A reduced order model for triethylene glycol natural gas dehydration system

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South African Journal of Chemical Engineering 44 (2023) 51–67

Available online 6 January 2023

A reduced order model for triethylene glycol natural gas dehydration system

Daniel Jia Sheng Chong

^a

, Dominic C.Y. Foo

^a^,^*

, Zulfan Adi Putra

^b

aDepartment of Chemical and Environmental Engineering/Centre of Excellence for Green Technologies, University of Nottingham Malaysia, Broga Road, Semenyih, Selangor 43500, Malaysia

bFormerly PETRONAS

A R T I C L E I N F O Keywords:

Glycol dehydration Natural gas Triethylene glycol Process simulation Process optimization Drizo process

A B S T R A C T

In natural gas processing plants, glycol dehydration is commonly used to remove water from the gas streams, to avoid pipeline blockage and equipment breakdown due to hydrates formation. This paper proposed a reduced order model developed based on integrated simulation-optimization approach for the glycol dehydration system, with the aim to minimize its operating cost while satisfying pipeline quality specifications. Steady-state process simulation software was used to identify important operating parameters for the glycol dehydration process;

these include reboiler temperature and flow ratio of the regeneration column, and solvent flowrate. The identified parameters are built into a non-linear programming model, which was developed as a reduced order model for ease of implementation in the plant. The studied parameters are reboiler duties, hot oil, condenser, and pump, as well as TEG make-up flow rate and CO2 equivalent (CO2-eq) emissions. The Pareto Front is developed to identify the minimum operating cost at different levels of water dew point specification. The work has resulted in the annual savings of more than 34.6%.

1. Introduction

Natural gas is an important fossil fuel with energy efficient benefits as compared to oil and coal. It is used as fuel and serves as the source of hydrocarbons to manufacture petrochemical products. Besides, natural gas is viewed as transitioning gas while the world is moving towards renewable energy. The widespread usage of natural gas in engines has led to substantial reductions in emissions of carbon dioxide (CO2), nitrogen oxides and particulate matter (Wei and Geng, 2016). In 2020, natural gas consumption which has reached 81 billion cubic meters, is accounted for a record high of 24.7% of the global energy consumption (Spencer Dale, 2021).

Natural gas extracted from production wells is saturated with water vapour. The presence of saturated water will create an unsafe operating condition in natural gas processing and transmission (Mokhatab et al., 2018). If the temperature of the process stream is below the water dew point temperature of the gas flow, multiple problems will arise. The presence of condensed water in the natural gas pipeline will cause slug flow and erosion. In addition, the water condensate will form solid gas hydrates which have the potential to agglomerate and plug pipelines and equipment (Kvamme and Aromada, 2018). Methane hydrates are

crystallized substances which are produced when high composition of methane is trapped inside the molecular structure of the water (Yin and Linga, 2019). Moreover, the presence of water vapour will lower the heating value of natural gas (Ghanbari et al., 2020).

To avoid these issues during the processing and transmission of natural gas, the gas needs to be dehydrated to lower its water dew point temperature. Another indicator of water dew point temperature can be represented in terms of water content. A directive for gas distribution is issued to provide the values for allowable concentration of water in the gas which prevents water condensation and hydrate formation. In Europe, the dew point temperature for water is set at − 7 ^◦C and 4 MPa (Bahadori, 2014). Countries situated at a warmer climate such as Nigeria has a maximum water dew point temperature specifications at 4 ^◦C and 4 MPa, containing twice the amount of water content in Europe (Netušil and Ditl, 2012).

There are three commercial methods for dehydrating the wet gas, including adsorption, absorption, and condensation (Kong et al., 2018).

Absorption is the most common technique for industrial dehydration of natural gas. Water vapour from the wet gas is absorbed into the liquid (glycol) solvent stream. Glycols which are commonly used as liquid desiccant are monoethylene glycol (MEG), diethylene glycol (DEG),

* Corresponding author.

E-mail address: [email protected] (D.C.Y. Foo).

Contents lists available at ScienceDirect

South African Journal of Chemical Engineering

journal homepage: www.elsevier.com/locate/sajce

https://doi.org/10.1016/j.sajce.2023.01.001

Received 17 August 2022; Received in revised form 21 December 2022; Accepted 4 January 2023

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triethylene glycol (TEG) and tetraethylene glycol (TREG) (Affandy et al., 2020). The selection of glycol types depends on the physical properties (vapour pressure, decomposition temperature, regeneration temperature and cost of the liquid desiccants). However, TEG is the most commonly used liquid desiccant in large scale natural gas dehydration processes due to its high chemical resistance, affinity to water, degradation temperature, regeneration as well as low operating cost, volatility and losses to off gas (Petropoulou and Voutsas, 2018).

There are four main types of glycol dehydration systems which are conventional glycol dehydration process, stripping gas, Coldfinger technology and Drizo process. Stripping gas process is considered as the most straightforward method to enhance glycol regeneration performance. However, in the simulation carried out by Neagu and Cursaru (2017), the glycol purity in the regeneration column was capped at 98.8% which has the lowest yield compared to glycol dehydration systems. This process was first implemented in 1991 where stripping gas reduces the partial pressure of the water, ultimately increasing the purity of glycol at the bottom stream of the regeneration column (Kidnay et al., 2019). The stripping agent can be dry natural gas, volatile hydrocarbon such as benzene, toluene and xylene as well as inert gases (Kong et al., 2018).

