

its corresponding unit operation do not exist, then in principle, a new unit operation is generated. As an example, consider the identified basic structures for separation task 1 and separation task 2 in Table 6. The list of unit operations are then screened based on the phase identity of the feed stream, use of a mass separating agent (MSA) and the presence of azeotropes with the data given in Table 7. The selected feasible unit operations are highlighted in bold in Table 7. 4.2. Supporting methods and tools Table 8 gives a list of different methods and tools that have been available for use through the process synthesis–intensification framework. The supporting methods used in the framework for flowsheet generation and pure compound and mixture analyses are the means-ends analysis (step 6 and IT-PBS.3) and method of

thermodynamic insights (step 6 and IT-PBS.3). The means-ends analysis is based on identifying transformation operators, that is, tasks, for eliminating property differences in moving from an initial state to a goal state (objective) (Siirola, 1996). The method of thermodynamic indicators consists of two levels. In level 1, the binary ratio is calculated and, pure compound analysis and mixtures analysis are performed in order to, identify all feasible tasks for reaction and separation. Feasible separation techniques, for example extractive distillation, are then identified using the compound analyses and identified tasks. In level 2, redundant tasks identified in level 1 are removed and the need for the use of external agents, for example, mass separating agents (for example, solvents) is identified. The remaining tasks in level 2 are screened using model-based calculation and/or graphical techniques for defining a feasible task search space. These tasks that are associated with the identified unit operations (in level 1) are then combined to generate flowsheet alternatives (Jaksland et al., 1995).

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Table 6 Identified basic structures that perform a task.

Note: “*” identified from Algorithm II.3.

Table 7 List of identified unit operations based on identified basic structures for three cases: phase identity of feed stream; MSA and the presence of azeotropes. SPB initiator in the basic structure

Task

Reaction/separation operation

Screening 1: feed phase

Screening 2: MSA-Y/N

Screening 3: azeotrope

=2phM = PC(VL) = PT(VL) = PS(VL)

Separation

N

Separation Separation

N N

N Y/N

=2phM = PC(VL) = PT(VL) = PS(VL)

Separation

Extractive distillation

Y

Y/N

=2phM = PC(VL) = PT(VL) = PS(VL)

Separation

Reboiled absorption

Y

N

=2phM = PC(VL) = PT(VL) = PS(VL) =2phM = PC(VL) = PT(VL) = PS(VL)

Separation Separation

Y Y

N N

=2phM = PC(VL) = PT(VL) = PS(VL) =2phM = PC(VL) = PT(VL) = PS(VL) =2phM = PC(VL) = PT(VL) = PS(VL)

Separation Separation Separation

Stripping Refluxed stripping (steam distillation) Reboiled stripping Evaporation Divided Wall Column

N N N

N N N

=2phM = PC(VL) = PT(VL) = PS(VL) =PC(VL) = PT(PVL) = PS(VL) =PC(VL) = PT(VV) = PS(VV)

Separation Separation Separation

Vapor and/or liquid Liquid Vapor and/or liquid Vapor and/or liquid Vapor and/or liquid Liquid Vapor and/or liquid Liquid Liquid Vapor and/or liquid Liquid Vapor Vapor

N

=2phM = PC(VL) = PT(VL) = PS(VL) =2phM = PC(VL) = PT(VL) = PS(VL)

Partial condensation or vaporization Flash vaporization Distillation

Y N N

N Y Y

Supercritical Extraction Membrane-pervaporation Membrane-vaporpermeation

The sustainability analysis uses an indicator-based methodology where a set of calculated closed- and open-path indicators for identifying the most critical paths that are used to identify the process hot-spots within any process flowsheet. The method calculates and ranks a set of mass and energy indicators, from data obtained from steady-state process simulation or plant real-time data (Carvalho et al., 2009). A brief explanation of the sustainability indicators and what should be done in order to improve the process are presented:

• Material value added (MVA) – The indicator provides an estimate of the value added between the entrance and exit (open path) of a given compound in a given process stream (path). Negative values of this indicator show that the component has lost its value in this open-path and therefore, point to potential for improvements. • Energy and waste cost (EWC) – The indicator is applied to both open- and closed-paths (recycle streams). It takes into account the energy (EC) and compound treatment costs (WC). The indicator represents the maximum theoretical, amount of energy that

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Table 8 Methods and supporting information for performing synthesis, design and sustainable design. Synthesis

Design

Innovative design

Methods

Superstructure Optimization

Simulation and analysis

Input data

Result

-Compounds -Reactions -Conversion -Separation factors -Unit operations -Processing route

-Compounds -Reactions -Conversion/kinetics -Thermodynamic model -Unit operations -Final design -Process hot-spots -Design targets

Model

-Balance models (mass and energy)

Model complexity

Simple but qualitatively correct

-Thermodynaimc models -Process models -Control models More complex but qualitatively as well as quantitatively correct

-Process intensification -Generation of more sustainble designs -Compounds -Mass balance -Energy balance -Unit operations (inclusive of hybrid/intensified) -Sustainable design -Improvement in economic factors -Improvement in LCA/environmental factors Design and sustainabilty share the same common tools

