A Parametric Sensitivity Study and Comparison Analysis on Multiple

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A Parametric Sensitivity Study and Comparison Analysis on Multiple Air Separation Processes Jieyu Zheng, hengyang Ye, Yanzhong Li, Yongbin Yang, and Biao Si Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b06046 • Publication Date (Web): 02 May 2019 Downloaded from http://pubs.acs.org on May 3, 2019

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Industrial & Engineering Chemistry Research

A Parametric Sensitivity Study and Comparison Analysis on Multiple Air Separation Processes Jieyu Zheng†, Hengyang Ye†, Yanzhong Li*,†,‡, Yongbin Yang†, Biao Si† † School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China ‡ State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

ABSTRACT: Flowsheet grassroots-design optimization is one of the promising debottlenecking operations for energy conservation of large cryogenic air separation units. In this paper several air separation schemes including single-column, double-column and triple-column cycles were theoretically compared and discussed to meet the diverse purity demands of industrial gases in different areas. All the proposed processes were rigorously verified via HYSYS platform processing the same amount of feed air of 10,000 Nm3/h. Both single and triple column processes presented considerable energy saving potentials compared with conventional double-column process with an average specific energy consumption decrease of 12.3%~25.9%. And triple column distillation 1

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scheme was more sensitive to oxygen product purity changes. A parametric sensitivity study was conducted using principal component analysis (PCA) method to investigate the influence of some important systematic parameters on novel triple-column cycle design. PCA method is fast enough to process large amounts of data, and is able to reflect more data essence compared with the sensitivity analysis method. The obtained results implied that the HP split ratio and oxygen purity have a greater impact on selection range of design parameter while optimal MP split ratio existed under certain FLP conditions. Subsystem contribution on the overall plant operation performance could be evaluated based on variance contribution. Heat-exchanger, distillation and refrigeration subsystem accounted for 41%, 30% and 24%, respectively. The obtained result has a great engineering significance for air separation engineering conceptual design.

KEYWORDS: Energy conservation; air separation; triple-column process; principal component analysis 1. Introduction Industrial gases such as oxygen (O2), nitrogen (N2), and argon (Ar) are vital for modern industries1. The production of large quantities of high-purity industrial gas still depends on cryogenic air separation method. The leading consumer of 2


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industrious gases is the steelmaking industries where large quantity of 99.6% purity oxygen is needed. Another main consumer of industrial gases is the so-called “three chemical industries”, namely, petrochemical, fertilizer, and coal liquefaction. These industries generally consume about 21% of O2 in total 2. Due to increased concern regarding CO2 emissions in recent years, oxy-combustion power plants have become a new application growth area of large scale air separation units (ASUs). For example, a 500 MWe coal fired plant boiler would consume around 292,000 m3/h oxygen 3. It was reported that the purity of oxygen product is typically around 95% 3, 4. As noted by Kerry 5, pyro-metallurgical and many chemical operations did not require pure oxygen, and for many years air-separation designers strove to find an economically optimum solution for these industries. Table 1 shows the argon content in oxygen product varied with oxygen purity. As shown in the table, the nitrogen component in 98% pure oxygen is less than 0.1%, and traceless in 99.6% pure oxygen. In steelmaking industry, dangerous by-products will be produced in some oxy-combustion processes if nitrogen is introduced. To avoid this, the corresponding oxygen products must have a minimum purity of 98%, limiting nitrogen impurity in a unit oxygen product to less than 1000 ppm. Table 1 Argon Content in Oxygen Product

3



Oxygen

Argon

Nitrogen

95%

3.65%

1.35%

98%

2.00%

less than 0.1%

99.6%

0.40%

Traces

Therefore, in most cases the oxygen purity around 95%~98% is enough to meet the practical requirements. The conventional cryogenic ASU designed for producing high purity oxygen and nitrogen gases is inefficient when just low purity gaseous oxygen and nitrogen products are needed. As higher oxygen purity leads to higher energy consumption, the difference of oxygen producing power consumption between the purity of 95% and 99.5% may be as much as 10%, and it may be as much as 6.5% for the case between 98% and 99.5% 5. The significance of energy conservation increases as the production scale enlarged where 1% decrease in energy efficiency may cause an extra annual operating cost of as much as a million in dollar. When discussing energy efficiency improvement of ASU, the primary concern comes to the improvements of cryogenic air distillation system. The key point is lowering the operational pressure of the columns therefore decreasing the max main air compressor (MAC) exhaust pressure. Moreover, the relative volatility of

4


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N2/O2 mixtures will increase at lower separation pressure, which will further improve the separation efficiency of distillation column. Efforts have been made to establish heat load coupling between the bottom end of the low pressure column (LPC) and the top end of the high pressure column (HPC), which results in a commonly seen structure known as Linde double-column air separation system. One alternative is the so-called “single-column” process which uses heat pump technology in perfecting the distillation system. The pure nitrogen stream circulates within the system as the heat medium. Thereby, the irreversibility of both distillation system and heat exchange network is reduced. Usually a portion of the overhead vapor (principally nitrogen) is recompressed by a compressor and re-condensed as distillation column reflux by exchanging its sensible and latent heat to the bottom liquid product (principally oxygen). Kansha

6

proposed a self-heat recuperation

process. Simulation verification reveals that the power consumption was decreased by more than 36% compared with the conventional cryogenic air separation process when producing 99.99 mol% O2. In another case

7

when

producing O2 with low purity (95 mol%), the energy requirement of the proposed cryogenic air separation process was reduced by 20.2% compared with the conventional process. The vapor recompression process can be done either at sub-ambient

temperature

or

ambient 5

temperature.


