Plantwide Control of the Formic Acid Production ... - ACS Publications

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Cite This: Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Plantwide Control of the Formic Acid Production Process Using an Integrated Framework of Simulation and Heuristics Dipesh S. Patle,*,§ Akhil Premkumar Gadhamsetti,† Swapnil Sharma,† Venu Agrawal,† and G. P. Rangaiah†,‡ §

Chemical Engineering Department, Motilal Nehru National Institute of Technology, Allahabad, 211 004, Uttar Pradesh, India Chemical Engineering Department, School of Civil and Chemical Engineering, VIT University, Vellore 632014, Tamilnadu, India ‡ Department of Chemical & Biomolecular Engineering, National University of Singapore, Engineering Drive 4, Singapore, 117585 Downloaded via UNIV OF SUNDERLAND on October 2, 2018 at 23:33:48 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

†

S Supporting Information *

ABSTRACT: Formic acid (FA) is an essential chemical, but its process design and control have not received much attention in the literature. This contribution describes the design of a plantwide control (PWC) structure for the intricate FA process having reactive distillation (RD), multiple liquid and gas recycles, as well as a snowballing effect. In this process, methyl formate (MF) is produced from methanol (MA) and carbon monoxide (CO), and then it undergoes a hydrolysis reaction in RD to produce FA and also MA for the first reaction. The presence of an RD in addition to a continuous stirred tank reactor (CSTR) coupled with three recycles makes control of the FA process interesting and challenging. This work employs a systematic method for PWC design, namely, an integrated framework of simulation and heuristics (IFSH), which is easy to implement and uses process simulators as well as heuristics in PWC design. The performance of the developed PWC structure was evaluated for various disturbances, using several criteria recommended for the evaluation of PWC design. Results of dynamic simulations and these performance evaluations show that the proposed PWC system is able to reject the tested disturbances efficiently, while meeting FA purity and production requirements. PWC for the two-step complex FA process is studied for the first time in this contribution. recently,5,9 its process control has not been studied in the open literature. Plantwide control (PWC) design means the development of a control system for interconnected unit operations in chemical plants with material and energy recycles. Process design has a direct impact on the process controllability. Therefore, it can be beneficial to address process design and control aspects in an integrated framework, especially for new processes. However, this adds up to the complexity, particularly for large complex processes. Recently, Mansouri et al.10 studied an integrated process design and control of RD processes through a computer-aided framework, where they showed that designing the RD process at the maximum driving force results in a feasible and reliable process design as well as the control system. In another study,11 they addressed integrated process design and control of RD unit involving multiple elements through a computer-aided hierarchical decomposition-based framework. They showed that the design at the

1. INTRODUCTION Formic acid (FA) finds widespread applications in various fields such as chemical, pharmaceutical, textile, rubber, and leather.1 Being biodegradable and environmentally sustainable, it is an attractive feedstock for producing various desired chemicals.1 Out of many FA production processes such as acidolysis of formate salts, oxidation of hydrocarbons, hydrolysis of formamide, mineral acid catalysis, and hydrolysis of lower alkyl formates,2−4 the most efficient with respect to less reaction time is the hydrolysis of methyl formate (MF).4 This process has two steps, namely, (1) carbonylation reaction of carbon monoxide (CO) and methanol (methyl alcohol, MA) to produce MF, and (2) hydrolysis of MF to produce the FA product and MA for the first reaction. This conventional FA process has some disadvantages such as unwanted side products, larger reaction time, ecological issues, and high investments.5,6 Huang et al.7 proposed a process by integrating the continuous stirred tank reactor (CSTR) and distillation column into a single reactive distillation (RD) unit for MF hydrolysis. This configuration is advantageous in terms of (1) higher yield and better selectivity, (2) reduction in energy required, and (3) avoidance of hotspots.8 Although FA is an important chemical and its design has been studied only © XXXX American Chemical Society

Received: Revised: Accepted: Published: A

June 15, 2018 September 8, 2018 September 15, 2018 September 15, 2018 DOI: 10.1021/acs.iecr.8b02654 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Industrial & Engineering Chemistry Research

Figure 1. Formic acid production process taken from ref 5; X denotes mass fraction of components [xCO, xMF, xMA, xH2O, xFA].