Model solution stragety

Complex because of the number of alternatives to evaluate

Complex because of the type of models-rigorous

Tools

-GAMS -Excel + GAMS -EOLO + GAMS

-ICAS -PROII+ -ASPEN -HYSYS -MoT -gPROMS -SuatinPro -LCSoft -SimaPro

can be saved in a path. High values of this indicator show high consumption of energy and waste costs. Therefore, these paths should be considered in order to reduce the indicator value. • Total value added (TVA) – The indicator describes the economic impact of a compound in a path. It is the difference between MVA and EWC. Therefore, negative values of this indicator show high potential for improvements related to the decrease in the variable costs. The tools used in the framework for performing various calculations associated with synthesis, design and innovation design are presented in Table 9. 5. Application of process synthesis–intensification: case study Application of each step of the framework is highlighted through a case study involving the production of di-methyl carbonate (DMC) from propylene carbonate (PC) and methanol (MeOH) with, propylene glycol (PG) as a by-product. 5.1. Step 1 – need identification Action 1 – Di-methyl carbonate (DMC) is an important, environmentally friendly, bulk chemical that is used as a fuel additive compared to methyl tert-butyl ether (MTBE) (Bilde et al., 1997), among other uses. The by-product of the reaction considered in this case study is also a valuable product, that is, propylene glycol (PG). Propylene glycol can be used for making plastics and heat transfer fluids, among others (CEFIC, 2008). Action 2 – The total production per year of DMC based on three of the main producers, Henan Zhongyuan Dahua Group located in China and, UBE and HighChem located in Japan, is

Simple but qualitatively correct (design tagets identification) Complex but qualitatively as well as quantitatively correct (evaluation of alternatives) Qualitatively correct (design tagets identification) Complex because of the type of models-rigorous (evaluation of alternatives) Design and sustainability share the same common tools

100 × 103 tons/year (World of Chemicals, 2012). Therefore, the specified production target is 1700 kg/h (Holtbruegge et al., 2013a). The purity of the product and by-product are set to be greater than or equal to 99.9 wt% and 99 wt%, for DMC and PG, respectively. 5.2. Step 2 – problem definition Action 1 – Problem statement: The identification of more sustainable process designs for the production of DMC. Action 2 – The objective function is defined in terms of minimizing the total annualized cost, Eq. (10) subject to constraints and performance criteria. In Eq. 10, C, m, E and t represent cost, mass and energy flows, and the project lifetime set at 10 years, respectively:



Max Fobj =

Ei CUt,i + (CEquip /tproj ) mprod

(10)

Action 3 – The considered constraints are given in Table 10. 5.3. Step 3 – reaction identification/selection Action 1 – The raw materials for the selected reaction are MeOH and PC. The Gibbs free energy, G0 = 4.76 kJ/mol at standard conditions (T = 298 K and P = 1 bar) and the equilibrium constant, Keq = 0.247, is obtained by fitting experimental data to a model (Williams et al., 2009). The operating temperature of the reaction is set at 313 K based on data reported by Holtbruegge et al. (2013b): 2CH4 O + C4 H6 O3 ↔ C3 H6 O3 + C3 H8 O2

(11)

Action 2 – The products and by-products from Eq. (11) are in the liquid phase. The heat of reaction is calculated to be −41.67 kJ/mol using standard heat of formation data, with an equilibrium conversion of approximately 54%. The reaction is an equilibrium reaction,

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Table 9 Tools applied in each step of the sustainable process synthesis–intensification framework. IT-PBS – integrated task-phenomena based synthesis, S – step. Step

Method/database

Objective

Tool ICAS/ICAS databasea

Database

Azeotrope databasea Knowledge-baseb

Database Database

Phase diagram generation/compound data Azeotrope data Hybrid/intensified unit operations Property Prediction Binary ratio Process simulation Economic analysis Sustainability analysis LCA analysis Model evaluation

a

ProPred

Model based

CAPSSa ASPEN/PROII

Model based Model based

ECONa

Model based a

SuatainPro

Model based

LCSOfta MoTa

Model based Equation oriented problem solution CAMD/Database

SolventProc

S1

S2

S3 *

S4

S5

S6

S7

S8

*

IT-PBS.1

IT-PBS.2

IT-PBS.3

*

*

IT-PBS.4

*

*

*

* * *

* * *

*

*

*

*

*

*

* *

* *

Solvent selection

a

Part of ICAS (Gani et al., 1997). The knowledge base tool consists of a list of hybrid/intensified unit operations (Lutze et al., 2013; Babi et al., 2014b) and the information specified in algorithm I and algorithm II. c The corresponding tool for a given process step in the framework. b

that is, it is reversible. The heat of reaction, Hrxn < 0, therefore the reaction is exothermic. 5.4. Step 4 – check for availability of the base case design Action 1 – From a literature survey, a known base case design (Schlosberg et al., 2002) is available and was pre-selected. It Table 10 Logical, structural and operational constraints and, performance criteria for achieving sustainable process synthesis–intensification of DMC production. Objective