Generally,

a

lower


compression temperature would lead to greater power saving. For this reason, Zhou et al

8

constructed and simulated four typical configurations of the

single-column processes at various cryogenic nitrogen compression temperatures. A minimum temperature around 93.3 K was specified for two of the four disclosed configurations. However, the calculation results implied that the best energy saving performance is achieved by the one whose nitrogen compression was around ambient temperature. Fu et al

9

analyzed this phenomenon and

suggested that the compression work should not be introduced to sub-ambient unit. Otherwise the compression heat would need to be removed by expensive refrigeration. Based upon it, a new configuration termed as RVRC heat pump10 was proposed where the overhead vapor from the distillation column was rewarmed to ambient temperature before being compressed, and thus the heat could be removed by relatively cheaper cooling water. Another practical way is to use an intermediate condenser/reboiler system. The original design named Oxyton cycle

5

is to use a third column operating at a

medium pressure to redistribute reboiling. Thus such a process is named “triple-column process” for simplicity. Besides placing a third distillation column, a variation of this process is to install an intermediate reboiler in the low pressure column (LPC), which may also be called dual-reboiler layout. Another feature of such process is that, other than the usual option of vapor nitrogen & liquid 6


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oxygen, a different condenser/reboiler heat exchange medium combination are used, such as vapor nitrogen & oxygen enriched liquid or feed air mixture & liquid oxygen. However complicated it may seems, the main attempt is to reduce the irreversibility of distillation process by using multi-reboiler cycle or multi-column cycle so as to find out an optimizing way for producing low purity oxygen and gaseous nitrogen with large quantities. Such variations have been further developed and realized in the industry, mainly for the production of low purity oxygen and pure nitrogen with elevated pressure

11-14.

A similar but

simpler design proposed by Linde Company was introduced by Stanley Santos 15 in his lecture for IEA Greenhouse Gas R&D Programme in 2010. However, no public energy consumption data were provided. The most cited literature introducing triple column cycle was written by Higginbotham et al 3 from APCI, in the paper several innovative air separation cycles were compared on the same basis used by Darde et al. 16 for producing 5400 tonnes per day contained oxygen at 95% purity and 1.1 bar(a) pressure. However, after giving brief introduction of the proposed cycles, the author put more energy into exploring integration with other elements of the system, discussing execution strategies for air separation unit projects as well as comparing commercial models. Although there is no shortage of various comparison analysis between novel air separation process and conventional air separation process in literatures, 7



there still lacks solid foundation as for a more accurate energy performance predictions. The air is always assumed as a binary N2/O2 mixture, since two-component distillation is much easier to convergence compared with three-component distillation, such simplified specification would cause huge systematic errors when making comparisons. Also to the author's knowledge, quite few comprehensive comparisons have been made between triple-column and single-column schemes. Fu et al. 10 was among the few authors who made an overall comparison between all the state-of-the-art processes available in literatures. However the comparison somehow went halfway since only the single-column processes were optimized but the triple-column process which was used as reference wasn’t thoroughly investigated. That’s because no technical details were disclosed in literatures about the triple-column layout let alone a thorough optimization. Important parameters can be identified via sensitivity analysis in an energy system on the objective function describing energy, exergy or thermo-economic targets. Fu10 investigated the effects of temperature difference in the condenser/reboiler exchanger, cooling water temperature and isentropic compressor efficiency on the plant performance novel air separation process applying RVRC heat pumps. Mehdi Mehrpooya 17 performed a typical sensitivity analysis investigating effects of six key parameters on the overall process 8


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performance of a novel cryogenic air separation process with LNG (liquefied natural gas) cold energy utilization.

Masood Jalali Zonouz

18

performed a

parametric analysis on the integrated cryogenic air separation unit (ASU), oxy-fuel carbon dioxide power cycle and LNG vaporization process, which mainly optimized the design parameters of pump, compressor and heat exchanger based on energy and exergy efficiency. And similar work was done where exergoeconomic and sensitivity analyses were performed

19.

Due to the

complexity of large scale triple-column configuration, data scale for sensitivity analysis increases explosively with number of optional variables. Therefore, choices have to be made on which variables should be constrained to get a more convincing sensitivity coefficient. Presently, variable selection for sensitivity analysis of air separation process heavily relies on heuristics and engineering experiences

7, 10, 19.

Operational performance and manufacture standard of

existing equipment are necessarily investigated in the very beginning of simulation. To clarify the operational characteristics of the triple-column system, measurable and adjustable parameters, such as three split ratios and expander inlet temperature were therefore chosen as variables for sensitivity analysis in the paper. Since there exits potential correlation between variables, measures have to be taken to reduce the deviation. The Principal Component Analysis (PCA) method dates back to the multivariate transformation analysis of non-random 9



variables created by K. Pearson in 1901. In 1933, HA. Hotelling extended it to random variables

20.

