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Towards a systematic framework for the synthesis of operable process intensification systems
Mario R. Eden, Marianthi Ierapetritou and Gavin P. Towler (Editors) Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018 July 1-5, 2018, San Diego, California, USA © 2018 Elsevier B.V. All rights reserved. https://doi.org/10.1016/B978-0-444-64241-7.50392-X
Towards a systematic framework for the synthesis of operable process intensification systems Yuhe Tiana, M. Sam Mannana,b, Efstratios N. Pistikopoulosa,c* a
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, United States b Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering Texas A&M University, College Station, TX 77843, United States c Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, United States [email protected]
Abstract In this work we present an integrated framework to derive intensified designs with guaranteed safety and operability performance. A step-by-step procedure is described featuring: (i) superstructure-based process synthesis using Generalized Modular Representation Framework, (ii) development of advanced multi-parametric control strategies following the PAROC framework and software platform, and (iii) inherent safety analysis at early design stage utilizing quantitative risk assessment. Two case studies with increasing intensification complexity, namely heat exchanger network synthesis and reactive distillation column design, are investigated to demonstrate the capabilities of the proposed framework. Keywords: Process intensification, Generalized Modular Representation Framework, multi-parametric Model Predictive Control, risk assessment
1. Introduction Modular process intensification (PI) offers the potential to drastically reduce the energy consumption and processing cost by involving multifunctional phenomena at different time and spatial scales (Van Gerven and Stankiewicz, 2009). In this context, phenomena-based synthesis tools, comparing to their conventional unit-operation-based counterparts, have attracted much interest as a potential means for automated generation of intensified options from a lower aggregation level without a pre-postulation of plausible flowsheets. Recent progress in this field includes, but not limited to, computer-aided software-tool for phenomena-based multiscale and multistage sustainable process synthesis – intensification by Tula et al. (2017), Infinite DimEnsionAl State-space (IDEAS) framework application on energetic intensification of hydrogen production (Pichardo and Manousiouthakis, 2017), and systematic building block approach by Demirel et al. (2017). However, it is worth noting that the aforederived intensified designs often reduce the degrees of freedom for safe operation and process control due to high integrity, thus arousing concerns from process safety and operability perspective (Baldea, 2015). In this way, further attention is required to simultaneously synthesize intensified but safely operable systems. The objective of this paper is to introduce a systematic framework to deliver intensified designs through the phenomenological Generalized Modular Representation Framework
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(GMF), and at the same time, to guarantee their safety and operability performance by conducting risk assessment and model predictive controller design. The full framework (Figure 1a) and its detailed methodologies (Figure 1b) are described in section 2. In section 3, the framework is applied to heat exchanger network (HEN) synthesis for thermal intensification. A further application on hybrid reaction/separation processes is demonstrated through a reactive distillation case study in section 4.
2. Framework Description As shown in Figure 1a, the proposed framework consists of three interactive toolboxes (i.e., process intensification/synthesis toolbox, process simulation/optimization toolbox, and process operability/control/safety toolbox) to link steady-state synthesis, dynamic analysis, and operability assessment at the conceptual design stage. Each toolbox can accommodate different techniques implemented in multiple software environments, thus rendering this framework a desirable flexibility. Verifiable and operable process intensification designs are delivered as the outcome. The procedure followed for this purpose and specific techniques for each toolbox in the current work are presented below on a step-by-step basis (Figure 1b). Step 1: Process Intensification / Synthesis Representation – Generalized Modular Representation Framework (GMF), presented originally in Papalexandri and Pistikopoulos (1996), is utilized for process synthesis from phenomena level. The GMF uses aggregated multifunctional mass/heat exchange modules and pure heat exchange modules to represent chemical processes, conventional or not, by detecting mass and heat transfer feasibilities based on Gibbs free energy (Ismail et al., 2001). Thus, the discovery of intensified pathways can in principle be achieved by optimizing fundamental performance to overcome process bottlenecks, such as improving mass/heat transfer and/or shifting reaction equilibrium. Step 2: Process Optimization – A superstructure formulation is developed based on GMF to account for the large search space of intensification possibilities. The overall problem is formulated as a mixed integer nonlinear programming (MINLP) problem and solved using GAMS. Step 3: Process Operability / Safety Assessment – After obtaining the initial optimal design from the aforementioned steps, feasibility test is performed to evaluate its functionality under varying operating conditions. A multiperiod MINLP model is then formulated considering the detected critical operation points. Moreover, risk assessment is incorporated as a constraint into the synthesis model to indicate the inherent safety performance of the resulting design, accounting for consequence severity and equipment failure frequency (Nemet et al., 2017). Step 4: Optimal Intensified steady-state designs – Up to this point, optimal intensified designs satisfying flexibility and safety targets are derived from steady-state analysis. Step 5: Dynamic Simulation and Control – “High fidelity” dynamic model of the resulting PI designs determines the validity of dynamic analysis. This modeling task takes place in gPROMS® Process Systems Enterprise (1997-2017). The development of multi-parametric model-based predictive controller (mp-MPC) is performed through the application of the PAROC framework and software platform (Pistikopoulos et al., 2015). Its simultaneous design and control step gives optimal design via the formulation of a (mixed-integer) dynamic optimization problem (Diangelakis et al., 2017).