Constraint

1 Flowsheet structure: reaction + separation Reaction occurs in the first unit operation The product purity of DMC and Propylene glycol is defined is ≥99 wt% PBBs are connected to form SPBs based on combination rules SPBs are connected to form Basic Structures based on combination rules Do not use mass separating agents for reaction/separation Recycle un-reacted raw materials Do not use recycle streams if not necessary Raw materials, methanol and acetic acid are assumed to be in their pure state The equilibrium conversion is defined as 54% (is to possibly be increased) Production target of DMC is set at 122 × 102 tons/year PI screening criteria for basic structures to unit operations: Novel equipment feasible Increase MeOH conversion is explored Minimization/reduction in energy consumption Inclusion of intensified equipment Reduction in the number of unit operations Waste minimization Sustainability and LCA factors must be the same or better

2

Performance criteria (ϕ) -

3

* * * * * * * *

is shown in Fig. 8. The pre-selected base case design consists of 5 unit operations: 1 reactor (R1) and 4 distillation columns (T1–T6). A brief description about the process is as follows. The raw materials, MeOH and PC, are fed at a mole ratio of 5:1, with MeOH in excess, to the reactor. In the reactor a trans-esterification reaction occurs to produce DMC and PG. The reactor outlet (effluent) consists of a multi-component mixture of MeOH, PC, DMC and PG. A minimum boiling azeotrope exists between MeOH/DMC. The first distillation column (T1) separates PC and PG (bottom of T1) from the reactor effluent. The top of T1 contains MeOH and DMC. The top stream of T1 is separated using pressure swing distillation, that is, the use of two distillation columns, (T2 and T3). The feed composition of MeOH and DMC entering T2 at the column pressure of 10 bar is to the left hand side of the azeotrope, therefore, high purity DMC is obtained as the bottom product of T2 and the top product of T2 is the MeOH/DMC azeotrope. The feed composition of MeOH and DMC entering T3 at the column pressure of 1 bar is to the right hand side of the azeotrope, therefore, MeOH is obtained as the bottom product. The recovered MeOH is recycled to the reactor and the top product is the MeOH/DMC azeotrope. In T4, PG is separated from PC. The recovered PC is recycled to the reactor.

5.5. Step 5 – check for base case feasibility *

The pre-selected base design in step 4 is verified using the process synthesis method of Douglas (1985). Based on the analysis the pre-selected base case design satisfied the synthesis method. Therefore, step 6 is by-passed.

* * * * * * * * *

5.6. Step 7 – perform rigorous simulation Action 1 – The base case design is rigorously simulated using equilibrium based models for the reactor and separators using Aspen Custom Modeler (Holtbruegge et al., 2013b). MeOH is fed in excess in order to achieve the equilibrium conversion. An overview of the simulation results is given in Table 11.

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Fig. 8. Selected and simulated base case design. The closed path (CP) from the sustainability analysis is also highlighted.

• • • • • •

Table 11 Base case design simulation results. Value Feed mole ratio (MeOH:PC) DMC product (kg/h) Energy usage (MJ/h) Utility cost ($/year)

5:1 1700 133,563 4393,537

Improvements in LCA/sustainability indicators Unit operations reduction Product purity (kept as the base case) Production target (kept as the base case) Reduce operational cost Waste minimization

Table 12 Closed Path (CP6, see Fig. 8) that has the highest potential for improvement. Path

Compound

Flowrate (kg/h)

MVA (103 $/year)

TVA (103 $/year)

EWC (103 $/year)

CP6

MeOH

761.83

–

–

10253

Action 2 – The following data are extracted from the simulation of the base case design, detailed mass and energy balance data, number of streams and unit operations that constitute the base case design. This data is used for the analyses in step 8. 5.7. Step 8 – economic, sustainability and LCA analysis Action 1–3: An economic, sustainability and LCA analysis are performed. The sustainability analysis is shown in Fig. 8 where the most critical stream (path) are highlighted and listed in Table 12. The LCA analysis and utility cost distribution (obtained from the economic analysis) are shown in Fig. 9(a) and (b) respectively. From Table 12, CP6 which follows the raw material MeOH, has a high EWC. This translates into a high flow of MeOH being recycled within this path, resulting in high loads of energy and waste/use of utilities. From Fig. 8, the unit operations belonging to this closedpath are T2 and T3. From Fig. 9, T2 and T3 also have high carbon footprints, that is, the reboiler of these two columns account for 30% and 15% of the utility costs. Action 4 – The identified process hot-spots are given in Table 13. Action 5 – Using Table 2, the design targets to be set/met are: • Reduce energy consumption • Reduce utility cost

Fig. 9. (a) LCA analysis; (b) utility cost distribution. Cond-condenser; Reb-reboiler.

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Table 13 Identified process hot-spots for the base case design. Indicator values

Base case property

Cause

Identified process hot-spot

˛1 -raw material cost ˇ1 -MVA

Un-reacted raw materials

Equilibrium reaction

˛2 -utility cost ˇ1 -MVA ˇ2 -EWC  1 -CO2 eq  2 -PEI

Un-reacted raw materials and products recovery

-Presence of azeotrope(s) -High energy usage-heating/cooling

-Limiting equilibrium/raw material loss -Azeotrope -Difficult separation due to low driving force -High energy consumption/demand

MeOH

Reaction Task MeOH+PC

PC

Separation Task MeOH+DMC/PC+PG

Separation Task MeOH/DMC

Separation Task MeOH/DMC

MeOH DMC

Separation Task PC/PG

PG

PC

Fig. 10. Task based flowsheet of the base case design.