Revamped regeneration systems are available in the industry that can produce higher glycol concentrations to lower the water dew point temperature of the treated gas beyond the conventional TEG dehydration process. Coldfinger technology is a glycol purification unit which installs a cooling coil (coldfinger) in the vapour space of the surge tank.

Based on the studies carried out by Lyons et al. (2016), this technology can purify the TEG from the reboiler up to 99.96%, which is significantly higher than the conventional glycol dehydration systems.

Another glycol purity enhancement method called Drizo process which was implemented by Dow chemical company in 1970 (Paymooni et al., 2011). Consequently, it allows the dehydration process to achieve a higher dew point depression of up to 150^◦F (Mokhatab et al., 2018).

Dew point depression refers to the decrease in water dew point after glycol absorption (Chebbi et al., 2019). The quantity of benzene, toluene, ethyl-benzene, xylene (BTEX) and volatile organic compounds (VOC) emissions flared from the overhead flow of the regeneration column to the atmosphere taken into consideration in the glycol dehydration process (Mukherjee and Diwekar, 2021).

To incorporate economic analysis into the performance of the dehydration process, Jokar et al. (2014) applied Visual Basic programming language to develop the HETP of structured packing. They proposed the replacement of existing trayed absorption column with structured packing which is more economically justifiable as well as to enhance the capacity and performance of the glycol dehydration process. Besides that, Neagu and Cursaru (2017) demonstrated that the effect of installing stripping gas configuration into the conventional system has negligible effect on the fixed capital investment estimation when steady-state simulator, UniSim Design is used to study the dehydration plant performance. Based on their parametric study, they concluded that stripping gas is effective to improve regeneration performance of the TEG concentration (Neagu and Cursaru, 2017).

Using Aspen HYSYS simulation software, Kamin et al. (2017) conducted the study on glycol dehydration system to determine the process parameters on the utility consumption and glycol losses from the overhead regeneration column. Response surface methodology (RSM) was used to optimize the energy consumption and glycol losses of the dehydration process within the targeted water dew point temperature range. In essence, energy conservation and carbon emission taxation are the key parameters to produce a cost effective and sustainable design optimization especially on a plant with a long economic life (Bayoumy et al., 2020).

A parametric optimization study on design variables such as TEG circulation rate, number of theoretical trays, wet gas temperature and pressure, feed flow rate and stripping gas rate was performed by Chebbi et al. (2019). Aspen HYSYS simulator and optimizer tool were carried out to optimize the processing cost which incorporates utility and capital cost (Chebbi et al., 2019). Despite the convenience of applying HYSYS built-in “Optimizer” function to generate the optimum values, it is however frequently associated with convergence issues (Hoorfar et al., 2018). Hence, LINGO programming language was introduced to express the optimization model.

Similar to the above investigations, the motivation of this study is to optimize the operating parameters of an existing glycol dehydration process coupled with Drizo technology, aiming to minimize its total operating cost. The proposed framework is carried out with a reduced order model, that is developed using an integrated simulation- optimization approach. Even though the simulation and optimization Fig. 1. Process flow diagram of the studied Drizo glycol dehydration facility.

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South African Journal of Chemical Engineering 44 (2023) 51–67

models were conducted in different software tools, both models will work interactively to generate the reduced order model. In the simulation stage, specifications of the processes are defined based on actual plant data. An optimization model was then developed based on the identified design variables and response parameters from the simulation model that affect the operating cost of the dehydration process.

The objectives of the study are (1) to identify process parameter that will affect the plant’s performance, (2) to develop mathematical model for the dehydration process, and (3) to generate an optimization model to minimize the operating cost from the design variables. In this paper, the background theory of glycol dehydration process is elaborated, preceded with the methodology of process simulation and optimization.

This is followed by a brief case study of the existing plant. Next, development of the reduced order model on an actual plant is described, which is based on non-linear regression model for each response parameters and the parametric study on the interaction between input and output variables obtained. Subsequently, the optimization model on the plant’s operating cost is developed. Validation and comparison of data with the optimized parameters are included in the discussion section of the report. Sensitivity analysis on the effect of design parameters on the operating cost is presented before the work is concluded.

2. Literature review/background theory 2.1. Glycol dehydration process

The natural gas treatment process is conducted to purify raw natural gas by removing impurities and contaminants which meets the pipeline- quality gas specifications. There is a series of natural gas processing steps which are required for treating raw gas. It begins with inlet separation, acid gas removal process, gas dehydration process, hydrocarbon dew point controlling process, nitrogen rejection process, helium re- covery process and ends with gas compression process before trans- mitted and distributed to the natural gas grid (Kidnay et al., 2019). Fig. 1 illustrates the process flow diagram of a TEG dehydration process with recycling of overhead regeneration column configuration. Drizo process is widely used in the industry since most Drizo solvent can be recycled to strip water from rich TEG and the emissions of BTEX to the atmosphere can be controlled (Kong et al., 2020; Mokhatab et al., 2018).

Drizo technology can be implemented in conventional dehydration process to improve the purity of glycol and water dew point temperature of sales gas. Nevertheless, Drizo glycol dehydration process requires the largest capital cost due to its complexity of the process and addition of major equipment such as three-phase separator, stripping column and heat exchangers (Kong et al., 2020). Kong et al. (2020) further concluded that Drizo process provides a better technical dehydration performance compared to stripping gas process. It can be observed in Table 1 that Drizo dehydration process is able to achieve a TEG purity of more than 99.99 wt%, with water dew point depression of 180^◦F to 220^◦F; this is more superior than other types of dehydration processes.

reduces water content in the sales gas) requires lower circulation flow of TEG in the glycol dehydration process. This leads to lower glycol losses and reduced energy consumption, which help to minimize the total operating cost of the glycol dehydration process.