that the economic operation was achieved when the process was driven to constraints active at the optimum. Jonesetal.21 developed a model-based approach for primary controlled variables (CVs) selection in a PWC system and applied it to an acid gas removal unit, which was part of an integrated gasification and combined cycle power plant with CO2 capture. Psaltis et al.22 proposed a PWC system for the vinyl-acetate monomer plant; it had a 9 × 9 regulatory control structure that was implemented on the nonlinear model using decentralized PI controllers. Patle et al.23 proposed the PWC for homogeneously catalyzed biodiesel production from waste cooking oil using IFSH methodology and demonstrated that the developed control system is able to provide stable performance for large disturbances. Ojasvi and Kaistha24 studied a decentralized PWC of a conventional reaction−separation process for continuous production of mono-isopropyl amine through catalytic amination of isopropanol. Simulation results showed that the oversized reactor design was operable using traditional decentralized plantwide control for a large production rate and feed composition changes. Peng and Wang25 studied PWC of the n-butyl acetate process. They proved the robustness of the control system by tackling the disturbances of feed flow rate and feed composition. Sharma et al.26 proposed a decentralized PWC structure including a single-point temperature control (inferential) in the RD column, where a fresh naphtha feed was the throughput manipulator; it provided effective process regulation for large changes in throughput, thiophene composition in fresh naphtha, and feed hydrogen composition. Regalado-Méndez et al.27 designed and implemented an economical PWC for biodiesel production involving RD and obtained satisfactory performance for disturbances and set-point changes. Thakur et al.28 developed a PWC for continuous di-isobutylene manufacturing via catalytic liquid-phase dimerization of isobutylene in the C4

maximum driving force can be controlled using controllers such as a proportional-integral (PI) and model predictive controller. In the context of PWC, recycles may change the transients of the plantwide process due to the introduction of an integrating effect, which may result in a snowball effect and consequently challenges in process control. PWC methodologies can be classified as heuristics, optimization, mathematical, and mixed approaches; see Vasudevan et al.12 for a review of methodologies and applications. Rigorous mathematical and optimization-based methodologies demand substantial computations, particularly when dealing with intricate processes, and their solution depends on the extent of detail considered. On the other hand, heuristic-based methodologies involve fewer computations and are easy to apply. One widely used heuristic-based PWC method is a nine-step procedure proposed by Luyben et al.,13 which considers the significance of control and the operational objectives. In general, limitations of heuristic-based methods are that they depend on a user’s experience. With current advances in rigorous simulators, it is feasible and beneficial to use them in PWC design. Process simulators can assist in deciding suitable controller pairings from a PWC perspective. One such method that makes effective use of heuristics and simulators is the integrated framework of simulation and heuristics (IFSH) methodology.14,15 Another PWC methodology is the economic PWC.16−18 However, the IFSH methodology is easy to apply and requires fewer computations. In recent years, Hung et al.19 illustrated an approach for synthesizing an effective control structure for PWC for three processes with RD and large recycle flow rates: hydrolysis, transesterification, and two-stage esterification. Bildea and Kiss20 developed a PWC structure for the biodiesel process by reactive absorption. Jagtap et al.17 studied economic optimum operation of a recycle process with a side reaction and showed B

DOI: 10.1021/acs.iecr.8b02654 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Article


liquid state (at 107 °C and 45 bar). Purge-1 from C1 is small and mainly contains CO. FA and MA production by the hydrolysis of MF (eq 2) is carried out in the RD column having 35 stages. Fresh water is fed to this column on the second stage (trays are numbered consecutively from top to bottom including condenser and reboiler) and MF from the C1 column goes on the 33rd stage.

feedstock. Moraru and Bildea29 developed a PWC system for a conventional reactor−separation−recycle system for n-butyl acrylate production; the developed control system was tested for disturbances in production capacity and operating conditions. It is clear from the review paper12 and the recent literature outlined above that, although PWC has been investigated by researchers, PWC of the commercially important two-step FA process has not yet been studied. Therefore, this study applies IFSH methodology to design and evaluate a PWC system for the complex FA process studied recently by our group.5 As the process design was reported earlier, here we studied PWC of this complex process. The presence of an RD in addition to a CSTR, three recycles, and snowballing in this process demand systematic development of an effective PWC system. Outcomes of the PWC system designed using IFSH methodology is evaluated for several disturbances, and the obtained results are discussed. The rest of this article is structured as follows. The FA process is discussed in Section 2. Section 3 discusses the design of PWC system for the complex FA process. The performance assessment of the proposed control structure for several disturbances is presented in Section 4. This assessment is through transient profiles and quantitative criteria, namely, settling time based on both production rate and overall accumulation, deviation from the production target (DPT), overall total variation (TV) in manipulated variables (MVs), and dynamic disturbance sensitivity (DDS). At the end, the article is concluded by summarizing the findings of this study.