Framework for Synthesis of operable Process Intensification Systems
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Figure 1. Proposed framework for the synthesis of operable intensification systems (a) The systematic framework, (b) Methodology flow Step 6: Verifiable and Operable Process Intensification Designs – Closed-loop validation is performed to ensure the consistency throughout the framework, after which verifiable and operable intensification designs will be delivered as the outcome.
3. Heat Exchanger Network Synthesis Example The afore-described framework is first applied to investigate a heat exchanger network (HEN) synthesis problem for thermal intensification, aiming to demonstrate the steps of the proposed framework as an example from a broader set of intensified systems. 3.1. Problem Description This case study consists of two hot streams (H1, H2), two cold streams (C1, C2), and one hot utility (HU). Given are: (i) stream flowrate data (Kotjabasakis and Linnhoff, 1987); (ii) uncertain heat transfer coefficient (UH1-C1), for which the flexibility of the network is desired if this stream match is selected; (iii) disturbance and control objective, for which controller design is essential; and (iv) stream toxicity, represented by LC50 (i.e., lethal concentration, 50%) of the substance, and equipment data for four types of heat exchanger (HE), namely double pipe HE (DP), plate and frame HE (PF), fixed plate shell and tube HE (SF), and U-tube shell and tube HE (UT), which necessitates inherent safety evaluation (Nemet et al., 2017). The objective is to synthesize a HEN with minimized total annual cost as desired operability performance. 3.2. Steady-state Synthesis with Flexibility and Safety Considerations The pure heat exchange module of GMF is used here for HEN representation. The superstructure representation accounts for all potential stream matches and their corresponding bypass options. A nominal HEN design without flexibility or safety considerations is first performed to obtain a reference network (Figure 2a). Its risk value is calculated as per introduced in Section 2 Step 3. Two periods of operation are identified by the feasibility test as the extreme values of UH1-C1. To obtain an inherently safer design, risk tolerance is decreased by constraining: (i) overall HEN toxicity risk less than 75% of the nominal risk value, and (ii) individual HE toxicity risk less than 50% of overall risk. Setting the first risk tolerance results in the change of H1-C2 exchanger type from SF to UT (Figure 2b), as the latter one has much higher area density, leading to significant reduction in HE volumes and in the amount of intrinsic
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hazards. The risk level is further decreased by confining individual equipment risk so that none of the HEs is particularly risky. In this case, a different network is synthesized to render H1-C2 HE a lower risk level by relieving its heat exchange burden (Figure 2c). These two safely operable HENs separately serve as Candidate 1 and 2 for the next step dynamic analysis.