2.2. Reboiler temperature

The crucial function of reboiler at the regeneration column is to remove water and non-condensable gases from the rich TEG flow in regeneration column. Sakheta and Zahid (2018) demonstrated that the increase in temperature improves the performance of the dehydration efficiency. In a typical regeneration column, water is removed to less than 1 wt% in lean glycol (Affandy et al., 2020). In the simulation presented by Okoro et al. (2020), the purity of TEG is achieved up to 99.95% with a reboiler temperature of 401^◦F (205 ^◦C). The boiling point for TEG is up to 288 ^◦C but the temperature of TEG will start degrading due to thermal decomposition once the temperature reaches 204 ^◦C (Chebbi et al., 2019). Nevertheless, the reboiler and condenser duties of the regeneration column are major contributors to the utility costs of the glycol dehydration process.

It is important to minimize the cost of reboiler duty which is a function of reboiler temperature while ensuring the TEG purity and the water dew point temperature (Tdew) (Okoro et al., 2020). This is resulted by the increase of glycol losses via the overhead regeneration column and subsequently strains the condenser which is required to liquify the evaporated TEG back to the dehydration process (Haque et al., 2019). As stated above, despite that the reboiler temperature was constrained to be lower than 204 ^◦C in the study, dry gas quality specification can still be satisfied by altering the flow ratio and solvent flow rate.

2.3. Flow (reflux) ratio of the overhead regeneration column

The flow ratio from the regeneration column overhead was manipulated primarily to control the glycol loss and the removal of light gases (CO2, CH4, C2H6) as off gases from the dehydration process (Haque et al., 2019). Since the overhead stream of the regeneration column consists mainly of water, recycling the overhead stream back to the still column will result in poor effect on the separation performance of the regeneration column. Therefore, Piemonte et al. (2012) proposed to set the reflux ratio of the column at a minimal value when developing the HYSYS simulation model.

In addition, Kiss and Ignat (2013) suggested that since TEG and water have a large boiling point difference, high reflux is redundant as the separation is relatively easy. Excess recirculation of cooled glycol to the regeneration column can lower the top temperature which has the tendency to condense water vapour into the TEG. This will increase the energy demand for the reboiler duty as a larger boil-up rate will be required. However, increasing recycle flow will recover the loss of glycol due to entrainment in the overhead stream (Haque et al., 2019). In conclusion, there is a trade-off between the cost incurred by glycol make-up flow and the cost of utilities in the regeneration column.

2.4. Solvent flow rate

The introduction of stripping agent in conventional glycol dehydration system enhances the TEG purity which remarkably reduces the water content in the sales gas (Mokhatab et al., 2018). Based on Nemati Rouzbahani et al. (2014), 10% increase in solvent molar flow rate reduces the water dew point temperature of dry gas up to 6% with negligible changes in the total energy requirements and emissions of VOC into the atmosphere.

The injection of hydrocarbon solvent into the dehydration process increases the volatility of water in the water-rich TEG since hydrocarbons such as n-heptane, isooctane and BTEX can form azeotropic Table 1

Characteristic of glycol in dehydration process (Lyons et al., 2016).

Regeneration

Process Characteristic TEG

Purity (wt%)

Water Dew Point Depression (^◦F)

Vacuum Vacuum pressure to reduce

partial pressure of water 99.2 to

99.9 100 to 150 Coldfinger Applies condenser to condense

water vapour from the reboiler using a cooling coil

99.96 100 to 150

Drizo Uses volatile hydrocarbon as

stripping gas 99.99+ 180 to 220

Stripping Gas Uses inert gas or portion of dry gas product as stripping gas 99.2 to

99.98 100 to 150

D.J.S. Chong et al.

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TEG concentration in the system. Furthermore, the application of solvent in the system minimizes energy duty compared to conventional dehydration processes. In terms of economic analysis, the utilization of Drizo solvent increases the higher heating value (HHV) of the sales gas which translates to higher the gross profit (Kong et al., 2020). However, higher processing cost will be incurred in the dehydration system, due to larger make-up glycol flow as a result of entrainment loss caused by the increasing stripping solvent flow rate (Ibrahim et al., 2017).

In summary, the increase in TEG purity reduces the water dew point temperature of sales gas, recirculation ratio of glycol, energy demand of the system, BTEX emissions as off gas and glycol loss flow rate.

3. Methodology

The development and application of simulation-optimization model requires a series of methodological stages, as shown in Fig. 2.

The base case model was developed according to the user preference and configured with defined parameters provided by the plant data. The model was assumed to be a multivariable steady state model. The strategy adopted was the coupling of Aspen HYSYS simulation modelling with LINGO optimization modelling algorithms to satisfy the sensitivity analysis and constrained bounding of the variables.

In this work, LINGO optimization tool was selected over HYSYS built-in “Optimizer” function. LINGO is a high-level programming language which employs both gradient-based and derivative-free optimization approaches (Bayoumy et al., 2020; Goodarzi et al., 2014). In this work, the sensitivity based “Case Study” function in Aspen HYSYS is used to generate a series of simulation results with varying operating conditions prior to the use of LINGO in constructing the optimization model .

Regression analysis was carried out between each response parameter to generate the p-value of each independent variables (Paolella, 2018). The p-value in the ANOVA test is used to determine whether the differences between the distribution of datasets are statistically significant (Kozlov et al., 2019). It can be interpreted that if the interaction between both the studied operating parameters and the response parameters are statistically significant, a low p-value (<0.05) will be obtained (Kamin et al., 2017). The reduced order non-linear mathematical equations will then be developed with the statistically significant input variables for each response parameters. The reduced order non-linear mathematical equations are used for ease of implementation in the plant.