HCOOCH3 + H 2O → HCOOH + CH3OH

(2)

The reactants (MF and water) are separated from the products (FA and MA) simultaneously with FA and MA production in the RD, which enhances both the reaction rate and the separation. The RD column includes reactive stages (2nd to 19th) and stripping section below the reactive section. Distillate of RD (Purge-2) is small and mostly contains CO, whereas the bottoms stream contains MA, FA, and unreacted water, which is sent to C2 for further separation. MA is separated as the distillate of C2 for recycling to the CSTR, whereas C2 bottoms is the desired product of 85 wt % FA (72 mol %) at a flow rate of 120.2 kmol/h. Important operating data are given in Figure 1. Aspen Plus V8.8 and Aspen Plus Dynamics (APD) V8.8 were used for the steady state simulation and the dynamic simulation of the FA process, respectively. The UNIQUAC-HOC model was selected for thermodynamic calculations.5,30 This model was used as it takes into account the strong association and solvation effects. The UNIFAC, i.e., UNIQUAC functional-group activity coefficient, method was used to estimate the missing binary parameters in the UNIQUAC model. Kinetics of the carbonyl reaction are taken from Bai,31 which are valid for 60−110 °C and 2−4 MPa. Kinetics of MF hydrolysis occurring in the RD are taken from Wang32 and were converted to the units (from m6·kmol−l·h−1·kg (cat)−1 to m3 s−1 kmol−1) acceptable for Aspen Plus simulation. For this, catalyst holdup on each stage was taken as 0.3 m3 and catalyst density as 652 kg/m3. Steady state simulation results, such as conversion and product purity, were validated against earlier papers. After the process was simulated in the steady state, it is exported to the dynamic mode by providing equipment dimensions, placing required valves and satisfying pressure balances. Balancing the pressure profile is crucial as pressure-driven dynamic simulation is carried out in this work to obtain realistic results. The pressure driven mode of simulation is based on a pressure-flow solver, where pressure depends on upstream conditions. Although APD provides a flow driven mode also, pressure driven simulation is realistic as it includes the effects of control valve sizing, pump sizing, and rangeability. Simulation in the dynamic mode was initialized at the steady state values. Typically, a pressure drop of 3−4 bar was provided in each control valve to obtain good control.33 Later, controllers were added as mentioned in the next section. Temperature and composition measurements include a time delay of 1 and 3 min, respectively.33

2. FORMIC ACID PROCESS AND ITS SIMULATION The FA process and its design details used in the present study are taken from our recent work,5 which was based on the process developed by Huang et al.7 This process includes a reactor for carbonyl reaction, one RD column for MF hydrolysis, and two distillation columns for separation of hydrolysis products. The merit of this process, shown in Figure 1, is that it combines hydrolysis and separation units into an RD column to produce FA, thus reducing the number of units and footprint of equipment. Sharma et al.5 simulated the process for processing 70.3 kmol/h of CO. In this work, this process is redesigned for CO feed of 100 kmol/h. Other than the flow rate, the process flow diagram, CSTR operating conditions, and equipment design are similar to those presented in Sharma et al.5 Major changes in the process are as follows. C2 is operated at 1.03 bar instead of 4 bar, to obtain better separation. Fresh water is preheated in the heat exchanger “HE” with the FA product from C2. The reactor temperature is 100 °C instead of 75 °C to obtain a higher conversion. A cooler is included before the flash operation, and it is then used for control of the flash temperature. All equipment are resized for increased CO flow rate. Fresh CO is given to the CSTR at a flow rate of 100 kmol/h (Figure 1). Recycled MA along with a small amount of makeup is also fed to this adiabatic CSTR, operating at 100 °C and 40.53 bar. In this reactor, MF is produced from MA and CO by the carbonyl reaction: CH3OH + CO → HCOOCH3 (1)

3. PWC SYSTEM DESIGN In the present study, the PWC structure for the FA plant was designed using the IFSH method that was developed by Konda etal.14 IFSH methodology is methodical and hierarchical having a total of eight levels. It utilizes a rigorous simulator in conjunction with the heuristics in PWC structure design, thereby avoiding the overdependence on heuristics.23 For sizing CSTR and reflux drum and sump of distillation columns and RD, the length to diameter ratio was assumed to be 2:1.