Figure 2. Topology of HEN at different operability level: (a) Nominal design, (b) Candidate 1, (c) Candidate 2 3.3. Dynamic Modelling and mp-MPC Controller Design The dynamic HEN is described by a Partial Differential Algebraic Equation (PDAE) model based on gPROMS® Process Model Library for Heat Exchange (PSE, 19972017). The network configuration is fixed for each candidate case study as per Figure 3b and 3c, and heat exchanger areas are used as design variables. To ensure the consistency going from steady-state synthesis to dynamic simulation, the dynamic model is validated to match its steady-state synthesis analogue. In this system, the bypass flowrate and the heat utility duties are the degrees of freedom, and the outlet temperature of stream H1 and C1 are the outputs. The inlet temperature of stream H2 is treated as a disturbance to the operation. The mp-MPC controller design, following PAROC framework (Pistikopoulos et al., 2015), takes place for the two candidates individually. Each mp-MPC problem is formulated using corresponding linear state-space model approximated by the System Identification Toolbox of MATLAB®. Via POP® toolbox in MATLAB®, the problem of Candidate 1 is solved for an output horizon of 2 and a control horizon of 2 resulting in 118 critical regions in solution map, while that of candidate 2 is solved for an output horizon of 2 and a control horizon of 1 resulting in 89 critical regions. Given random disturbances deviating within ±10 K per second, the designed controllers are tested against the “high fidelity” model for closed-loop validation, indicating the agreement between the outputs and setpoints.
Figure 3. Closed-loop validation of the controller against the high fidelity model: (a) Candidate 1, (b) Candidate 2
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The dynamic optimization problem is then formulated and solved for the minimal total annual cost under each HEN configuration. The obtained results are shown in Table 1. Up to this point, two HENs are designed with different levels of operability, control, and safety. While the final construction decision depends on the trade-off between desired operability behavior and economic performance, this framework demonstrates the potential for comparison of various operable design alternatives. Table 1. Optimization results for HEN example HE Area (m2) H1 – C1 H1 – C2 H2 – C1 Candidate 1 / 604.4 125.6 Candidate 2 221.4 522.2 /
H2 – C2 99.9 165.7
Investment Cost (k$) 528.4 557.4
Operating Cost (k$/yr) 7357.0 8593.9
4. Reactive Distillation Example In this section, the proposed framework is applied on hybrid separation/reaction systems to exploit the synergetic potential of this representative intensification system. The synthesis problem of classical methyl-tertiary-butyl-ether (MTBE) reactive distillation, originally presented in Ismail et al. (2001), is revisited herein for the purpose of demonstration, and to be further investigated for its operability performance. The design problem aims to produce MTBE bottom product at a rate of 197 mol/s and with the purity higher than 98 mol%. Two types of multifunctional mass/heat exchange modules (i.e., reaction/separation module and pure separation module) and the pure heat exchange modules in GMF are utilized to detect intensification possibilities with respect to the minimal operating and pseudo-capital cost. The optimal design is illustrated in Figure 4. The structure features two hybrid modules for reactive section, one separation module at the bottom separating unreacted methanol Figure 44. Optimal Fi O i l GMF ddesign i back to reactive section, and another of MTBE reactive distillation separation module at the top transferring n-butene to distillate. This result is consistent while slightly improved comparing to the literature results. In the dynamic analysis, the “high fidelity” dynamic rate-based model of MTBE reactive distillation column is adapted from Schenk et al. (1999), represented by sets of differential algebraic equations (DAE) of index 1. In this system, the design variables include column diameter, reboiler and condenser heat exchange areas. Reflux ratio, steam flow rate, and cooling water flow rate are the three degrees of freedom. Sinusoidal disturbance in the isobutylene inlet composition is introduced while product specification is to be satisfied via the design of mp-MPC controller.
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4. Conclusions In this paper, we present a systematic framework for the design of intensified system with guaranteed operability and safety performance. The framework is applied to two representative intensification systems (i.e., heat integration and reactive distillation). The results indicate that significant improvement can be achieved to exploit intensification opportunities by utilizing phenomena-based Generalized Modular Framework, and to enhance safety and control by performing corresponding assessment at the early design stage. The full framework enables to complete the whole story of synthesizing operable intensification systems. Future work will focus on applying the framework to more intensification systems, as well as zooming in the techniques employed in each toolbox.
5. Acknowledgements Financial supports from Texas A&M Energy Institute, Shell, and RAPID SYNOPSIS Project are gratefully acknowledged.