It’s currently widely used with the medical, management

science, statistics, engineering and other disciplines. It is achieved under the assumption that features that exhibit high variance are more likely to make good distinctions between categories. Unlike the conventional sensitivity analysis method which focuses on relationship between variables and objects, the PCA method is used to estimate the correlation structure of the variables. Large amounts of data can be fast processed and explained in a more essential way. Especially for studying interrelation of highly integrated energy system such as the novel triple-column process. The main purpose of this paper is to analyze the energy consumption of different process layouts. In the present study, promising novel layouts are proposed, discussed and thoroughly investigated in the following sections by conducting rigid trinary air model simulation and comprehensive energy consumption performance comparison with conventional double-column layout. On the basis of the simulation results, a parametric sensitivity analysis is first conducted on triple-column distillation layout to predict its design parameter effect and investigate its operating characteristics. Finally, to overall evaluate the process, an attempt is firstly made to study its interrelation and quantified the

10


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subsystem contribution on the overall plant operation performance using PCA methods. 2 Process overview and description 2.1 Conventional double-column air separation process There is a long-standing advice from experienced engineers that “never touch a running system”, wherefore any ignorance of the existing process designs will probably lead to extra power penalty and unexpected shutdown. Originally proposed by Carl Linde in 1905, the classic double column scheme has been developed for over a century. After being fine-tuned by generations of engineers, it is extremely stable and efficient in producing ambient pressure gas product from fractional distillation of liquid air. Figure 1 demonstrates a detailed flowsheet of conventional ASP, the filtrated feed air drawn from atmosphere is compressed to around 500~600 kPa, and cooled to 283.15 K, after being purified by pre-purification unit (PPU) to exclude impurities (mainly H2O and CO2 to avoid blockage in the pipe), the clean and dry feed air is then divided into stream A and B, stream A is cooled against the cold returning gases in the main heat exchanger as the feedstock of the LPC, stream B is further compressed and then expanded to yield complementary cold-energy for the cold box before entering HPC as one of its two feeds. Another feed is the oxygen enriched liquefied air from the bottom of the LPC which is throttled beforehand and is then introduced 11



into the middle of HPC. The overhead nitrogen in the LPC is condensed in the condenser/reboiler heat-exchanger, and part of the condensed nitrogen is throttled to operating pressure of the HPC as its reflux. Oxygen, on the other hand, is vaporized as rising vapor of the HPC. For such a process, nitrogen is produced at the top of the HPC and LPC while oxygen at the bottom of the HPC. To obtain extremely high purity product, the argon presents in the air must be considered as a third component and can be removed in a nitrogen enriched draw-off stream from the HPC, which is called “waste nitrogen” in air separation industry. Alternatively, an extra argon extraction system can be selected, which including a crude argon column, an optional pure argon column and their auxiliary equipment consist of pumps and pipes. All leaving streams from the distillation column are with plenty of cold energies which should be cascade recovered previously in main heat exchanger and subcooler before it is released into the environment.

12


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V-2

TURBINE

COM COOLER

V-1 S UBCOOLER

WN N2

O2

UPPER COLUMN

MHX

LOWER COLUMN

IMPURITIES PPU

S PLITER AIR COMPRES S OR

COOLER

FEED AIR

Figure 1. Flowsheet of conventional ASP The main condenser/reboiler is a key device which is responsible for thermally connecting LPC and HPC. Approximately, it’s assumed that the overhead condensing nitrogen as a heat source and the bottom boiling oxygen as heat sink, the huge heat load between the two makes it significant to improve their heat transfer efficiency. The temperature difference of the condenser/reboiler is an important fact regarding the overall energy performance. It could be adjusted by changing the operating pressure of the LPC and HPC, which was directly relevant to compressor shaft work. An increase in the temperature difference of 13



only 0.1K for example, results in an increase in power of around 0.5%, which would be a considerable cost for large plants

21.

Usually a thermo-syphon type

reboiler is more commonly used in the conventional design, with which the static effect of oxygen is not negligible, and the temperature difference is 1.5~2 K. An alternative is to use downflow type reboiler, where the liquid to be vaporized flows through the heat transfer tubes or passages from top to bottom of the exchanger with the aid of gravity. And it allows a smaller temperature difference somewhere around 1 K. 2.2 Heat pump distillation process Previous researchers

22

had modularized the air separation process into four

basic operations of heat exchange, refrigeration, distillation, and compression. Quite naturally, the complex ASP can thus be divided into four corresponding subsystems. By combining distillation subsystem with heat exchange subsystem, a novel self-heat recuperation technology was proposed and developed by Kansha 6. Both latent and sensible heat was carefully matched with suitable heat source in the proposed system. The original objective Kansha proposed his invention was to optimize heat exchange network thermodynamically, yet his original version was too complicated for practical use. It will greatly increase the overall system instability to design such a highly integrated compact heat-exchanger network. Therefore Zhou8 and Fu10 further improved this 14


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technology and proposed their own retrofitting schemes for cryogenic air separation, which mainly simplified the heat exchange subsystem to increase the practicality. The refrigeration subsystem, which most authors have neglected, however played the major X-factor for the performance of novel single-column ASP with self-heat recuperation technology. Either feed air or pressurized nitrogen is a possible medium to add refrigeration into cold box with combined turbine booster and expander. To investigate the influence of refrigeration mode on the single-column ASP system performance, two detailed single-column ASP with either N2 or air booster expansion are first proposed and are compared with conventional double and triple-column ASP afterwards. Figure 2 and Figure 3 demonstrate detailed flowsheet, and are referred to as Cycle I and Cycle II respectively in the following chapter.