Table 14 Binary ratio matrix for a set of selected properties. rij (Binary pair)

MeOH/PC MeOH/DMC MeOH/PDO PC/DMC PC/PDO DMC/PDO

Property Binary Ratio Mw

Tm

Tb

RG

SolPar

VdW

VM

VP

3.19 2.81 2.37 1.13 1.34 1.18

1.28 1.56 1.21 1.22 1.05 1.28

1.52 1.08 1.36 1.42 1.12 1.27

2.20 2.09 2.03 1.05 1.08 1.03

1.13 1.46 1.00 1.30 1.12 1.46

2.08 2.13 2.15 1.02 1.03 1.01

2.10 2.09 1.82 1.01 1.16 1.15

2736.13 2.28 980.63 1198.69 2.79 429.61

MW – molecular weight (g), Tb – normal boiling point (K), RG – radius of gyration (Å), Tm – normal melting point (K), VM – molar volume (m3 /mol), SolPar (Hansen) – solubility parameter, VDW (m3 /mol) – Van der Waal volume, VP (kPa) – vapor pressure.

5.8. IT-PBS.1 – process analysis Action 1 – The task based flowsheet of the base case design is shown in Fig. 10. Action 2 – The phenomena based flowsheet of the base case design is generated and shown in Fig. 11. The identified PBBs in the base case design are: • Reaction task: M, R, C • Separation task: ◦ VL-M, 2phM, C/H, PC(VL), PT(VL), PS(VL) Action 3 – The binary ratio matrix and the azeotrope analysis are presented in Table 14 and Fig. 12.

From Fig. 12 a minimum boiling binary azeotrope is found between MeOH/DMC. This is can also be pre-conceived from the boiling point binary ratio matrix value because it is also the only binary pair that has a value close to unity. The azeotrope is further analyzed for pressure dependence and from Fig. 12, it can be seen that the azeotrope is pressure dependent. At low pressures the MeOH/DMC azeotrope reaches a MeOH purity of approximately 80 mol% and at high pressure the azeotrope disappears. 5.9. IT-PBS.2 – identification of desirable tasks and phenomena Action 1 – The additional PBBs selected are PT(PVL), PT(VV), and PS(VV). These are selected as follows. From step 7 one of the identified process hot-spots is the presence of an azeotrope, which may need to be broken in order to obtain the compounds that are associated with the azeotrope. Therefore, using Table S5.1, the process hot-spot “azeotrope” is identified (in column 1). Then, the properties listed in column 3 of Table S5.1 are retrieved from the ICAS database (Gani et al., 1997) and from these the binary ratios are calculated (see Table 14). The rule for selection is that if the binary ratios are greater than 1.2, the corresponding PBBs are selected. For binary ratios close to unity or less than 1.2, the separation promoted by the PBBs is not feasible. For example, the boiling point binary ratio of the binary pair MeOH/DMC is 1.08 which is close to unity, therefore, a PBB PT(VL) will most likely not separate a stream of these two compounds into two high purity products. The selected PBBs are PT(PVL), PT(VV), and PS(VV). Therefore, the total list of PBBs are: R, M, 2phM, C, H, PT(VL), PT(PVL), PT(VV), PC(VL), PS(VL), PS(VV), D.

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MeOH

PC

M, 2phM, C/H, PC(VL), PT(VL), PS(VL)

M, 2phM, C/H, PC(VL), PT(VL), PS(VL)

M, C, R

M, 2phM, C/H, PC(VL), PT(VL), PS(VL)

MeOH DMC

M, 2phM, C/H, PC(VL), PT(VL), PS(VL)

PG

PC

Fig. 11. Phenomena based flowsheet of the base case design.

1 0.9 0.8 0.7

x1

0.6 DMC/MeOH

0.5

MeOH/DMC

0.4 0.3 0.2 0.1 0 0

5

10

15

20

25

30

35

P/bar Fig. 12. Pressure dependence analysis of the minimum boiling azeotrope between MeOH and DMC.

Action 2 – The remaining PBBs from applying the constraints defined in step 2 are R, M (assuming four types: ideal liquid, flow, rectangular, ideal vapor), 2phM, C, H, PT(VL), PT(PVL), PT(VV), PC(VL), PS(VL), PS(VV), D. Action 3 – The operating window for each PBB is given in Table 15. 5.10. IT-PBS.3 – generation of feasible flowsheet alternatives Action 1 – The maximum number of phenomena that can be combined to form an SPB, nPBB,Max , is calculated to be 11. The total number of SPBs that can be generated, having a maximum of 11 PBBs is calculated to be16278. A list of feasible SPBs assuming three types of mixing, that is, that is ideal liquid, flow and rectangular, are presented in Table 16. Action 2-3 – The generated task based superstructure is shown in Fig. 13. Action 4 – Table 17 gives the identified basic structures that perform reaction and separation tasks. Action 4 – The identified task based flowsheets that are highlighted in level 1 (no task-merging) and level 2 (considering

Table 15 List of operating windows of the identified PBBs in the base case design. All concentrations are below the dew point line and above the bubble point line. Subscript: Id – ideal, V – vapor. Phenomena