References M. Baldea, 2015. From process integration to process intensification. Computers & Chemical Engineering, 81, 104-114. S.E Demirel, J. Li, and M.F. Hasan, 2017. Systematic process intensification using building blocks. Computers & Chemical Engineering, 105, 2-38. N.A. Diangelakis, B. Burnak, J. Katz, and E.N. Pistikopoulos, 2017. Process Design and Control optimization: A simultaneous approach by multiǦparametric programming. AIChE Journal. S.R. Ismail, P. Proios, and E.N. Pistikopoulos, 2001. Modular synthesis framework for combined separation/reaction systems. AIChE Journal, 47(3), 629-649. E. Kotjabasakis, and B. Linnhoff, 1987. Flexible Heat Exchanger Network Design: Comments on the Problem Definition and on Suitable Solution Techniques. In IChemE Symposium" Innovation In Process Energy Utilization, 1-17. K.P. Papalexandri, and E.N. Pistikopoulos, 1996. Generalized modular representation framework for process synthesis. AIChE Journal, 42(4), 1010-1032. P. Pichardo, and V.I. Manousiouthakis, 2017. Infinite DimEnsionAl State-space as a systematic process intensification tool: Energetic intensification of hydrogen production. Chemical Engineering Research and Design, 120, 372-395. E.N. Pistikopoulos, N.A. Diangelakis, R. Oberdieck, M.M. Papathanasiou, I. Nascu, and M. Sun, 2015. PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chemical Engineering Science, 136, 115138. Process Systems Enterprise, 1997-2017. gPROMS. URL http://www.psenterprise.com/gproms/ A. Nemet, J.J. Klemeš, I. Moon, and Z. Kravanja, 2017. Safety Analysis Embedded in Heat Exchanger Network Synthesis. Computers & Chemical Engineering. https://doi.org/10.1016/j.compchemeng.2017.04.009 M. Schenk, R. Gani, D. Bogle, and E.N. Pistikopoulos, 1999. A hybrid modelling approach for separation systems involving distillation. Chemical Engineering Research and Design, 77(6), 519-534. A.K. Tula, D. Babi, J. Bottlaender, M.R. Eden, and R. Gani, 2017. A computer-aided softwaretool for sustainable process synthesis-intensification. Computers & Chemical Engineering, 105, 74-95. T. Van Gerven, and A. Stankiewicz, 2009. Structure, energy, synergy, time - The fundamentals of process intensification. Industrial & engineering chemistry research, 48(5), 2465-2474.
Towards a systematic framework for the synthesis of operable process intensification systems Yuhe Tiana, M. Sam Mannana,b, Efstratios N. Pistikopoulosa,c* a
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, United States b Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering Texas A&M University, College Station, TX 77843, United States c Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, United States [email protected]
Abstract In this work we present an integrated framework to derive intensified designs with guaranteed safety and operability performance. A step-by-step procedure is described featuring: (i) superstructure-based process synthesis using Generalized Modular Representation Framework, (ii) development of advanced multi-parametric control strategies following the PAROC framework and software platform, and (iii) inherent safety analysis at early design stage utilizing quantitative risk assessment. Two case studies with increasing intensification complexity, namely heat exchanger network synthesis and reactive distillation column design, are investigated to demonstrate the capabilities of the proposed framework. Keywords: Process intensification, Generalized Modular Representation Framework, multi-parametric Model Predictive Control, risk assessment
1. Introduction Modular process intensification (PI) offers the potential to drastically reduce the energy consumption and processing cost by involving multifunctional phenomena at different time and spatial scales (Van Gerven and Stankiewicz, 2009). In this context, phenomena-based synthesis tools, comparing to their conventional unit-operation-based counterparts, have attracted much interest as a potential means for automated generation of intensified options from a lower aggregation level without a pre-postulation of plausible flowsheets. Recent progress in this field includes, but not limited to, computer-aided software-tool for phenomena-based multiscale and multistage sustainable process synthesis – intensification by Tula et al. (2017), Infinite DimEnsionAl State-space (IDEAS) framework application on energetic intensification of hydrogen production (Pichardo and Manousiouthakis, 2017), and systematic building block approach by Demirel et al. (2017). However, it is worth noting that the aforederived intensified designs often reduce the degrees of freedom for safe operation and process control due to high integrity, thus arousing concerns from process safety and operability perspective (Baldea, 2015). In this way, further attention is required to simultaneously synthesize intensified but safely operable systems. The objective of this paper is to introduce a systematic framework to deliver intensified designs through the phenomenological Generalized Modular Representation Framework
2384
Y. Tian et al.
(GMF), and at the same time, to guarantee their safety and operability performance by conducting risk assessment and model predictive controller design. The full framework (Figure 1a) and its detailed methodologies (Figure 1b) are described in section 2. In section 3, the framework is applied to heat exchanger network (HEN) synthesis for thermal intensification. A further application on hybrid reaction/separation processes is demonstrated through a reactive distillation case study in section 4.