The mathematical expression of the objective function and the constraints were developed. Since the model is based on an existing Fig. 2. Methodological framework for simulation-optimization approach for glycol dehydration process.

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reference plant, the natural problems are multidimensional and non- linear. Hence, non-linear mathematical equations are selected over linear regression equations in the LINGO optimization model to reduce the discrepancies when obtaining the optimized values (Ray, 2019).

Subsequently, the optimized values obtained from LINGO optimization model will be verified in the simulation model. A case study is next demonstrated for the proposed approach.

4. Case study 4.1. Process description

The case study is conducted based on a TEG dehydration plant coupled with Drizo process in an offshore platform in Malaysia. The wet natural gas is sourced from the acid gas removal unit (AGRU) which performs acid gases removal from the sales gas (Aliff Radzuan et al.,

2019). Fig. 3 illustrates the process simulation model of the glycol dehydration system constructed in Aspen HYSYS.

The wet natural gas (G1) enters the Flash Separator, and its water condensate is removed before entering the Glycol Contactor. For this glycol dehydration process, the target water dew point temperature specification for the dry natural gas is fixed to a minimum value of − 50

◦C. However, the gas pipeline specifications for maximum water dew point is set as − 25 ^◦C by local authorities for pipeline-transported natural gas (Kong et al., 2020).

The wet natural gas enters from the bottom of the Glycol Contactor, whereas the lean TEG (LG6) enters from the overhead. The counter- current flow enhances the absorption efficiency of water into the lean glycol. The water-rich TEG (S22) is combined with the water condensate flow (S21) to be sent to the Regenerator. In the Regenerator column, the overhead stream (OG2) will consist of the Drizo solvent and water as well as trace amount of TEG. Insoluble gases from the make-up solvent and overhead vapour from the Regenerator will be emitted as off gases (OG4) from the Flash Separator-3. Bottom liquid of the latter (S6) is transported into the Three Phase Separator, where non-homogeneous water and liquid solvent are separated. Part of the water rich stream is recycled whereas the remaining are removed (W3). Note that a portion of glycol is lost with the water rich stream W3.

Fig. 3.Process simulation model of the glycol dehydration process in Aspen HYSYS (parameters for process units and important stream data are found in Appendix 1).

Table 2

Mass flow rate for the wet natural gas feed flow (G1) and the Drizo solvent flow rate (SOLV1).

G1 SOLV1

Flowrate (kg/h) (wt%) Flowrate (kg/h) (wt%)

N2 2608 0.33 0.0 0.00

CO2 56507 7.22 4.7 1.70

C1 607119 77.57 19.8 7.14

C2 51583 6.59 7.5 2.72

C3 33778 4.31 15.1 5.44

iC4 8906 1.14 8.5 3.06

nC4 9321 1.19 12.2 4.42

iC5 4042 0.52 10.4 3.74

nC5 2563 0.33 9.4 3.40

nC6 2164 0.28 21. 7 7.82

nC7 0 0.00 65.0 23.47

nC8 0 0.00 71.6 25.85

nC9 3590 0.46 25.4 9.18

nC12 0 0.00 4.7 1.70

nC14 0 0.00 0.9 0.34

H2O 542 0.07 0.0 0.00

Total 782723 100.00 277 100.00

Table 3

Parameters for comparison for the actual data versus the plant data.

Stream

number Description Parameters Plant

data Simulation

data Percentage Difference (%) LG6 Lean TEG

flow Flow Rate

(m³) 14.93 15.00 0.47

Temperature

( ^◦C) 28 31.78 13.50

S8 Recycled Drizo solvent flow

Flow Rate

(m³) 0.17 0.18 5.88

RG5 Rich TEG

flow Temperature

( ^◦C) 135 140.5 4.07

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The hydrocarbon stream (SOLV2) from the Three Phase Separator is further heated and is sent to the Stripping Column to strip the remaining water content from the lean glycol leaving the the Regenerator. The regenerated lean TEG (LG2) leaving the Stripping Column is cooled, repressurized and recycled to the Glycol Contactor for natural gas dehydration.

4.2. Base case simulation

Aspen HYSYS simulation package v11 was used for developing the process model (Fig. 3) (AspenTech, 2022). The Glycol Package was selected as the thermodynamic package for the simulation model. This thermodynamic package is used due to its ability of accurately pre- dicting the solubility of BTEX in the aqueous phase (Alnili and Barifcani, 2018). To obtain the base case simulation results which closely resemble the plant model data, equipment specifications, plant operating values and constraints were simulated according to the plant data. The composition and mass flow rate of both wet natural gas (G1) and Drizo solvent (SOLV1) that were obtained from the actual plant operations are displayed in Table 2.

Several assumptions were made for the simulation model:

(1) Pressure drop across the column was assumed to be 0.05 bar for each bubble cap tray.

(2) Pressure drop across the heat exchanger is 0.3 bar.

(3) The flow ratio of stream S17 (normally no flow) to stream SOLV3 in splitter TEE-102 is assumed to be 0.005.

(4) Water hydrate formation is not considered in the glycol process.

(5) Hydrocarbon dew point temperature is not incorporated in the focus of the study.