Operating window

R

Tlow = 337.70 K (lowest boiler) Thigh = 313.15 K (set T for reactor according to base case) Tlow = 337.70 K (lowest boiler) Thigh = 514.70 (highest boiler) Tlow = 175.15 K (lowest melter) Thigh = 514.70 K (highest boiler) Tlow = 336.66 K (lowest boiling azeotrope) V–L present Tlow = 336.66 K (lowest boiling azeotrope) Thigh = 514.70 K (highest boiler) V–L present Component affinity Component affinity V–V present (all compounds in the vapour phase) – – –

MV Mld MV , 2phM PC(VL) PT(VL) PS(VL) PT(PVL) PT(VV) PS(VV) H C D

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Table 16 Partial list of feasible SPBs. Mix. – mixing, Cool. – cooling, Heat. – heating, React. – reaction, Sep. – separation, Ph. Cr. – phase creation, Div. – dividing. SPB

Connected PBB

In

Out

Task they may perform

SPB.1 SPB.2 SPB.3 SPB.4 SPB.5 SPB.6 SPB.7 SPB.8 SPB.9 SPB.10 SPB.11 SPB.12 SPB.13 SPB.14 SPB.15 SPB.16 SPB.17 SPB.18 SPB.19 SPB.20 SPB.21 SPB.22 SPB.23 SPB. . . SPB.70

M M = 2phM M=R M=H M=C M=R=H M=R=C M = R = H = PC(VL) = PT(VL) M = R = C = PC(VL) = PT(VL) M = R = 2phM = PC(VL) = PT(VL) M = R = 2phM = PC(VL) = PT(VL) = PS(VL) M = R = 2phM = PC(VL) = PT(PVL) = PS(VL) M = R = H = 2phM = PC(VL) = PT(PVL) = PS(VL) M = R = C = 2phM = PC(VL) = PT(PVL) = PS(VL) M = 2phM = PC(VL) = PT(VL) M = 2phM = C = PC(VL) = PT(VL) M = 2phM = H = PC(VL) = PT(VL) M = 2phM = PC(VL) = PT(VL) = PS(VL) M = C = 2phM = PC(VL) = PT(VL) = PS(VL) M = H = 2phM = PC(VL) = PT(VL) = PS(VL) M = 2phM = PC(VL) = PT(PVL) = PS(VL) M = 2phM = PC(VL) = PT(VV) = PS(VV) M = 2phM = PT(VV) = PS(VV) ... D

1 . . . n(L,VL,V) 1 . . . n(L,VL,V) 1 . . . n(L,VL,V) 1 . . . n(L,VL,V) 1 . . . n(L,VL,V) 1 . . . n(L,VL,V) 1 . . . n(L,VL,V) 1 . . . n(L,VL,V) 1 . . . n(L,VL,V) 1 . . . n(L,VL) 1 . . . n(L,VL) 1 . . . n(L,VL) 1 . . . n(L,VL) 1 . . . n(L,VL) 1 . . . n(L,VL) 1 . . . n(L,VL) 1 . . . n(L,VL) 1 . . . n(L,VL) 1 . . . n(L,VL) 1 . . . n(VL) 1 . . . n(VL) 1 . . . n(L,VL,V) 1 . . . n(V) ... 1(L;VL,V)

1(L,VL,V) 1(L,VL,V) 1(L,VL,V) 1(L,VL,V) 1(L,VL,V) 1(L,VL,V) 1(L,VL,V) 1(L,VL,V) 1(L,VL,V) 2(V/L) 2(V;L) 2(V;L) 2(V;L) 2(V;L) 2(V;L) 2(V;L) 2(V;L) 2(V;L) 2(V;L) 2(V;L) 2(V;L) 2(V;V) 2(V;V) ... 1 . . . n(L;V; VL)

Mix. Mix. Mix. + React. Mix. + Heat. Mix. + Cool. React. + Heat. React. + Cool. React. + Heat. React. + Cool. React. + Sep. React. + Sep. React. + Sep. React. + Sep. React. + Sep. Mix. + Ph. Cr Mix. + Ph. Cr. Mix. + Ph. Cr. Mix. + Sep. Cool. + Sep. Heat. + Sep. Mix. + Sep. Mix. + Sep. Mix. + Sep. ... Stream Div.

R-Task 1

S-Task 1

S-Task 2

S-Task 3

React.

Sep. A(BCD)

Sep. B(CD)

Sep. C(D)

React.

Sep. B(ACD)

Sep. C(BD)

Sep. C(D)

Sep. B(ACD)

Sep. D(BC)

Sep. B(D)

Sep. C(ABD)

Sep. A(CD)

Sep. B(C)

Sep. D(ABC)

Sep. C(AD)

Sep. A(D)

Sep. AB(CD)

Sep. D(AC)

Sep. A(C)

Sep. AC(BD)

Sep. A(BD)

Sep. A(B)

Sep. AD(BC)

Sep. B(AD)

Sep. A/C/D

React.+Sep. AB(CD)

Sep. D(AB)

React.+Sep. AC(BD)

Sep. A(BC)

React.+Sep. AD(BC)

Sep. B(AC)

React.+Sep. (CD)

Sep. C(AB)

React.+Sep. (BD)

Sep. A(B)

React.+Sep. (AD)

Sep. A(B)

Level 1

Level 2 (task merging)

Note: For each task an example of all the possible connections (infeasible + feasible) are shown in the superstructure

Sep. A(C) Sep. A(D) Sep. B(C) Fig. 13. Task-based superstructure, including task-merging. Flowsheet alternatives 3 (bold), Flowsheet alternatives 5 (italics), Flowsheet alternatives 9 (underlined). A-PC, B-MeOH, C-DMC, D-PG. R – task-reaction task, S – task-separation task, React. – reaction, Sep. – Separation.