2. Framework Description As shown in Figure 1a, the proposed framework consists of three interactive toolboxes (i.e., process intensification/synthesis toolbox, process simulation/optimization toolbox, and process operability/control/safety toolbox) to link steady-state synthesis, dynamic analysis, and operability assessment at the conceptual design stage. Each toolbox can accommodate different techniques implemented in multiple software environments, thus rendering this framework a desirable flexibility. Verifiable and operable process intensification designs are delivered as the outcome. The procedure followed for this purpose and specific techniques for each toolbox in the current work are presented below on a step-by-step basis (Figure 1b). Step 1: Process Intensification / Synthesis Representation – Generalized Modular Representation Framework (GMF), presented originally in Papalexandri and Pistikopoulos (1996), is utilized for process synthesis from phenomena level. The GMF uses aggregated multifunctional mass/heat exchange modules and pure heat exchange modules to represent chemical processes, conventional or not, by detecting mass and heat transfer feasibilities based on Gibbs free energy (Ismail et al., 2001). Thus, the discovery of intensified pathways can in principle be achieved by optimizing fundamental performance to overcome process bottlenecks, such as improving mass/heat transfer and/or shifting reaction equilibrium. Step 2: Process Optimization – A superstructure formulation is developed based on GMF to account for the large search space of intensification possibilities. The overall problem is formulated as a mixed integer nonlinear programming (MINLP) problem and solved using GAMS. Step 3: Process Operability / Safety Assessment – After obtaining the initial optimal design from the aforementioned steps, feasibility test is performed to evaluate its functionality under varying operating conditions. A multiperiod MINLP model is then formulated considering the detected critical operation points. Moreover, risk assessment is incorporated as a constraint into the synthesis model to indicate the inherent safety performance of the resulting design, accounting for consequence severity and equipment failure frequency (Nemet et al., 2017). Step 4: Optimal Intensified steady-state designs – Up to this point, optimal intensified designs satisfying flexibility and safety targets are derived from steady-state analysis. Step 5: Dynamic Simulation and Control – “High fidelity” dynamic model of the resulting PI designs determines the validity of dynamic analysis. This modeling task takes place in gPROMS® Process Systems Enterprise (1997-2017). The development of multi-parametric model-based predictive controller (mp-MPC) is performed through the application of the PAROC framework and software platform (Pistikopoulos et al., 2015). Its simultaneous design and control step gives optimal design via the formulation of a (mixed-integer) dynamic optimization problem (Diangelakis et al., 2017).
Framework for Synthesis of operable Process Intensification Systems
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Figure 1. Proposed framework for the synthesis of operable intensification systems (a) The systematic framework, (b) Methodology flow Step 6: Verifiable and Operable Process Intensification Designs – Closed-loop validation is performed to ensure the consistency throughout the framework, after which verifiable and operable intensification designs will be delivered as the outcome.