Unconverted CO is recovered in the flash and is recycled to the CSTR via a compressor, in the vapor state (at 60.5 °C and 40.53 bar). On the other hand, MA is partly recovered in the bottoms of the C1 column for recycling to the CSTR in the C


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Industrial & Engineering Chemistry Research Scheme 1. Eight Levels of IFSH Methodology for PWC Design

Table 1. Expected Disturbances in the FA Process and Their Influence on Crucial Parameters disturbance number with details D1 (+5% in CO flow rate) D2 (−5% in CO flow rate) D3 (−10% in the pre-exponent factor of kf in carbonylation reaction) D4 (D2 and D3 simultaneously) D5 (+10% in CO flow rate) D6 (−10% in CO flow rate) D7 (+20% in CO flow rate) D8 (−20% in CO flow rate)

Δ (FA flow rate), %

Δ (vapor recycle from flash),%

Δ (overall conversion of CO), %

Δ (overall formation of MF), %

Δ (liquid MA recycle 1), %

Δ (liquid MA recycle 2), %

Δ (overall conversion of MF), %

+4.957 −4.953 0

+8.524 −8.083 +17.679

−0.565 +0.550 −2.909

+4.597 −4.554 +0.767

+4.957 −4.954 0.000

+4.918 −4.914 0.000

+0.37 −0.404 0.000

−4.954 +9.917 −9.904 +19.84 −19.79

+8.284 +17.48 −15.722 +36.698 −29.611

−2.282 −1.147 +1.077 −2.344 +2.047

−3.840 +9.235 −9.052 +18.607 −17.833

−4.954 +9.916 −9.904 +19.836 −19.799

−4.914 +9.840 −9.824 +19.691 −19.619

−0.404 +0.709 −0.848 +1.311 −1.886

Volume of CSTR is determined based on the residence time required. The volumetric flow rate (m3/min) was multiplied with 10 (considering the inventory for 10 min) to yield a total volume of reflux drum and sump of distillation columns and RD. Required tray hold up on the reactive trays in RD was found to be 0.3 m3.5 The tray/pack sizing option in Aspen Plus was used for the column diameter calculations, where the column diameter was determined based on maximum vapor velocity. Levels of IFSH methodology are presented in Scheme

1, and their implementation for the FA process is discussed below. Level 1.1: Define Plantwide Control Objectives. This level deals with the formulation of PWC objectives, typically, product purity, production rate, constraints associated with process or equipment, safety issues, and environmental norms, from the operational perspective. In the case of divergence between plantwide objectives and unit-wise objectives, priority is given to the former. It is important to define the objectives right at the start as different objectives can result in a different D


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Figure 2. Designed PWC system for the FA production process; reflux flow rate is flow controlled in C1 and C2, but these two controllers are not shown in the figure for clarity.

control structure. For the present FA process, control objectives are (1) to obtain the desired production rate under normal operation; (2) to achieve quick and smooth control in rejecting the disturbances; and (3) to maintain the desired product purity despite the disturbances. Level 1.2: Determine the Number of Control Degrees of Freedom (CDOF). Following the method proposed by Konda et al.,34 CDOF is calculated depending on the number of restraints. In this simple method, the total number of mass and energy streams in the process is first counted. Then, the number of restraints and number of redundancies are subtracted from the total number of mass and energy streams in the process to calculate the CDOF. As defined by Konda et al.,34 the restraining number of a unit is the number of overall mass balances (independent) without inventory, and the number of redundancies in a process is the number of variables that are not required to be manipulated. In the given FA process, there are 69 streams, 30 restraints, and 13 redundancies. Hence, CDOF = 69 − 30 − 13 = 26. Details of CDOF calculation are given in Table S1 of the Supporting Information. Level 2.1: Identify and Analyze Plantwide Disturbances. Disturbances during the operation of the plant should be effectively controlled. Hence, prior knowledge of potential process disturbances is crucial in PWC design. A steady state simulation is used in this step of IFSH methodology to identify important disturbances and to investigate their effects. Table 1 presents the possible disturbances during FA process operation and their influence on the crucial parameters. In this work, we have considered up to ±20% in CO flow rate and −10% change in carbonylation kinetics. MA and water flow rate were varied accordingly. The values in Table 1 are percentage changes with respect to the steady state. They are useful in