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Table 17 Identified basic structures that perform single or multiple tasks.

Note: “*” the SPB number corresponds to the SPB given in Table 16; for combined basic structures, only the SPBs present in the combined basic structure are highlighted in this table; each binary pair that represents the inlet to a task is actually representing the two key compounds under consideration.

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task-merging) of the task based superstructure shown in Fig. 13 are explained as follows: Level 1: Flowsheet alternative 2–3 highlighted “bold” in Fig. 13: • Flowsheet alternative 2–3: A reactor feed, as in the base case design, of a 5:1 mole ratio of MeOH to PG is used. The reaction is reversible taking place in the liquid phase, therefore, the reactor outlet contains a mixture of raw materials and products. Therefore, a basic structure containing a R(L) PBB is selected to perform the reaction task. From the mixture analysis, the minimum boiling, pressure dependent azeotrope basic structures containing a PT(PVL) or PT(VV) PBB is selected to separate MeOH from DMC. The stream from S-Task 1, contains DMC, PG and PC. No azeotropes are present in this mixture. From the basic structures identified in Table 17, a basic structure containing a PT(VL) PBB is feasible to perform the S-Task 1 and S-Task 2. At this point all recovered unreacted raw materials are recycled thereby closing the task based flowsheet. Level 2: Flowsheet alternative 4–5 highlighted in “italics” and flowsheet alternatives 6–9 “underlined” in Fig. 13: • Flowsheet alternative 4–5: In level 2 the task merging is considered and is feasible because the same basic structure performs multiple tasks and this reduces the overall number of tasks that must be performed in meeting a desired design target. Therefore, the merging of the S-Task 1 and S-Task 2 is considered. The tasks are merged based on the system properties: (1) Distinct boiling points between the compounds to be separated DMC, PG and PC and (2) no azeotropes present. This is given in Table 18. • Flowsheet alternative 6–9: The merging of reaction and separation tasks is considered and found to be feasible because SPBs that perform simultaneous reaction and separation (see, for example, Table 16, SPB.10) can be combined to form basic structures that perform these two tasks simultaneously. Therefore, the merging of R-Task, S-Task 1, S-Task 2 and S-Task 3 is considered. This is possible based on the system properties: (1) esterification reaction (2) an azeotrope is present and (3) the reaction is in the liquid phase. The basic structures are be combined to perform a R = S Task together. This is given in Table 19, where the basic structures presented in Table 18 have been combined, therefore, the corresponding tasks are merged. Action 6 – The basic structures that perform the different identified tasks are translated into unit operations. The final flowsheet alternatives are presented in Fig. 14 using a superstructure representation. In Fig. 14(a), flowsheet alternative 2 uses a pervaporation membrane and flowsheet alternative 3 uses a vapor permeation membrane. In Fig. 14(b), flowsheet alternative 4 uses a pervaporation membrane and flowsheet alternative 5 uses a vapor permeation membrane. In Fig. 14(c) flowsheet alternative 6 is a single feed RD column with reactive stages only, flowsheet alternative 7 is a double feed RD column with reactive stages only, flowsheet alternative 8 is a single feed RD column with both reactive and non-reactive stages and flowsheet alternative 9 is a double feed RD column with both reactive and non-reactive stages. Action 7 – The flowsheet alternatives are analyzed and screened in order to select the feasible alternatives for IT-PBS.4. 1. The flowsheet alternatives are analyzed using the following models while satisfying the following logical (1 ) and operational constraints (3 ) (defined in step 2), 1 – The product purity of DMC is defined is ≥99 mol%, 3 – Production target of DMC is set at 1700 kg/hr:

Fig. 14. The generated flowsheet alternatives for the production of DMC. (a) Flowsheet alternatives 2–3, (b) flowsheet alternatives 4–5, (c) flowsheet alternatives 6–9, (d) flowsheet alternative 9. VP – vapor permeation membrane, PV – pervaporation membrane.

• Flowsheet alternative 2–5: From the availability of data (Holtbruegge et al., 2013c) and the system properties, a vapor permeation membrane is selected. It should be noted that in principle, since the reactor outlet is liquid, a pervaporation membrane would be a better membrane candidate. However, the limitation of availability of data for such a membrane exists. For the investigation of the dividing wall column, the

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Table 18 Identified basic structures for flowsheet alternatives 3. Note each binary pair that represent the inlet to a task represent the two key compounds under consideration.

Note: “*” the SPB number corresponds to the SPB given in Table 16. For combined basic structures, only the SPBs present in the combined basic structure are highlighted in this table.

method of Rangaiah et al. (2009) and the method of Halvorsen and Skogestad (2011) was used. • Flowsheet alternative 6–9: Reactive distillation model. This investigation was carried out by Holtbruegge et al. (2013a, 2014). Alternatively, the elemental-based method for reactive distillation design by Daza et al. (2003), where the compounds in the system are represented by elements and analyzed, can

be employed for analysis of the reactive distillation superstructure. It was found that the best configuration of the reactive distillation column was a double feed RD column with both reactive and non-reactive stages. The top composition of the column consists of the MeOH/DMC mixture and the bottoms PG. The top product of the RD column is in the vapor phase, therefore, it is fed to a vapor permeation

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Table 19 Identified basic structures for flowsheet alternatives 6–9.