In Table 3, several stream compositions were compared with the actual plant data to identify the variation of the performance between the plant model versus the simulation model. The percentage difference of actual data and plant data in stream S8 is 5.88% (Table 3). The in- compatibility of the flow rate is caused by the inconsistent feed input of Drizo solvent in stream SOLV1. In actual practise, the flow of Drizo solvent is frequently adjusted in order for the natural gas stream to achieve the targeted water dew point temperature; this is mainly due to the cheap cost of hydrocarbon solvent that is sourced from effluents of natural gas treatment facility (Table 4). (. Table 3 shows the deviation of stream LG6⋅is relatively high (13.5%). As this stream is the outlet of Heat Exchanger-2, the inconsistency was caused by the varying in flow rates that will result in the change in thermal transfer between fluids.

5. Development of non-linear regression model and mathematical equations

The sensitivity analysis developed using “Case Study” function of Aspen HYSYS simulation model generated values for each response according to the studied ranges of the parameters. Subsequently, the response parameters are formulated in reduced polynomial order after applying the regression model analysis. The observable parameters such as reboiler duty (Qr), hot oil duty (Qh), condenser duty (Qc), pump power (QP), TEG make up flow (FTEG), CO2-eq flow rate (FCO2), and water content (WH2O)are represented by a generalized variable Pi in Eq.

(1). The latter is used to define all observable parameters that could be fitted in the cubic polynomial Eq. (1) with multiple input values. In Eq.

(1), β₀denotes the coefficient of the model intercept and β_irepresents the regression coefficient of the linear, quadratic, and interactive effects of the models respectively.

Table 4

Price unit of raw materials, utility, and carbon emission penalty cost.

Cost of Raw Materials Values Reference

Triethylene glycol, CTEG ($/kg) 2.49 (Feliza Mirasol, 2013) Drizo solvent, Cs ($/kg) 0.01 From plant authority

Cost of Utility Values Reference

Reboiler duty, CR ($/kJ) 4.51•10⁻⁶ From plant authority Hot oil duty, CH ($/kJ) 4.51•10⁻⁶ From plant authority Cooling water, CCW ($/kJ) 3.78•10⁻⁷ (Turton et al., 2018) Electricity for pumps, CP ($/kWh) 7.14•10⁻² (Tenaga Nasional

Berhad, 2021)

Other Cost(s) Values Reference

Carbon emission penalty cost per kg CO2-

eq, CCO2 ($/kg CO2-eq) 19.04 From plant authority

Table 5

Coefficient values for the observable parameters and coefficient of determination (R²).

Reboiler duty, QR

(kJ/h) Hot oil duty, QH

(kJ/h) Condenser duty,QC

(kJ/h) Pump power, QP

(kW) TEG make up flow,

FTEG (kg/h) CO2-eq flow rate, FCO2 (kg

CO2-eq/h) Water content, WH2O (lb MMSCF⁻¹)

β₀ −2.75•10⁸ 1.29•10⁹ 9.50•10⁸ 3.91•10² 8.32•10² 1.90•10³ 6.16•10²

β₁ 2.90•10⁶ −1.46•10⁷ −1.08•10⁷ −4.12 −1.76•10¹ 3.39•10¹ − 8.86

β₂ −1.16•10⁴ 6.26•10⁴ 4.67•10⁴ 1.79•10⁻² 1.00•10⁻¹ −2.74•10⁻¹ 4.24•10⁻²

β₃ 18.3 −1.01•10² −7.58•10¹ − 2.86•10⁻⁵ −1.73•10⁻⁴ 4.78•10⁻⁴ − 6.83•10⁻⁵

β₄ 1.66•10⁶ 2.05•10⁷ 2.23•10⁷ 4.91 8.28•10² 9.73•10 − 4.15•10⁻¹

β₅ −4.06•10⁶ −8.17•10⁶ −1.25•10⁷ −2.15 −2.61•10² 1.05•10¹ 1.81

β₆ 1.11•10⁷ 1.01•10⁶ 1.21•10⁷ 4.82•10⁻¹ 1.34•10¹ 3.48•10¹ 6.51•10⁻²

β₇ 7.07•10⁵ −2.85•10⁶ −2.08•10⁶ − 6.31•10⁻¹ 1.86 −3.64•10¹ 3.78•10⁻²

β₈ −8.52•10² 3.47•10³ 2.58•10³ 7.37•10⁻⁴ −4.09•10⁻³ 6.07•10⁻² − 4.00•10⁻⁴

β₉ 4.13•10⁻¹ − 1.63 − 1.25 − 3.34•10⁻⁷ 3.62•10⁻⁶ −4.16•10⁻⁵ 4.70•10⁻⁷

β₁₀ 9.06•10³ −8.58•10⁴ −7.79•10⁴ − 2.03•10⁻² −3.59 −3.73•10⁻¹ 1.78•10⁻³

β₁₁ −6.36•10¹ 1.99•10² 1.42•10² 4.88•10⁻⁵ 5.40•10⁻³ −3.18•10⁻⁴ − 3.95•10⁻⁵

β₁₂ −2.74•10³ 1.12•10⁴ 8.16•10³ 2.50•10⁻³ −5.72•10⁻³ 1.33•10⁻¹ 3.33•10⁻⁴ β₁₃ 9.41•10⁻³ − 4.06•10⁻² − 2.98•10⁻² − 8.69•10⁻⁹ 4.17•10⁻⁸ −5.78•10⁻⁷ 2.73•10⁻¹⁰ β₁₄ 2.66•10² −1.26•10⁴ −1.21•10⁴ − 2.84•10⁻³ −5.70•10⁻¹ −4.40•10⁻² 3.83•10⁻⁴ β₁₅ 1.48•10⁻¹ −1.52•10¹ −1.42•10¹ − 3.34•10⁻⁶ 6.06•10⁻⁴ −2.25•10⁻⁴ − 2.91•10⁻⁶

R² 0.9990 0.9957 0.9964 0.9952 0.9968 0.9995 0.9913

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Fig. 4. Surface and contour plots for (A) flow ratio, for reboiler duty; (B) hot oil duty; (C) condenser duty; (D) pump duty; (E) TEG make-up flow; (F) CO2-eq flow rate and (G) water content in sales gas (Nratio =0.2).