Cyc -Co m

Turbo

M-Co m N2 Dis tillatio n Co lumn

M-Co nde ns e r

O2 N2

S ubc o o le r

M-He ate r Impuritie s

M-Re bo ile r

PPU DCA

Blo we r

Air

LO

Figure 2. Flowsheet of single-column ASP with N2 booster expansion 15



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Turbo

Cyc -Co m

M-Co m Dis tillatio n Co lumn

M-Co nde ns e r

O2 N2

S ubc o o le r

M-He ate r Impuritie s

M-Re bo ile r

PPU DCA

Blo we r Air

LO

Figure 3. Flowsheet of single-column ASP with air booster expansion 2.3 Novel triple-column distillation process A preliminary process flow scheme for triple column ASP is showcase in Figure 4.

Vapo r N2

LP Co lumn

MP Co lumn Fe e d Air

Liquid O2

Liquid N2 HP Co lumn Fe e d Air

Ric h Liquid

Figure 4. Preliminary process flow scheme for triple column ASP 16


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Due to variation of operating pressure, the feed air is compressed diversely. It is recommended that a multiple-stage compressor with side extraction be used for convenient production of compressed air at different pressure level. The nitrogen reflux of the HPC is produced by the main condenser/reboiler, like the case in conventional double column cycle. The oxygen-rich liquid air flows at the lower end of HPC are pumped into the MPC and LPC respectively after depressurizing themselves to appropriate pressure. The oxygen-rich liquid air at the lower end of the MPC is pumped to intermediate condenser/reboiler located at the top end of the MPC, where it release its cold energy to the uprising medium pressure gaseous nitrogen. The nitrogen flow then condensed and is partially sent back to the MPC as its reflux, while the rest part acts as the reflux of the LPC. The subsequent oxygen-rich air mixture at the top end of the MPC is then sent separately onto different stage of the LPC for further refinement. High purity gaseous nitrogen is obtained at the top-end of the LPC while high purity oxygen from the bottom. A more complete flowsheet is showcase in Figure 5, up-to-date pressurized expansion refrigeration system is employed which consist of a booster end (COM) and expansion end (TURBINE). Part of compressed air is secondarily pressurized through booster end and successively cooled by booster after-cooler and main heat-exchanger before it is extracted from the middle of the compact 17



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multi-flow heat exchanger. This part is then expanded through turbine to operational pressure of the LP column acting as Lachman gas by that means the LP column distillation potential is fulfilled. For MP column, the reflux nitrogen is refrigerated by the self-throttling oxygen enriched liquid air at the bottom and the MP air feed acts as its uprising gas. The HP column is mainly in charge of separating nitrogen component while the LP column producing all the required gas and liquid products.

COM Vapor N2

COOLER

LP split MP Column

MP split

LP Column

Feed Air Vapor O2

Liquid O2

TURBINE Liquid N2 HP Column

Feed Air

IMPURITIES PPU II

PPU I

COOLER

AIR COMPRESSOR II

HP split

Rich Liquid

SPLITER

FEED AIR

AIR COMPRESSOR I

COOLER

Figure 5. Flowsheet of triple-column ASP 3. Characteristics and novel analysis method of triple-column process 18


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3.1 Important parameters for triple-column process The oxygen recovery ηO2 is a parameter representing the perfection of air separation system which is of significant importance. The definition is expressed as follow:

O  2

xO 2 , GOX M GOX  xO 2 , LOX M LOX xO 2 , air M air

(1)

Where xO2,GOX and xO2,LOX is the molar oxygen purity of gaseous and liquid oxygen product, respectively. MGOX and MLOX is the molar flow rate of gaseous and liquid oxygen product, respectively. xO2,air and Mair in denominator is the molar oxygen purity and molar flow rate of feed air, respectively. The feed air inlet temperature Tair.in is another important parameter indicating the refrigerating capacity the refrigeration subsystem is to offer since the higher the temperature is the higher enthalpy drop is achieved To better characterize the scheme performance, several split ratios are brought in, the calculating methods are shown as follows:

FLP 

FMP 

FHP 

mEXPair mLPair mMPliq.air mliq.air

mHPair mair 19


(2)

(3)

(4)


The low pressure (LP) split ratio FLP is expressed as the ratio of the molar flow rate of the expanded feed air to the molar flow rate of all the LP feed air produced by side extraction, as is shown in eq(2). It is a parameter reflecting the cold energy deficit of the whole system, and having proportional relation with the liquid production of system. The medium pressure (MP) split ratio FMP is expressed as the ratio of the molar flow rate of the MP oxygen-rich liquid air to the molar flow rate of all the rich liquid drawn from the HPC bottom, the former one is then sent to the middle of the MPC. This parameter is set forth on the purpose of apportioning rich liquid among the HPC and LPC, which essentially reflects the reboiling distribution of the scheme. The high pressure (HP) split ratio FHP is expressed as the ratio of the molar flow rate of HP air feed to the molar flow rate of air feed from front end of the air compressor. The HP split ratio FHP together with MP split ratio FMP plays an important role in quantifying similarity of the present design to traditional double column design. The closer the values are to 1 and 0 respectively, the more similar the scheme is to traditional one. It’s noteworthy that the value of FHP directly affects the air compressor power consumption, since an increase in FHP means a larger percentage of feed air is highly pressurized. 3.2 Principal component analysis method 20