assessing the snowball effect, if any. As it can be seen from Table 1, disturbances D1−D8 produce more than proportionate changes in the vapor recycle from the flash. This indicates the presence of snowballing in the process, which is due to the vapor recycle. However, disturbances D1−D8 produce nearly proportionate changes in the overall formation of MF and FA. Also ,the change in the overall conversion of CO and of MF is not much. As noted earlier, the FA process has the following attributes: (1) three recycle streams (one gas recycle and two liquid recycles); (2) two step reaction (carbonylation reaction to produce MF and hydrolysis of MF to produce FA); and (3), and one of the reactants, i.e., MA, is produced in the hydrolysis step. These attributes along with snowballing make the control of this process interesting and challenging. The performance of the developed PWC scheme for the disturbances in Table 1 is presented and discussed in Section 4. Level 2.2: Set Performance and Tuning Criteria. Settling time is considered as the control performance evaluation criterion in this level since it will decide the time the process would take to settle to a new throughput or to reach the steady state in the presence of disturbances. As performance assessment criteria have a significant impact on structural or/and parametric decisions, it is logical to consider this level before any such decisions are made. As a process has generally many control loops, every controller should be properly tuned once control loops are recognized. Guidelines given by Luyben33 were used for tuning flow and pressure controllers. The Autotuner in APD was used to tune all controllers. A closed-loop autotune variation method in APD was used for tuning the individual controllers with time delays, i.e., all composition and temperature control loops.35 Tuning E


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Column Temperatures. Temperatures in the distillations columns, namely, C1, RD, and C2, should be controlled at their set points to obtain the required products. These temperatures may vary in a reasonable range. The controller gets active as the CV (i.e., temperature) hits the upper/lower limit; this was done to avoid unnecessary action by the controller for insignificant changes in the temperature. In any case, if the composition deviates from the respective set points, the set point of temperature controllers is given remotely by respective composition controllers to control the compositions. Reasons for the CV−MV pairing for all units are mentioned in the Supporting Information (in Table S2). Heuristics and simulations are suitably used in making these pairing decisions. In addition, RGA is also used for deciding CV−MV pairings for the distillation column control. Flash Pressure. Pressure control in flash operation in the FA process is challenging. This is because it is controlled using vapor outlet from the flash, which is returned back to the CSTR, and it shows snowballing, as noted in Section 2.1. The vapor flow rate from the flash was used as a manipulated variable to control pressure in the flash. This strategy worked well for the studied disturbances D1−D8. Level 4.2: Selection of Manipulator for Less Severe Controlled Variables. This step addresses the level and (remaining) pressure controllers in the process. Levels need to be controlled while ensuring that those in the primary process path are self-consistent.23 Level control is critical as it has an integrating nature. For a stable control, the level control before the TPM has to be in the opposite direction of the flow, and the level control after the TPM has to be in the direction of the flow.23 As a fresh CO feed is chosen as the TPM in this work, the levels have to be controlled in the direction of the flow. A strict level control in CSTR is essential as it has a direct influence on the reaction rates,33 and so the PI controller is employed for level control in CSTR. A good level control in FLASH operation and in distillation is also desired. The level in flash operation was maintained by manipulating the liquid outlet flow rate. In distillation columns “C1” and “C2”, the reflux drum levels and column base levels were controlled by manipulating the distillate flow rates and bottoms flow rates, respectively. In RD, the reflux drum level was controlled by manipulating the reflux flow rate as it has total reflux, and the column base level was maintained using the bottoms flow rate. In this way, the disturbances are directed away from the process path as suggested by the heuristics. In this work, the reflux flow rate in “C1” and “C2” was kept constant by default. Maintaining/manipulating the reflux ratio or reflux-to-feed (R/ F) ratio was not warranted as constant reflux flow yielded satisfactory results. The pressure in each of the three distillation columns (including RD column) was controlled using the respective condenser duty using PI controllers. Level 5: Control of Unit Operations. Control of individual unit operations is dealt with in this step before examining material balances. By completing this step, some of the component inventory loops may be implicitly taken care of, which makes the analysis in the next level easier. Basic control of the most common processes is described in Luyben.33 All level, pressure, and temperature control loops in the FA process have already been fixed in the earlier stages. Level 6: Check Component Material Balances. For a stable PWC structure, it is vital that the component inventory is well controlled. The accumulation rate of each component in the entire process should be zero or negligible, which means