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Note that reboiler duty (Qr), hot oil duty (Qh), condenser duty (Qc), pump power (QP), TEG make up flow (FTEG), CO2-eq flow rate (FCO2)will contribute to operating cost of the glycol dehydration system. On the other hand, water content (WH2O)is important for the estimation of water dew point temperature of the sales gas, in order to satisfy the specification of the sales gas.

The coefficients produced from the regression analysis will create the most fitted polynomial equation which has the capability to accurately predict each output response with varying input variables (Kamin et al., 2017). The coefficient values for the parameters and the coefficient of determination (R²) are displayed in Table 5. Detailed steps in generating these coefficient values are given in Appendix A2.

The water dew point temperature of the sales gas (Tdew) can be represented as a discontinuous function of water content of the gas (WH2O) in Eq. (2). Note that both equations have R²of 0.9999 respectively.

System Constraints:

The main constraint covering the process network is the targeted water dew point temperature of the natural gas, Tdewwhich must be lower than − 50 ^◦C to achieve the target specification for the sales gas. It is given in Eq. (3):

Tdew(^∘C) ≤ − 50 (3)

To ensure the operating parameters stay within the plant capacity, , Eq. (4)-(6) are necessary:

195≤Tr(^∘C) ≤204 (4)

0.20≤Nratio≤0.60 (5)

230≤Fs(kg/h) ≤330 (6)

6. Analysis of parameters of in the glycol dehydration process Detailed analysis on the reboiler temperature (Tr), flow ratio of

regeneration column overhead stream (Nratio)and the mass flow rate of the stripping solvent (Fs)are carried out to identify the effect on the system’s operational cost and the water dew point temperature. These independent variables are manipulated in the simulation model to study the effects on the parameters such as the reboiler duty (Qr), hot oil duty (Qh), cooling duty (QC), pump energy consumption (Qp), CO₂-eq flow rate (FCO2)and make-up TEG flow (FTEG)which will be translated into operational cost. In addition, the relationship between water dew point temperature (Tdew)and water content (WH2O)were reviewed.

The three operating parameters have combined outcomes on the investigated parameters, the surface and contour plots were developed to visualize the responses as functions of two independent variables (i.e.

reboiler temperature and solvent flow rate), whereas flow ratio(Nratio) was set as a constant value and manipulated as Nratio=0.2, 0.4 and 0.6 in three instances (Fig. 4-6).

6.1. Effect of operating parameters on the reboiler duty

Based on Figure 4(A), Figure 5(A) and Figure 6(A), the reboiler duty ranges from 2.6 × 10⁶to 5.3 × 10⁶kJ/h. The reboiler duty has an increasing trend with the flow ratio increases from 0.2 to 0.6. This is contributed by the increase in a larger recycle flow rate reintroduced into the regeneration column. It is observed that the reboiler duty decreases when reboiler temperature and solvent flow increases. This is because the surge in hot solvent flow will facilitate the heating effect of the reboiler at regeneration column to maintain the temperature at the calibrated specifications by increasing the hot oil duty. Nevertheless, the reboiler temperature is fixed at an upper limiting factor of 204 ^◦C because thermal degradation will affect the dehydrating properties of the TEG (Chebbi et al., 2019).

6.2. Effect of operating parameters on the hot oil duty

The results from HYSYS built-in ‘Case Study’ function showed that Tdew(WH2O) =

⎧

⎪⎪

⎨

⎪⎪

⎩

− 4.46 ×10⁴− 6.72 ×10⁵W_H²₂_O+3.96 ×10⁶W³_H₂_O +4.24 ×10⁴exp(WH2O) − 362.3 ln(WH2O), WH2O<0.0317

4.21 ×10⁴+4.24 ×10⁴WH2O+2.01 ×10⁴W_H²₂_O +9.28 ×10³W_H³

2O− 4.22 ×10⁴exp(WH2O) +8.25 ln(WH2O), WH2O≥0.0317

(2) (1)

(11)

there is a surge in heating duty of hot oil when the solvent flow rate increases. The heating duty increases with larger Drizo solvent flow rate.

Higher temperature specifications of the reboiler at Regenerator will burden the Hot Oil Heat Exchanger to maintain the temperature of the solvent outlet at 220 ^◦C. From Figs. 4(B), 5(B), and 6(B), the alteration of flow ratio has little effect on the hot oil duty since the flow ratio (TEE- 101) which adjusts the exit stream W3 comprised of a water rich mixture with low concentration of hydrocarbon solvent content. The response from the simulation model showed that the hot oil duty ranges from 7.5

×10⁵to 5.6 ×10⁶kJ/h.

6.3. Effect of operating parameters on condenser duty

Figs. 4(C), 5(C), and 6(C) represent the performance of condenser duty with the variation of the three independent variables. The condenser functions to cool down overhead streams from the regeneration column. Consequently, with the increase in reboiler temperature specifications, solvent feed flow rate into the Regenerator and flow ratio of the overhead recycle stream, the cooling requirement of the Condenser will be higher, since the condenser outlet temperature is fixed at 50 ^◦C. The increase in condenser duty will ultimately lead to a surge in utility costs of the system.