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The initial objective of PCA is to take out as much information as possible from the original statistics, by reassembling the original variables into a new set of integrated variables that mutually are independent. A set of relevant variables are converted through orthogonal transform into a set of linearly irrelevant variables, which are called principal components, the ith principal component may be expressed as follows: PCi=ai,1X1+ai,2X2+……+ai,kXk

(5)

The basic steps for principal component analysis are as follows: 1. Data standardization

xi 

Xi - Xi Si

(6)

2. Determine the correlation coefficient matrix

 r11  r R   21 ...   rn1

r12 r22 ... rn 2

... ... ... ...

r1n   r2 n  ...  rnn 

(7)

Where: rij= rji (i，j=1，2，…，n) n

rij 

 (x k 1

n

 (x k 1

ki

 xi )( x kj  x j )

 xi )

2

ki

n

 (x k 1

(8)

 xj)

2

kj

21



3. Computation of eigenvalues and eigenvectors 4. Rotation of principal components 5. Calculate the principal component loadings 6. Calculate the principal component scores Rotation is a series of mathematical methods which make the component load matrix easier to interpret, and also a beneficial trial to denoise the components as much as possible. There are two ways to rotate: 1. Orthogonal rotation makes the selected components remain uncorrelated; 2. Oblique rotation causes the selected components to become relevant. The rotation method is also different according to the definition of denoise. The most popular one is the orthogonal varimax rotation, it attempts to load the column array denoise, so that each component is just a finite set of variables (i.e. load matrix per column only a few large load, the other is a small load) 4. Result and Discussion 4.1 Process simulation specifications All the presented cycles are rigorously simulated with Aspen HYSYS 2006. The Peng-Robinson (PR) equation of states (EOS) with modified binary interaction coefficient

23

is used. An identical operation condition is set for all cycles: air

flow-rate at 10,000Nm3/h, inlet temperature of MAC at 288.15 K. The outlet temperature is also 288.15 K for all compressor after-coolers. The feed air is 22


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assumed as a ternary mixture of nitrogen, oxygen and argon (78.118 %, 20.95% and 0.932% in mole fraction, respectively) in this paper. To simplify the comparison and focus on the perspective of process layout, a total pressure loss of 30 kPa is set for all cycles. Important systematic parameters in this study are all listed in Table 2. A minimum temperature difference of 1.5 K is chosen for the multi-stream heat exchangers in this study. Table 2. Important systematic parameters Item

Value

Number of plates for HP Column

30

Number of plates for HP Column in 40 Double-Column ASP Number of plates for MP Column

30

Number of plates for LP Column in all 80 ASPs Compressor adiabatic efficiency

0.80

Expander adiabatic efficiency

0.75

23



Hot side MHX temperature difference

3~5 K

Cold side MHX temperature difference

2~3 K

Minimum temperature difference in 1.5 K Main Heat Exchanger Minimum temperature difference in 1.5 K Main condenser/reboiler Temperature

difference

in

MP 1K

condenser/reboiler As the most commercially successful ASP presently, Linde process or double column process is chosen as the basis for the coming comparison and is referred to as Cycle base. Figure 2 and Figure 3 demonstrate detailed flowsheet evolved from the literatures, and is referred to as Cycle I and Cycle II respectively. The triple column ASP in Figure 5 is referred to as Cycle III naturally. 4.2 Overall comparisons

24


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0.42

specific energy consumption (kW·h·m-3)

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


0.40

double-column process single-column process with N2 expander

0.38

single-column process with air expander triple-column process

0.36 0.34 0.32 0.30 0.28 0.26 0.95

0.96

0.97

0.98

0.99

1.00

Oxygen purity

Figure 6. Effect of oxygen recovery on unit oxygen energy consumption Typical specific energy consumption βO2 of ASU24 was defined as follows:

O  2

M

W

(9)

shaft ,i

GOX ,i

 3

M

LIQ, i

Where ΣWshaft,i, ΣMGOX,i, ΣMLIQ,i represented total shaft work, total gas oxygen product molar flow rate and total liquid product molar flow rate, respectively. The index βO2 validates itself especially for overall energy consumption performance evaluation of large-scale ASU with relatively small amount of liquid products. For cryogenic air separation process, product purity is an important factor affecting power consumption. Figure 6 presents how oxygen purity xO2 affects the specific energy consumption βO2 of all the four proposed flowsheets. Apparently, the higher the purity of the O2 product is, the greater the amount of power is required in the process. For the conventional process, βO2 is reduced by 25



0.386~0.368 kW·h/m3O2 when the O2 purity decreases from 99.6% to 95%. Correspondingly, for the proposed processes Cycle I, Cycle II and Cycle III, βO2 is respectively reduced by 0.347~0.315 kW·h/m3O2, 0.342~0.309 kW·h/m3O2 and 0.298~0.265 kW·h/m3O2. Compared with Cycle base, the specific energy consumption βO2 of Cycle I decrease by 9.9%~14.3% when the O2 purity decreases from 99.6% to 95%, while that of Cycle II and Cycle III is 11.3%~16.1% and 22.6%~27.8%, respectively. Similar variation relation can be observed for all flowsheets as specific energy consumption βO2 decrease with oxygen purity xO2. For conventional double column process, the variation relation is more consistent with linear change compared with the rest three Cycles. A comparatively sharper βO2 decrease can be observed for Cycle III especially when oxygen purity is within the range from 0.99 to 1. The variation relation of Cycle I and Cycle II are similar as the two curves are basically parallel. Both decrease slightly faster within the range of 0.98 to 1. The variation relation differences reveal the specific effect of distillation subsystem on the overall ASP performance, and among them a triple column distillation scheme seems to be more sensitive to oxygen product purity changes. 4.3 Parameter sensitivity analysis