parameters for such controllers were calculated using the method of Tyreus−Luyben (TL).36 Tuning of the remaining controllers was carried out using the open-loop Cohen and Coon (CC) tuning method in APD. Values of all controller parameters are mentioned in the Supporting Information (see Table S2). Level 3.1: Production Rate Manipulator Selection. This level deals with the determination of the primary process path to produce the main product from the main raw material. In the FA process, there are two main feeds, namely, CO and water. CO enters the process much before (i.e., at the start) water as the former is first converted to MF, which then reacts with water to produce FA in RD. Further, the CO feed was found to have a larger gain (of 0.98) as compared to the gain with respect to water (of 0.48) for producing FA. Hence, the primary process path is from CO to FA. After the primary process path was identified, internal variables on this primary path are generally chosen as throughput manipulators (TPM) over external variables.37 The choice of an appropriate TPM is vital for the process to react to the disturbances in the most effective manner. The TPM can be selected based on a steady state simulation results. Logically, a process variable with a maximum steady state gain is the preferred choice for the TPM. An internal variable such as CSTR temperature can be used as a TPM. As the CSTR temperature should be maintained to obtain maximum MF in the CSTR and the reactor/RD should be operated at the optimal conditions to obtain the maximum product, internal variables such as CSTR temperature are not preferred in this study. Instead, the CO feed flow rate is chosen as the TPM. As the FA process has two steps, a change in CO causes a change in MF. Later, the water feed flow rate in RD needs to be manipulated proportionately to convert MF into FA. Level 3.2: Product Quality Manipulator Selection. This level addresses the selection of the MV for maintaining the product purity. Maintaining FA purity is one of the major objectives in the PWC system design. Although the product purity is generally a local decision, it should be addressed before other plant wide decisions as it is of prime importance. Here, FA purity in the bottoms product from the C2 column is controlled using the composition controller “CC103” (Figure 2), which is cascaded to a temperature controller “TC104”; this means that CC103 will change the set point of TC104 in the case of deviation in the FA purity. Level 4.1: Selection of Manipulators for More Severe Controlled Variables. Constraints associated with the equipment, stability, operation, safety, and environment are dealt with in this level. CSTR Temperature. To sustain the CO conversion in the CSTR, the temperature has to be controlled at the set point. APD has various methods for heat transfer in reactor temperature control, viz. the log mean temperature difference (LMTD), constant duty, constant temperature, etc. The LMTD approach was used in this work to obtain practical outcomes. The overall heat-transfer coefficient (U) was assumed to be 0.44 kW/(m2 K). Cooling water (CW) was assumed to enter at 25 °C and leave at 45.8 °C at the initial steady state. Specific heat of the CW is taken as 4.2 kJ/kg K. The area (A) for heat transfer, considering 50% of the reactor surface area, was 30 m2. Accordingly, the required CW flow rate was calculated to be 35 669 kg/h. F


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Figure 3. Accumulation profiles with and without all three recycles, due to (a) D5 and (b) D6.

the inventory is well-regulated. The overall accumulation should therefore be calculated and monitored for all the components in the plant. Developing an effective control system for the complex systems is tough due to the number of recycles present. Here, component material balances have been checked by preparing a “Downs Drill” table, which is provided as Table S3 in the Supporting Information. It was found that the component balances were satisfied. Level 7: Effects of Integration. In this important step after all the above steps have been completed, the dynamics of the process are studied for both with and without recycle.14 Processes with recycles may exhibit slower dynamics or unstable dynamics at times. If it is not the case, it can be inferred that the recycle dynamics are not critical. This step is performed by calculating the overall accumulation of all components, i.e., CO, MA, MF, H2O, and FA, in the entire process and studying the effect of recycles on critical parameters such as production rate and conversion. From the dynamic simulations of the FA process with or without all three recycles for disturbances D5 and D6, it can be seen from Figure 3 that the settling time did not differ much with and without recycles. Hence, it is inferred that the process dynamics were not radically affected. Also, conversion of CO and MF, and product formation were largely unchanged when the recycles were open or closed. This can be attributed to the composition controllers (i.e., CC100, CC101, CC102, and CC103) cascaded to temperature controllers (i.e., TC100, TC102, TC103, and TC104, respectively). Level 8. Enhancing Control System Performance with Remaining CDOF. As warranted, the control structure can be modified to further improve its performance. For the entire FA plant, the designed PWC system was found to yield satisfactory control of the entire process, warranting no further modifications. The final PWC system obtained by applying the IFSH methodology uses 22 of the 26 CDOF. As noted in our earlier study,38 valve openings deviate slightly from the initial value. In the FA process too, valve openings deviated within ±5% from 50%. Note that the present study uses pressure-driven simulation, to obtain realistic simulation results.