Note: “*” the SPB number corresponds to the SPB given in Table 16. For combined basic structures, only the SPBs present in the combined basic structure are highlighted in this table.

membrane where MeOH is removed until the composition is on the side of the azeotrope where DMC can be recovered by vapor-liquid separation. The basic structures for these two separations, that is, the removal of MeOH and the separation of DMC/MeOH to recover DMC, are given in Table 18 (containing PT(VV) PBB) and Table 17 (containing PT(VL) PBB), respectively. The final flowsheet alternative is given in Fig. 14(d). 2. The screening of the flowsheet alternatives is given in Fig. 15. The total number of generated SPBs was 16,278 and using the combination rules (structural constraint), 64 were found to be feasible. The 64 SPBs were then combined to fulfill reaction, separation and reaction-separation tasks and 9 flowsheet alternatives were generated. The 9 flowsheet alternatives were screened based on the availability of membrane data and, the logical and operational constraints defined in step 2. Of the 9 alternatives, 4 were found to be feasible. The 4 feasible flowsheet alternatives selected for further analysis in IT-PBS 4 are flow-sheet alternative 1 (not shown), flowsheet alternative 3 (see Fig. 14(a), with vapor permeation membrane), flowsheet alternative 5 (see Fig. 14(b), with vapor permeation membrane) and flowsheet alternative 9 (Fig. 14(c), double feed RD

column with both reactive and non-reactive stages, the final design is shown in Fig. 14(d)). 5.11. IT-PBS.4 – comparison and selection of the best flowsheet alternatives The results of the economic factors, sustainability metrics and LCA factors for the four feasible more sustainable process designs are given in Table 20. The total operating time of each process is assumed to be 300 days/year. The prices used for different utilities are: cooling water at 0.35 $/GJ, LP Steam at 7.78 $/GJ and electricity at 16.80 $/GJ (Peters et al., 2003). The results of the objective function (Eq. (10)) are given in Table 20. Flowsheet alternative 1 and flowsheet alternative 5 show the best values of the objective function and the lowest carbon footprint (see LCA results in Table 20). The four flowsheet alternatives are all better than the base case design with respect to economic factors and sustainability metrics and LCA factors. The payback periods of the equipment based on capital investment for the different alternatives are given in Table 20. It can be seen that alternative 1, alternative 5 and alternative 9 have the lowest payback period. Also, these alternatives earn higher profits per year compared to the base case design. The profit is defined as the

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Fig. 15. Screening of the 9 generated flowsheet alternatives.

Table 20 Economic, sustainability and LCA analysis for the 4 feasible flowsheet alternatives generated for the production of DMC. Measures of comparison (units)

Base case

Alternative 1

Alternative 3

Alternative 5

Alternative 9

Feed mole ratio Mole feed to reactor (kmol/h) Mole feed Make-up (kmol/h) PC conversion Input-MeOH (kg/h) Input-PC (kg/h) Input-total (kg/h)

5–1 177:35 38:19 0.54 1215.65 1936.6 3152.25

2–1 70:35 38:19 0.535 1212.329 1937.97 3150.299

5–1 38:19

5–1 38:19

0.54 1211.614 1949.66 3161.274

0.54 1211.614 1953.859 3165.473

12.5– 237:19 38:19 99.5 1214.45694 1934.69658 3149.15352

Outlet information

DMC product (kg/h) Operating Days Product purity (DMC,%) By-product purity (PG,%) Raw material loss-MeOH (kg/h) Raw material loss-PC (kg/h) Raw material loss-total (kg/h)

1698 300 99.9 99 4.86 7.74 12.59

1698 300 99.9 99.1 0.72 7.79 8.51

1702 300 99.92 98.6 0.00 0.19 0.19

1699 300 98.9 98.1 0.00 23.70 23.70

1698 300 99.9 99 6.24 9.95 16.19

Results summary

Energy usage (MJ/h) Utility cost ($/year) Raw material cost ($/year) Operating cost ($/year)

133,563 4393,537 24,853,986 29,247,523

28,424 979,301 24,858,022 25,837,323

17,818 906,474 24,981,958 25,888,432

17,734 593,541 24,820,352 25,413,893

64,816 2,011,964 24,829,564 26,841,528

Primary performance metrics

Raw material usage (kg raw material/kg DMC product) Energy usage per kg of product (MJ/kg of DMC product) Raw material cost per kg of product ($/kg of DMC product) Equipment capital investment ($) Utility cost per kg of product ($/kg of DMC product) Operational cost ($/kg of DMC product)-min Product Sale ($/year) Product sale per kg of product ($/kg of DMC product) Profit ($/year)-max Profit ($/kg of DMC product)-max Payback period-equipment (years) Fobj -TAC ($/kg of DMC product)-min