26


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Page 27 of 49

Some important systematic parameters like FLP, FMP, FHP, feed air inlet temperature Tair.in, oxygen recovery ηO2 are explored in the following section in term of their effect on the overall operation cost of Cycle III. 4.3.1 Effect of oxygen purity -3

specific energy consumption (kW·h·m )

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


0.31

0.20MPa， 0.5MPa

0.30

0.20MPa， 0.5MPa

0.22MPa， 0.5MPa 0.29 0.28 0.27 0.26 0.25 0.91

HP split 80% , oxygen purity 0.996 HP split 85% , oxygen purity 0.996 HP split 80% , oxygen purity 0.99 HP split 85% , oxygen purity 0.99 HP split 80% , oxygen purity 0.98 HP split 85% , oxygen purity 0.98 HP split 80% , oxygen purity 0.95 HP split 85% , oxygen purity 0.95 0.92

0.93

0.94

0.20MPa， 0.5MPa 0.20MPa， 0.5MPa

0.20MPa， 0.5MPa 0.20MPa， 0.5MPa 0.20MPa， 0.5MPa

0.95

0.96

0.97

0.98

0.99

1.00

oxygen recovery

Figure 7. Variation relationship of oxygen recovery vs. specific power consumption Figure 7 shows the variation relationships of oxygen recovery with specific power consumption where FHP and xO2 range from 0.8~0.85 and 99.6%~95%, respectively, and the MPC and HPC pressures are 0.2 MPa and 0.5MPa. It can be seen that, more energy savings can be achieved when producing less pure oxygen, the less the better. For the case when FHP=0.8, the specific energy consumption was reduced by 0.306~0.265 kW·h/m3O2 when the O2 purity decreased from 99.6% to 95%. And when FHP=0.85, the specific energy 27



consumption is reduced by 0.302~0.274 kW·h/m3O2 when the O2 purity decreases from 99.6% to 95%. Also as the picture reflects, the oxygen recovery is linearly negative related to specific power consumption for a certain data set with the same FHP and xO2. Videlicet, the oxygen recovery can be considered as an equivalent index like specific power consumption in some cases. This is an important hypothesis by which the next comparisons are made. 4.3.2 Effect of split ratios To investigate a certain ASU, efforts have to be made on improving the oxygen recovery ηO2 since it’s a typical index which denotes the perfection degree of air separation. FLP, FMP and FHP of Cycle III are crucial factors for operation. Figure 8 demonstrates a comprehensive effect of split ratio on oxygen recovery ηO2 of Cycle III in a wide range of different split ratios and oxygen product purity xO2. To analyze the complex interrelation of three split ratios and oxygen purity, the obtained data is arranged in a serial of grouped bar graphs. In part (a), 6 bar graphs are put together in a row which demonstrate effect of FMP on oxygen recovery ηO2 with fixed FHP and varied FLP (variation range 0.3~0.8) and xO2 (variation range 0.996~0.95). For comparison, the same number of graphs is arranged in part (b) with the same FLP and xO2 variation range. The HP split ratio FHP has a greater impact on oxygen recovery ηO2 when producing high purity oxygen within the range from 0.996 to 0.99. Such impact faded as oxygen purity 28


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decreased. When the oxygen purity of the product drops to 0.95, the FHP has almost no impact on ηO2. It's obvious that by choosing a smaller FHP, the adjustable range of the rest two split ratios will be expanded. There is an inconspicuous quadratic relationship between MP split ratio FMP and ηO2 which is quite obvious for the high FLP and xO2 cases (xO2=0.996; FLP=0.7~0.8). In other words, an optimal FMP design value can be selected in such cases. However, due to the actual engineering limitations, only part of the working conditions can be achieved for the rest cases. Therefore their corresponding ηO2 values change monotonically with FMP. Generally a higher FMP value was suggested when producing enriched oxygen (xO2 ≤ 0.99), while for producing pure oxygen with lower FLP value (FLP < 0.5), lower FMP value is suggested. By comparing data in the FLP range from 0.6 to 0.8, it’s obvious that the LP split ratio is correlated with oxygen recovery ηO2 at fixed FHP, FMP and xO2. The reason for this can be explained that increase the expanded LP feed air amount into the LPC will decrease the L/V ratio of LPC rectifying section, which is harmful to rectifying efficiency and therefore decrease the performance of LPC.