Figure 4. Profiles of the FA production for D1−D8.

Table 2. Performance Results of the Developed PWC Scheme for the FA Process criterion settling time (h) based on disturbance D1 (+5% in CO flow rate) D2 (−5% in CO flow rate) D3 (−10% in the preexponent factor of kf in carbonylation reaction) D4 (D2 and D3 simultaneously) D5 (+10% in CO flow rate) D6 (−10% in CO flow rate) D7 (+20% in CO flow rate) D8 (−20% in CO flow rate)

production rate

DDS

TV

DDS

9.08

4.41

+550

62

29

9.19

5.15

−488

53

22

0.21

−38

24

17

8.17

5.11

−461

51

23

9.31

5.1

+1351

159

48

9.75

6.3

−969

103

31

11.27

20.14

+1716

407

70

12.92

12.04

−2254

209

53

13.6

DPT

summarizes the CV and MV pairings, reason for the selection of MV, controller parameters (Kc and τi), and respective valve opening (in %) at steady state. The performance of the developed PWC structure was investigated for disturbances D1−D8. Performance evaluation of PWC systems requires special criteria as the criteria for single-loop controller performance evaluation such as the integral of squared error, rise time, and peak time, are not sufficient. The PWC system

4. ASSESSMENT OF THE DESIGNED CONTROL SCHEME The PWC structure for the entire FA process has 26 controllers, including 4 cascade schemes (Figure 2), which use 22 CDOF. Table S2 in the Supporting Information G


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Figure 5. Deviation from production target for (a) disturbances D5 and D6, and (b) disturbances D7 and D8.

then allowed reach steady state again; this took 3.9−11.8 h depending on the disturbance introduced. From the obtained results, the above-mentioned criteria were computed and analyzed. The profiles of FA production rate for D1−D8 are presented in Figure 4. The process is disturbed at 2 h by the introduced disturbance and smoothly reaches to a new steady state, i.e., throughput. The settling time increases with the extent of disturbance. For all disturbances tested, the FA process settled between 8 and 13 h, excluding 2 h before the disturbance was introduced. Table 2 shows the outcomes of the designed PWC system for the FA process for D1−D8. Settling time was computed based onthe production rate as well as DDS, i.e., accumulation. DPT was calculated from the difference between the curves for PT and PA. A lesser DPT is desired as it denotes a lower economic loss. The overall TV was calculated by adding the TV of all MVs, which are in percentages, for example, change in valve positions. It is pertinent to consider these in percentages as computing the overall TV based on actual units of TV would be inconsistent in terms of both dimensions and magnitudes. DDS was found considering the accumulation of each and every component. In general, settling time, DPT, TV, and DDS values are larger for greater throughput changes. Settling time, DPT, TV, and DDS are the largest for D7 and D8, owing to the large throughput changes of +20% and −20%, respectively (Table 2). Settling time based on the production rate for all disturbances, except D3, D7, and D8, was about 10 h. On the contrary, settling time based on DDS for all disturbances, except D3, D7, and D8, was about 5 h, whereas D7 and D8 showed a relatively larger settling time of 20 and 12 h, respectively. D3 showed the least settling time based on DDS due to no change in the throughput. DPT, TV, and DDS were more for D7, whereas they were the least for D3. Generally, positive disturbances (i.e., D1, D5, and D7) showed greater values of DPT, TV, and DDS as compared to those for negative disturbances (i.e., D2, D6, and D8). This is because it is usually difficult to control the throughput changes toward increasing capacity. Figure 5 shows how the actual production rate tracks the target production rate for four disturbances: D5 and D6 in plot 5a, and for D7 and D8 in plot 5b; it is shown only for these disturbances for brevity. Product deviation for shorter duration is desired for better PWC of the process. For disturbances D5−D8, the actual production rate reaches the target within about 8 h. Overall accumulation profiles for all disturbances are illustrated in Figure 6. This figure clearly indicates that the

Figure 6. Accumulation profiles due to D1, D2, D3, D4, D5, D6, D7, and D8.