3. Heat Exchanger Network Synthesis Example The afore-described framework is first applied to investigate a heat exchanger network (HEN) synthesis problem for thermal intensification, aiming to demonstrate the steps of the proposed framework as an example from a broader set of intensified systems. 3.1. Problem Description This case study consists of two hot streams (H1, H2), two cold streams (C1, C2), and one hot utility (HU). Given are: (i) stream flowrate data (Kotjabasakis and Linnhoff, 1987); (ii) uncertain heat transfer coefficient (UH1-C1), for which the flexibility of the network is desired if this stream match is selected; (iii) disturbance and control objective, for which controller design is essential; and (iv) stream toxicity, represented by LC50 (i.e., lethal concentration, 50%) of the substance, and equipment data for four types of heat exchanger (HE), namely double pipe HE (DP), plate and frame HE (PF), fixed plate shell and tube HE (SF), and U-tube shell and tube HE (UT), which necessitates inherent safety evaluation (Nemet et al., 2017). The objective is to synthesize a HEN with minimized total annual cost as desired operability performance. 3.2. Steady-state Synthesis with Flexibility and Safety Considerations The pure heat exchange module of GMF is used here for HEN representation. The superstructure representation accounts for all potential stream matches and their corresponding bypass options. A nominal HEN design without flexibility or safety considerations is first performed to obtain a reference network (Figure 2a). Its risk value is calculated as per introduced in Section 2 Step 3. Two periods of operation are identified by the feasibility test as the extreme values of UH1-C1. To obtain an inherently safer design, risk tolerance is decreased by constraining: (i) overall HEN toxicity risk less than 75% of the nominal risk value, and (ii) individual HE toxicity risk less than 50% of overall risk. Setting the first risk tolerance results in the change of H1-C2 exchanger type from SF to UT (Figure 2b), as the latter one has much higher area density, leading to significant reduction in HE volumes and in the amount of intrinsic
Y. Tian et al.
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hazards. The risk level is further decreased by confining individual equipment risk so that none of the HEs is particularly risky. In this case, a different network is synthesized to render H1-C2 HE a lower risk level by relieving its heat exchange burden (Figure 2c). These two safely operable HENs separately serve as Candidate 1 and 2 for the next step dynamic analysis.
Figure 2. Topology of HEN at different operability level: (a) Nominal design, (b) Candidate 1, (c) Candidate 2 3.3. Dynamic Modelling and mp-MPC Controller Design The dynamic HEN is described by a Partial Differential Algebraic Equation (PDAE) model based on gPROMS® Process Model Library for Heat Exchange (PSE, 19972017). The network configuration is fixed for each candidate case study as per Figure 3b and 3c, and heat exchanger areas are used as design variables. To ensure the consistency going from steady-state synthesis to dynamic simulation, the dynamic model is validated to match its steady-state synthesis analogue. In this system, the bypass flowrate and the heat utility duties are the degrees of freedom, and the outlet temperature of stream H1 and C1 are the outputs. The inlet temperature of stream H2 is treated as a disturbance to the operation. The mp-MPC controller design, following PAROC framework (Pistikopoulos et al., 2015), takes place for the two candidates individually. Each mp-MPC problem is formulated using corresponding linear state-space model approximated by the System Identification Toolbox of MATLAB®. Via POP® toolbox in MATLAB®, the problem of Candidate 1 is solved for an output horizon of 2 and a control horizon of 2 resulting in 118 critical regions in solution map, while that of candidate 2 is solved for an output horizon of 2 and a control horizon of 1 resulting in 89 critical regions. Given random disturbances deviating within ±10 K per second, the designed controllers are tested against the “high fidelity” model for closed-loop validation, indicating the agreement between the outputs and setpoints.
Figure 3. Closed-loop validation of the controller against the high fidelity model: (a) Candidate 1, (b) Candidate 2
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The dynamic optimization problem is then formulated and solved for the minimal total annual cost under each HEN configuration. The obtained results are shown in Table 1. Up to this point, two HENs are designed with different levels of operability, control, and safety. While the final construction decision depends on the trade-off between desired operability behavior and economic performance, this framework demonstrates the potential for comparison of various operable design alternatives. Table 1. Optimization results for HEN example HE Area (m2) H1 – C1 H1 – C2 H2 – C1 Candidate 1 / 604.4 125.6 Candidate 2 221.4 522.2 /
H2 – C2 99.9 165.7
Investment Cost (k$) 528.4 557.4
Operating Cost (k$/yr) 7357.0 8593.9
4. Reactive Distillation Example In this section, the proposed framework is applied on hybrid separation/reaction systems to exploit the synergetic potential of this representative intensification system. The synthesis problem of classical methyl-tertiary-butyl-ether (MTBE) reactive distillation, originally presented in Ismail et al. (2001), is revisited herein for the purpose of demonstration, and to be further investigated for its operability performance. The design problem aims to produce MTBE bottom product at a rate of 197 mol/s and with the purity higher than 98 mol%. Two types of multifunctional mass/heat exchange modules (i.e., reaction/separation module and pure separation module) and the pure heat exchange modules in GMF are utilized to detect intensification possibilities with respect to the minimal operating and pseudo-capital cost. The optimal design is illustrated in Figure 4. The structure features two hybrid modules for reactive section, one separation module at the bottom separating unreacted methanol Figure 44. Optimal Fi O i l GMF ddesign i back to reactive section, and another of MTBE reactive distillation separation module at the top transferring n-butene to distillate. This result is consistent while slightly improved comparing to the literature results. In the dynamic analysis, the “high fidelity” dynamic rate-based model of MTBE reactive distillation column is adapted from Schenk et al. (1999), represented by sets of differential algebraic equations (DAE) of index 1. In this system, the design variables include column diameter, reboiler and condenser heat exchange areas. Reflux ratio, steam flow rate, and cooling water flow rate are the three degrees of freedom. Sinusoidal disturbance in the isobutylene inlet composition is introduced while product specification is to be satisfied via the design of mp-MPC controller.