29



Page 30 of 49

FHP=0.8 xO

0.94

2

0.996 0.99 0.98 0.95

FLP=0.3

0.996 0.99 0.98 0.95

FLP=0.4

0.92 0.94

0.92 1.00

O

2

FLP=0.5

0.996 0.99 0.98 0.95

0.96 0.92 1.00 0.96

0.996 0.99 0.98 0.95

FLP=0.6

0.996 0.99 0.98 0.95

FLP=0.7

0.92 1.00 0.96 0.92 1.00 0.996 0.99 0.98 0.95

0.96 0.92

0.2

0.3

0.4

0.5

0.6

0.7

FMP (a)

30


0.8

0.9

1.0

FLP=0.8

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FHP=0.85 xO

0.94

2

0.92

0.996 0.99 0.98 0.95

0.94

FLP=0.3

0.996 0.99 0.98 0.95

FLP=0.4

0.996 0.99 0.98 0.95

FLP=0.5

0.996 0.99 0.98 0.95

FLP=0.6

0.996 0.99 0.98 0.95

FLP=0.7

0.996 0.99 0.98 0.95

FLP=0.8

0.92 1.00

O

2

0.96 0.92 1.00 0.96 0.92 1.00 0.96 0.92 1.00 0.96 0.92

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

FMP (b) Figure 8. Bar graphs of split ratio effect on O2 recovery 31


1.0


4.3.3 Effect of compressor efficiency and cooling temperature 1.20

Power consumption ratio

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

Interstage cooling temperature/°C 0 5 10 15 20 25 30 35 40

1.15 1.10 1.05 1.00 0.95 0.90 0.85 70

75

80

85

90

Compressor efficiency

Figure 9. Compressor efficiency vs. power consumption ratio The main air compressor contributes principally to the main energy consumption and cause the greatest exergy loss in the system. Previous researchers 7, 10 have related and analyzed the importance and concrete impact of compressor efficiency and cooling water temperature on the overall plant performance. A more adiabatically efficient compressor with lower cooling water temperature will achieve a smaller specific energy requirement. More specifically, every 1% decrease in specific energy consumption will required 0.7~1.0 percentage point increase of compressor efficiency or 2.5 °C cooling water temperature decrease. While in most cases, the compressor train is independent of the system hence making it possible a separate study on compressor performance. In this case, a two-stage compressor with 298.15 K inlet 32


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temperature and identical interstage adiabatic efficiency is studied. To make the results universal, a dimensionless power consumption ratio is defined, which is a ratio of certain calculation to that of a reference working condition. The reference working condition is with 15 °C interstage cooling temperature and 80% interstage adiabatic efficiency. It’s shown in Figure 9 that every 1% decrease in specific energy consumption will require a 0.9 percentage point increase of compressor efficiency or a 6.5 °C interstage cooling temperature decrease. 4.4 Principal component analysis of triple-column process Table 3. Data structure column Wshaft

ηO2

β

FHP

FMP

FLP

xO2

Tex_in

Tair_in

1

659.5

0.9928

0.2883

0.8

0.1994

0.88

0.95

131.91

NA

2

659.5

0.9932

0.2881

0.8

0.2173

0.87

0.95

131.67

NA

3

659.5

0.9934

0.2881

0.8

0.223

0.86

0.95

134

NA

64

667.3

0.9833

0.3031

0.82

0.1674

0.9

0.98

136.31

98.2985

65

667.3

0.9826

0.3033

0.82

0.1363

0.91

0.98

135.77

98.2991

……

33



Page 34 of 49

…… 519

628.5

0.9392

0.3028

0.72

0.5053

0.8

0.996

126.09

98.2572

To investigate the changing characteristics of the triple-column ASP, 519 data set of 9 key parameters including compressor shaft work Wshaft, oxygen recovery ηO2, specific power consumption β, HP split ratio FHP, MP split ratio FMP, LP split ratio FLP, oxygen product purity xO2, expander inlet temperature Tex_in, feed air inlet temperature Tair_in are analyzed. To reduce the dimension of the problem and put emphasis on influence of adjustable system parameters, the influence of compressor shaft work Wshaft and feed air inlet temperature Tair_in are neglected. The specific power consumption β in Table 3 is set as the target. A 6*6 scatterplot matrix is demonstrated in Figure 10, which visually reflects the relationship matrix for data groups. To make it more comprehensible, additional labels are pasted beside the x-axis and y-axis. The sub-figures in lower-triangular part show the correlation coefficient between each data group. Histograms on the matrix diagonals illustrate frequency distribution of each corresponding parameters. Scatterplots on the upper-triangular part visualize all the correlations between each two parameters. For example, the sub-figure marked

34


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“Cor=0.81” represents the correlation coefficient between FHP and FMP is equal to 0.81. And the scatterplot which is symmetrically located along the diagonal demonstrates the correlation between FHP and FMP where FMP is varied along the horizontal ordinate. The color of the dots represents the difference in oxygen purity, as is clearly shown in legend below. And the meaning of the rest sub-figure can be deduced from this. The red lines, however, are the smooth curves of each group of data, which better reflect the responsive relationship between variables. It can be seen from the graph that there is a strong correlation between several sets of data as their correlation coefficient is greater than 0.6. Among them, the dependence between MP split ratio FMP and LP split ratio FLP is the strongest with the correlation coefficient around 0.96. Because of the existence of such multi-collinearity, direct linear fitting will lead to greater uncertainty and error and thus a PCA analysis is needed. To make the component load matrix more interpretable, a varimax rotation is conducted and the analysis result is show in Figure 11. Three primary components can be obtained termed as R1, R2 and R3. The correlation matrix can be expressed quite clearly in Figure 11. The first component R1 is mainly a combination of FHP, FMP and FLP, which is obviously a parameter set concerning the heat-exchanger subsystem. R2 is mainly a combination of ηO2 and xO2, which is a parameter set concerning the distillation subsystem. And R3 in the end can 35



be explained as parameter set concerning the refrigeration subsystem since parameters like FHP and Tex_in are the major factors. Table 4 displays a very interesting proportion for each parameter set as R1 accounts for 41% of the variance and 30% for R2 and 24% for R3, which quantifies the subsystem contribution on the overall plant operation performance of power consumption.