1.86 78.65 2.03 667,541 0.36 2.39 32,580,433 2.66 3,332,909.93 0.27 0.2 0.36

1.85 16.74 2.03 284,523 0.08 2.11 32,415,861 2.65 6,578,537.92 0.54 0.02 0.08

1.86 10.47 2.04 1,640,804 0.07 2.11 32,672,416 2.67 6,783,984.39 0.55 0.24 0.09

1.86 10.44 2.03 1,780,088 0.05 2.08 32,538,792 2.66 7,124,898.39 0.58 0.15 0.06

1.85 38.17 2.03 1,801,222 0.16 2.20 32,550,111 2.66 5,708,583.86 0.47 0.15 0.18

LCA results

Total carbon footprint (kg CO2 /kg of DMC product) HTPI (1/LD 50) GWP (CO2 eq.) HTC (kg benzen eq) HTNC (kg toluen eq.)

2.08 2.83E−04 6.15E+00 4.68E−03 6.84E−02

0.46 2.76E−04 4.53E+00 4.01E−03 6.68E−02

0.31 2.75E−04 4.37E+00 3.94E−03 6.65E−02

0.31 6.08E−05 3.70E+00 3.80E−03 6.63E−02

0.98 2.78E−04 5.05E+00 4.20E−03 6.73E−02

Inlet information

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25

Operational Cost 0% 20% GWP (CO2 Eq)

Utility Cost

40% 60% 80% 100%

HTPI (1/LD50)

Energy usuage

Carbon Footprint (CO2 Eq)

Base Case

Alternative 1

Profit

Alternative 3

Alternative 5

Alternative 9

Fig. 16. Economic and LCA improvements relative to the base case design per kilogram of DMC produced. HTPI – Human Toxicity Potential by Ingestion, GWP – Global Warming Potential.

difference between the product sale and the operating cost (Babi et al., 2014a, 2014b, 2014c). The operating cost in this case study includes the raw material costs and the utility costs. The results presented in Table 20 are represented in terms of a radar plot (Babi et al., 2014b). From Fig. 16 it can be seen that for all the considered criteria, the alternatives are better that the base case design and therefore, non-tradeoff solutions have been obtained because the values of the indicators all lie within the radar plot. From the results presented in Table 20 and the design targets set in step 8, the following can be concluded for each alternative. Reductions in energy consumption and utility cost have been achieved. The alternatives have a better value of the objective function compared to that of the base case design while showing improvements in the sustainability metrics and LCA factors. The number of unit operations for each alternative have been reduced for each alternative that is: 5 (base case), 4 (flowsheet alternative 3), 4 (flow-sheet alternative 5) and 3 (flowsheet alternative 9). For each of the alternatives the product purity has been kept or improved while maintaining the production target. The operational cost of the alternatives showed improvement compared to the base case design and raw material lost has been minimized. 6. Conclusion A computer-aided, multi-level, multi-scale, synthesis, design and intensification framework to achieve more sustainable process alternatives has been presented. The framework operates at three different scales, the unit operations scale, task scale and phenomena scale. The synthesis-intensification problem has been formulated as an MINLP problem and in order to manage the complexity in solving the problem, a decomposition based solution strategy method is applied. The framework is systematic with a given hierarchal structure following a series of steps in order to achieve sub-objectives defined by each step. The framework is computer-aided because different computer-aided tools assist in achieving the objectives of a particular step. The framework is multi-level because it operates at different stages, that is, the synthesis stage, design (and analysis) stage and innovation design

stage. The framework is multi-scale because it performs process synthesis–intensification at different scales, that is, the unit operations scale, the task scale and phenomena scale. The framework has the flexible ability to cover/handle a wider range of applications and robust-reliable for its ability to solve a wide range of synthesisintensification problem using reliable numerical methods. It has been shown that based on the application of the framework, more sustainable process designs inclusive of hybrid/intensified unit operations can be generated by simultaneously performing process synthesis and intensification together. To identify design targets that would lead to more sustainable design alternatives, three analyses have been implemented within the framework. These are, economic factors, sustainability metrics and LCA factors that provide the relevant indicators. If all design targets are met, process hot-spots are either minimized and/or eliminated, thereby producing non-tradeoff solutions for process designs that are therefore, more sustainable. For the DMC process, 9 feasible flowsheet alternatives have been generated, including those containing hybrid/intensified unit operations. These have been generated using a rule based approach by first combining PBBs to form SPBs. These SPBs are then combined to form basic structures that satisfy a task or set of tasks for reaction, separation and, simultaneous reaction and separation. The 9 alternatives are screened using the defined structural and operational constraints and evaluated for selection of the more sustainable flowsheet designs (4 in total were selected). The reduction in energy consumption (per kilogram of product) and utility cost, varies from 78% to 86% for the 4 more sustainable designs which has a positive impact on the carbon footprint showing an improvement of between 53% and 85%. The objective function related to the total annualized cost is the best for flowsheet alternative 5, which employs a divided wall column. The framework has also been successfully applied to a wide range of case studies employing further chemical processes, that is, the production of isopropyl acetate (Lutze et al., 2013) and methyl acetate (Babi et al., 2014b, 2014c), as well as bio-processes, that is, the production of N-acetyl-dneuraminic acid (Lutze et al., 2012) and bio-diesel (Mansouri et al., 2013).

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