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4. Conclusions In this paper, we present a systematic framework for the design of intensified system with guaranteed operability and safety performance. The framework is applied to two representative intensification systems (i.e., heat integration and reactive distillation). The results indicate that significant improvement can be achieved to exploit intensification opportunities by utilizing phenomena-based Generalized Modular Framework, and to enhance safety and control by performing corresponding assessment at the early design stage. The full framework enables to complete the whole story of synthesizing operable intensification systems. Future work will focus on applying the framework to more intensification systems, as well as zooming in the techniques employed in each toolbox.
5. Acknowledgements Financial supports from Texas A&M Energy Institute, Shell, and RAPID SYNOPSIS Project are gratefully acknowledged.
References M. Baldea, 2015. From process integration to process intensification. Computers & Chemical Engineering, 81, 104-114. S.E Demirel, J. Li, and M.F. Hasan, 2017. Systematic process intensification using building blocks. Computers & Chemical Engineering, 105, 2-38. N.A. Diangelakis, B. Burnak, J. Katz, and E.N. Pistikopoulos, 2017. Process Design and Control optimization: A simultaneous approach by multiǦparametric programming. AIChE Journal. S.R. Ismail, P. Proios, and E.N. Pistikopoulos, 2001. Modular synthesis framework for combined separation/reaction systems. AIChE Journal, 47(3), 629-649. E. Kotjabasakis, and B. Linnhoff, 1987. Flexible Heat Exchanger Network Design: Comments on the Problem Definition and on Suitable Solution Techniques. In IChemE Symposium" Innovation In Process Energy Utilization, 1-17. K.P. Papalexandri, and E.N. Pistikopoulos, 1996. Generalized modular representation framework for process synthesis. AIChE Journal, 42(4), 1010-1032. P. Pichardo, and V.I. Manousiouthakis, 2017. Infinite DimEnsionAl State-space as a systematic process intensification tool: Energetic intensification of hydrogen production. Chemical Engineering Research and Design, 120, 372-395. E.N. Pistikopoulos, N.A. Diangelakis, R. Oberdieck, M.M. Papathanasiou, I. Nascu, and M. Sun, 2015. PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chemical Engineering Science, 136, 115138. Process Systems Enterprise, 1997-2017. gPROMS. URL http://www.psenterprise.com/gproms/ A. Nemet, J.J. Klemeš, I. Moon, and Z. Kravanja, 2017. Safety Analysis Embedded in Heat Exchanger Network Synthesis. Computers & Chemical Engineering. https://doi.org/10.1016/j.compchemeng.2017.04.009 M. Schenk, R. Gani, D. Bogle, and E.N. Pistikopoulos, 1999. A hybrid modelling approach for separation systems involving distillation. Chemical Engineering Research and Design, 77(6), 519-534. A.K. Tula, D. Babi, J. Bottlaender, M.R. Eden, and R. Gani, 2017. A computer-aided softwaretool for sustainable process synthesis-intensification. Computers & Chemical Engineering, 105, 74-95. T. Van Gerven, and A. Stankiewicz, 2009. Structure, energy, synergy, time - The fundamentals of process intensification. Industrial & engineering chemistry research, 48(5), 2465-2474.