36


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Page 37 of 49

Figure 10. Scatterplot matrix of adjustable system parameters 1.0

R1

R2

R3

0.8 0.6

Main component

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


0.4 0.2 0.0 -0.2

O

-0.4

FMP

-0.6

FMP

2

FLP

-0.8

xO

2

-1.0

Tex_in

Figure 11. Principal component load chart using varimax rotation Table 4. PCA analysis result RC1

RC2

RC3

SS loadings

2.48

1.82

1.43

Proportion Var

0.41

0.3

0.24

Cumulative Var

0.41

0.72

0.95

Proportion Explained

0.43

0.32

0.25

Cumulative Proportion

0.43

0.75

1

5. Conclusion

37



A parametric sensitivity analysis associates with PCA method is, for the first time, conducted to get an overall performance of novel triple-column process. Effects of various operational parameters on specified energy consumption are investigated. The interrelationship is also studied via PCA method. Also several promising novel distillation system layouts are discussed and compared in this study. The Peng-Robinson (PR) equation of states (EOS) with modified binary interaction coefficient is used to simulate all the processes using the trinary air model. Main conclusions of the study are listed below: 1. Both single column and triple column processes have a considerable energy saving potentials compared with conventional double-column process with a 12.3%~25.9% specific energy consumption decrease averagely. The triple column distillation scheme is more sensitive to oxygen product purity variation and has achieved the best energy savings in this study as its specific energy consumption βO2 decreases by 0.298~0.265 kW·h/m3O2 when the O2 purity decreases from 99.6% to 95%. 2. Sensitivity analysis results imply that the split ratios are crucial operational factors for triple-column ASUs. Among them, the HP split ratio FHP has a greater impact on oxygen recovery ηO2 when producing high purity oxygen within the range from 0.996 to 0.99. A smaller FHP will expand the adjustable range of the rest two split ratios. Generally a higher FMP value is suggested when producing 38


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enriched oxygen (xO2 ≤ 0.99), while for producing pure oxygen with lower FLP value (FLP < 0.5), lower FMP value is suggested. And every 1% decrease in specific energy consumption will required a 0.9 percentage point increase of compressor efficiency or a 6.5 °C interstage cooling temperature decrease. 3. The Principal Component Analysis (PCA) method is a useful tool in studying the interrelation of highly integrated energy system. Preliminary study results suggest that the proposed six parameters can be merged into three parameter set. R1 is mainly a combination of FHP, FMP and FLP, which is obviously a parameter set concerning the heat-exchanger subsystem. R2 is mainly a combination of ηO2 and xO2, which is a parameter set concerning the distillation subsystem. And R3 in the end can be explained as parameter set concerning the refrigeration subsystem since parameters like FHP and Tex_in are the major factors. 4. Interesting proportion value for each parameter set worth further discussion since it quantifies the subsystem contribution on the overall plant operation performance of power consumption. The heat-exchanger subsystem accounts for 41% of the variance and 30% for distillation subsystem and 24% for refrigeration subsystem. Massive data monitoring and storage requirement nowadays of the automatic controlled ASUs provide a big pile of messy data. Highly developed rapid computing technology provided a new solution to find order out of chaos. The 39



method of combining tradition energy analysis and statistic provide many interesting avenues for further system engineering research and industrial big data applications. Further research is needed as for more comprehensive parameters selection under more real conditions as well as interaction effect of parameters and their dynamic characteristics. ASSOCIATED CONTENT Supporting Information The sample data for PCA analysis and Results of simulation for Cycle base, Cycle I, Cycle II and Cycle III. AUTHOR INFORMATION Corresponding Author *Phone: +86(29)82668725; e-mail: [email protected]. ORCID Yanzhong Li: 0000-0002-0156-3169 Jieyu Zheng: 0000-0002-9858-0546 Notes The authors declare no competing financial interest. 40


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ACKNOWLEDGEMENTS This work was supported by National Key Research and Development Program (2018YFB0904400) and the National Natural Science Foundation of China (51876153). The authors would like to thank the organizations above for their financial support. Nomenclature F

flow split ratio

M

molar flow rate (Nm3/h)

T

temperature (K)

Wshaft

shaft work (kW)

X

observation value

x

standardized observation value

xO2

molar oxygen purity

R

correlation coefficient matrix

r

basic element of matrix R

S

standard deviation 41



Abbreviations ASP

air separation process

ASU

air separation unit

LNG

liquefied natural gas

LPC

low pressure column

MPC

medium pressure column

HPC

high pressure column

PPU

pre-purification unit

PCA

Principal Component Analysis

WEN

work exchange network

HEN

heat exchange network

GCC

Grand Composite Curve

Greek symbols β

specific power consumption (kW·h/Nm3O2)

ηO2

oxygen recovery (Nm3/Nm3) 42


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Subscript air

feed air

ex

expander

GOX

gaseous oxygen product

HP

high pressure

i

ith component

in

inlet

j

jth component

LIQ

liquid product

LP

low pressure

LOX

liquid oxygen product

MP

medium pressure

out

outlet

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For Table of Contents Only

48


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table of contents only 213x127mm (300 x 300 DPI)


A Parametric Sensitivity Study and Comparison Analysis on Multiple

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