6.4. Effect of operating parameters on pump consumption

Since the glycol recirculation rate is not the focus of the study, the glycol flow rate is a control value in the studied model. The pump duty will not vary significantly since the main energy demand is used to repressurize the lean glycol stream after leaving the stripping column for dehydrating wet natural gas in the Glycol Contactor (Chebbi et al., 2019). Referring to the response and contour plots in Fig. 4(D), Fig. 5 (D), and Fig. 6(D), the changes of pump energy is negligible as a function of the studied operating parameters. Nevertheless, the increment of the step value in solvent flow rate, flow ratio and reboiler temperature in the case study increase the pump energy consumption of the system.

Fig. 7. Water dew point temperature against water content.

Table 6

Operating cost breakdown comparison between the base case parameters and the proposed configurations.

Configurations Base case Proposed

configuration Operating cost for reboiler ($/h) 15.42 22.73 Operating cost for hot oil heat exchanger

($/h) 10.80 9.19

Operating cost for condenser ($/h) 1.55 2.03 Operating cost for electrical pump ($/h) 2.92 2.91 Cost for TEG makeup flow ($/h) 67.83 21.00 Cost for Drizo solvent flow injection($/h) 2.77 2.76 Carbon emission penalty cost ($/h) 15.97 16.02 Total Operating Cost ($/h) 117.25 76.63

Table 7

Comparison of Results between simulation model and optimization model.

Configurations Simulation

Model Optimization

Model Percentage

Difference (%) Reboiler duty, Qr (GJ/h) 5.036 5.050 0.29%

Hot oil duty, Qh (GJ/h) 2.036 2.017 0.91%

Condenser duty, Qc (GJ/

h) 5.361 5.357 0.08%

Pump power, QP (kW) 40.793 40.788 2.15%

TEG make-up flow, FTEG

(kg/h) 8.433 8.614 0.01%

CO2-eq flow rate, FCO2

(kg CO2-eq/h) 841.02 841.09 0.01%

Total Operating Cost

($/h) 76.630 77.062 0.56%

Table 8

Comparison between before and after optimization model.

Configurations Base case

value Optimized

model Changes

(%) Reboiler Temperature, Tr ( ^◦C) 204 204 0 Flow ratio of overhead regeneration

column, Nratio

0.20 0.60 +200%

DRIZO solvent Flow, Fs (kg/h) 277.0 275.8 −0.44%

Reboiler duty, Qr (GJ/h) 3.42 5.04 47.41%

Hot oil heat exchanger duty, Qh

(GJ/h) 2.40 2.04 −14.96%

Condenser duty, Qc (GJ/h) 4.11 5.36 30.59%

Pump power, QP (kW) 40.83 40.79 −0.10%

TEG Make up Flow, FTEG (kg/h) 27.2 8.4 −69.04%

CO2-eq flow rate, FCO2 (kg CO2-eq/

h) 838.3 841.0 +0.33%

(12)

6.5. Effect of operating parameters on TEG make-up flow

TEG loss is exceptionally affected particularly by the flow ratio parameter (N_ratio). There is a significant decline in the scale of the TEG loss as presented in Figs. 4(E), 5(E) and 6(E) where the flow ratio (Nratio) is varied as 0.2, 0.4 and 0.6 respectively. The TEG loss approaches a (local) minimum when temperature is 200 ^◦C and solvent flow rate 250 kg/h. However, the optimization modelling must be developed to locate the optimal values since there will be trade-off between the raw materials and utility costings to achieve a minimum operational cost. The increase in solvent flow rate and reboiler temperature exacerbate the entrainment of TEG to the overhead regeneration column, which leads to further glycol depletion in the system.

6.6. Effect of operating parameters on CO₂-eq flow rate

The surface and contour plot in Figs. 4(F), 5(F) and 6(F) demonstrated a linear relationship between the CO₂-eq flow rate and the solvent flow rate. This is because light gases (CO2, CH4, C2H6) contained in the Drizo solvent will be liberated as off gases in the Flash Separator-3 before feeding into the Stripping Column. Despite the minor effect of

flow ratio on the CO2-eq flow rate of the dehydration facility, reboiler temperature have a positive impact on CO2-eq flow rate.

6.7. Effect of operating parameters on the water content of sales gas Water content of sales gas will be reduced when temperature of the reboiler and solvent flow rate increases as depicted by Figs. 4(G), 5(G), and 6(G). This is because the rise in temperature and solvent flow rate in the stripping and regeneration columns will increase the lean TEG purity. Hence, more water can be readily absorbed from the wet gas in the glycol contactor. The increase of flow recycle ratio will intensify the water-rich stream to the regeneration column, subsequently lowering the separation performance of the tray column.

6.8. Observation of water content and water dew point temperature Based on Fig. 7, the water dew point temperature decreases loga- rithmically with the drop in moisture content in the sales gas. A discontinuous function can be described for the water dew point temperature as a function of water content. Referring to the simulated data, state of the Eq. (2) depends on the water content of sales gas. First state is applied when water content is 0.0317 lb MMscf⁻¹or higher whereas the second state is introduced when water content is lower than 0.0317 lb MMscf⁻¹. The simulated data for water dew point temperature is better correlated to the water content due to its discontinuity property than the correlation between the independent variables. Therefore, the three manipulated variables are initially compared with the water content in the dry natural gas before evaluating the water dew point temperature of the dry natural gas. The mathematical equations for Trendline 1 and Trendline 2 were displayed in Eq. (2).