Figure 7. Profiles of FA purity in the presence of tested disturbances.

should be evaluated from the perspective of an overall plant and not just from the perspective of individual units. At the same time, the criteria for PWC system assessment should be comprehensive, easy, and reliable. In this work, various criteria proposed by Vasudevan and Rangaiah,39 namely, settling time of FA production rate (which has a direct impact on profitability) and overall accumulation (which is concerned with the safe operation of the process as it deals with overall accumulation), deviation from the production target (DPT, which is related to the economic interest), the total variation in MVs (TV, which quantifies control valve moves), and DDS (which quantifies build up of components in the plant), are used. These criteria are described in the Supporting Information. Initially, the process was allowed to run for 2 h, and subsequently a disturbance was introduced. The process was H


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Figure 8. continued

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Figure 8. (a) Level control in the C1 reflux drum, (b) level control in the C2 reflux drum, (c) level control in the RD sump, (d) level control in the C2 sump, (e) pressure control in the C1 column, (f) pressure control in the C2 column (g) pressure control in the C1 column, (h) pressure control in the RD column, (i) temperature control in CSTR, (j) temperature control in the C1 column, (k) temperature control in the RD column, and (l) temperature control in the C2 column.

5. CONCLUSIONS In this study, a complete PWC scheme was developed using IFSH methodology for a complex two-step FA process involving a CSTR, RD, and several recycles. The CV−MV pairings were identified considering heuristics, simulations, and/or RGA. Pressure driven simulation in APD was used for more realistic results. The designed PWC system was assessed for various disturbances, in terms of settling time of FA production rate, settling time of overall accumulation, DPT (which impacts economics), TV (which accounts for control moves required to reject a disturbance), and DDS (which quantifies accumulation of components). Results show that the designed control system maintained FA purity and production rate as well as provided smooth and stable control for all disturbances tested (up to ±20% change in the throughput), despite several intricacies of the FA process such as multiple recycles and snowballing. One future work is the development of PWC for the conventional FA process optimized recently;40 then, the PWC performance of the intensified FA process studied in this paper can be compared with that of the conventional FA process.

accumulation is greater for larger magnitude of the throughput change, requiring larger settling time. The total accumulation of all components reached almost zero for all disturbances. This is crucial for the safe operation of the process as the buildup of the accumulation may endanger the plant and the surroundings. Profiles of FA purity in the presence of all disturbances are presented in Figure 7. As expected, FA purity was disturbed after the disturbance was introduced. However, the purity was restored to the original value for all disturbances tested. This confirms that the designed control structure is effective in rejecting the disturbances and to maintain the desired purity. As expected, for D7 and D8, the FA purity deviated more and also required a longer time (∼14 h versus ∼8 h for other disturbances) to return to the set point due to the greater magnitude of these disturbances. Among all disturbances, D7 required an increase of the set point in TC104 through CC103 (cascade scheme) to maintain the purity. Figure 8 presents the responses of some of the crucial controllers for selected disturbances. Remaining controllers (not shown for conciseness) also performed equally well. As there were many controllers present in the developed PWC system, performance assessment was not done for individual control loops in terms of traditional criteria such as overshoot, decay ratio, and peak time. As mentioned in Section 4, assessment criteria from the entire plant perspective were used instead. It is inferred from Figure 8 that the controllers were properly designed as these loops controlled the CVs at the desired values for all tested disturbances.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.8b02654. CDOF analysis, control structure with tuning parameters for the FA process, “Downs Drill” table indicating J


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component material balances, and definition of the performance evaluation criteria (PDF)

AUTHOR INFORMATION

Corresponding Author

*Phone: +91-9003670402. E-mail: [email protected]. ORCID

Dipesh S. Patle: 0000-0001-7592-5444 Swapnil Sharma: 0000-0001-8724-0987 G. P. Rangaiah: 0000-0001-8108-6608 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Authors are grateful to Vellore Institute of Technology for allowing us to use AspenOne in the Process Systems Engineering lab.

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