7. Optimization model

The objective function of the model in Eq. (7) is based on the minimization of operating cost of the glycol dehydration process. The operating cost is a function of duties of reboiler (Qr), hot oil (Qh), condenser (Qc), and pump (QP) as well as flow rates of TEG make-up stream (FTEG), Drizo solvent (FS), and CO2-eq emission(FCO2).

Minimise Operating Cost (7)

Fig. 8. Sensitivity analysis for total operating cost against deviation from the base value.

Fig. 9.Pareto front for trade-off between minimum total operating cost against the water dew point temperature of the sales gas.

(13)

Operating cost=CTEGFTEG+CsFs+CRQr+CHQ_h+CPQP+CCWQc

+CCO2FCO2

(8) where CTEG, Cs,CR, CH,CP,CCW and CCO2 denote the unit price of TEG, Drizo solvent, reboiler duty, hot oil duty, cooling water, electricity consumption for pumps and carbon emission penalty cost respectively.

All observable parameters (i.e. except water content (WH2O) which are derived from the reduce order of polynomial equations will be translated into operating cost of the dehydration process.

The reduced order model is solved with objective in Equation(7), subject to the system constraints from Eqs. (3)-(6), a minimum operating cost of 76.63 $/h was obtained. This means a reduction of 34.6% from 117.25 $/h of the base case model. With an annual operating time of 8000 h, the total operating cost of the optimized system is hence determined as $613 000 per annum, with cost breakdown listed in Table 6.

Based on the summary of validation of response parameters, all reduced order non-linear regression equations were obtained with low discrepancies of less than 1% except for pump power (QP)(see Table 7).

The 2.15% deviation in pump power of the two models is due to the sensitivity issues caused by high number of recycle loops in the complex process of Drizo dehydration process. The comparison between the response parameters given by the base case design variables and the optimized values is illustrated in Table 8. Table 8 shows the reduction in TEG loss due to entrainment has reduced by 69%, thus lowering the makeup of TEG flow in the recycle stream. Based on the optimized model, flow ratio of regeneration column overhead stream (Nratio) is to be increased, so that more TEG can be recovered from the aqueous reflux stream S8 (see Table 8). Since the objective function is to minimize the operating cost of the glycol dehydration process, the cost of TEG make- up flow under the proposed configurations have decreased by threefold after trading off with high reboiler and condenser cost, while still maintaining the water dew point temperature of the sales gas to be (lower than − 50 ^◦C). Hence, it is interesting to note that despite common engineering judgement to reduce energy consumption, the mathematical model proposes to increase energy duty in order to minimize the operating cost for this case study. This leads to lower operating cost for the natural gas TEG dehydration process.

8. Sensitivity analysis of variables on the cost minimization Sensitivity analysis was employed to understand the degree of influence in raw material and utility costs, as well as carbon emission penalty on the operating cost. Sensitivity analysis is important in process engineering as it presents a better overview in decision making with different (numerical) outcomes, as well as producing a more reliable predictions in the area of improvement (Incerti et al., 2019; Sin et al., 2009).

As shown in Fig. 8, it is visible that the total operating cost is most sensitive towards hot utility cost, due to, due to the large heating duty required to ensure an efficient treatment of raw natural gas. On the other hand, makeup cost of TEG also incurs significant contribution to the operating cost, despite of the insignificant makeup volume of TEG, as compared to Drizo solvent. This is due to the high unit price of TEG, as compared to Drizo solvent (see Fig. 8).

9. Pareto front optimization

By varying the water dew point using constraint in Eq. (3), the model is solved repeatedly with the objective function in Eq. (7). This results with a Pareto front plotted in Fig. 9. Since the water dew point temperature is the limiting factor for the pipeline specification for this case study, it affects the minimum total operating cost of the glycol dehydration process.

As discussed earlier (see Section 6), higher energy consumption, larger TEG makeup flow and Drizo solvent flowrate are required to increase the dehydration efficiency of natural gas. Thus, a less stringent water dew point specification will result in a lower operating cost, as shown in Fig. 9.

10. Conclusion

Plant optimization is vital in maximising profits, increasing production efficiencies and yield. The purpose of this work is to develop a practical reduced order model using an integrated simulation- optimization approach to minimize the operating cost of an existing glycol dehydration process. The approach makes use of Aspen HYSYS for the development of a steady-state simulation model. Important parameters are identified from the model and incorporated into a newly developed non-linear reduced order models to facilitate optimization and ease of use by process engineers.

The developed models showed high R²values (>0.99) and which were then utilized to optimize the processes to minimize total operating cost while meeting the desired water dew point. The integrated simulation-optimization approach was fairly accurate with a maximum discrepancy of 2.15% when the optimization value is compared with the simulation value. The percentage difference particularly caused by the sensitivity level of the ‘Recycle’ logical unit operation when converging the simulation model. The total operating cost is reduced by 34.6%, from $ 938 017 to $ 613 036. Sensitivity analysis revealed that the reboiler temperature has a substantial impact on the operating cost compared to other variables.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

All data of this study are found in the paper.

CRediT authorship contribution statement

Daniel Jia Sheng Chong: Formal analysis, Investigation, Method- ology, Software, Writing – original draft. Dominic C.Y. Foo: Concep- tualization, Investigation, Supervision, Writing – review & editing.

Zulfan Adi Putra: Conceptualization, Supervision, Writing – review &

editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.