Synthesis of work exchange networks for gas processing applications 3

Chapter Optimal Load Distribution in AP-XTM Refrigeration System CHAPTER OPTIMAL LOAD DISTRIBUTION in AP-XTM REFRIGERATION SYSTEM LNG plants are facing a broader range of process requirements than ever before due to surge in LNG demand in worldwide As a result, LNG facilities must be designed to meet a number of new challenges such as larger train capacity, wide range of environmental conditions, mixed refrigerant (MR) composition, NG feed conditions, and so on For many years, the propane pre-cooled mixed refrigerant (C3MR) process which has only two refrigerant cycles: Propane and MR, remained the dominant liquefaction process The versatility of this cycle makes it well-suited to accommodate this industry However, to mitigate the increasing demand a new liquefaction technology, AP-XTM refrigeration system, is introduced which has higher train capacity than the well known C3MR process Due to energy intensive nature of this relatively new technology a process optimization approach is presented in the following sections to minimize compressor power requirement 3.1 Technical Background of the AP-XTM Technology The AP-XTM system is an enhancement over the C3MR system which is currently the dominant NG liquefaction system utilized by LNG producers around the world This enhancement was motivated by the demand for greater LNG production capacity and cost savings associated with economies of scale With the increasing environmental pressure towards cleaner burning fuels such as NG, there had been an unprecedented surge in the demand for LNG With such high demands, greater LNG production rate is high on the 17    Chapter Optimal Load Distribution in AP-XTM Refrigeration System priority list in any plant construction or expansion plan The current C3MR technology is only rated up to a maximum of million ton per annum (mta) due to equipment limitations32 Single train capacities could not be further increased without drifting into the unproven size, performance, and reliability zones of refrigerant compressors Parallel trains are costly and not benefit from economies of scale The use of parallel equipments such as parallel compression faces problems of flow imbalances which poses unacceptable operational and safety concerns Heat exchanger size and capacity also limit such expansion On the other hand, the latest AP-XTM system has a nominal single train capacity of 7.8 mta, which is approximately 50% greater than the largest C3MR train currently in service32 This allows capital cost savings from economies of scale while maintaining C3MR’s industry leading high efficiency levels Figure 3.1 illustrates the basic schematic of the AP-XTM system on which the model formulation will be based on   LNG P1,N2 Sub-cooler P2,N2 N2-N2 Heat Exchanger N2 Cycle MCHE NG NG Precooler P1,MR TMR PR Cycle MR Cycle MR cooler TSW P2,PR P1,PR P2,MR TSW Figure 3.1 Generalized Schematic of the AP-XTM Refrigeration System 18    Chapter Optimal Load Distribution in AP-XTM Refrigeration System As mentioned earlier, the AP-XTM system is an evolution of the C3MR process, the flow schematic of PR and MR cycle except N2 cycle in Figure 3.1 is essentially the classical C3MR process In order to provide refrigeration at warm condition, high pressure superheated propane is cooled and condensed at ambient temperature using cooling water The saturated liquid propane is then throttled through a Joule-Thompson (JT) valve, producing two phase flow at reduced temperature and pressure The liquidvapor mixture of propane is completely vaporized as it passes through evaporators to cool the NG feed from ambient temperature down to approximately 240 K Moreover, the C3 loop also provides pre-cooling to the MR loop Note that the lowest pressure of PR stream in PR cycle is maintained at or above atmospheric pressure to avoid air leakage High pressure MR stream exiting from compressor is cooled using cooling water Then it is pre-cooled by the C3 loop This produces partially condensed MR which is further cooled and liquefied by low pressure and temperature MR stream in the main cryogenic heat exchanger (MCHE) The exiting stream is then let down in pressure resulting in partial flashing and a reduction in temperature According to technical publications by Air Products, a three or four staged pressure let down system is usually implemented to provide pre-cooling refrigeration at several pre-determined temperature levels, analogous to the cascade design This, in theory, provides greater matching of the hot and cold streams in the heat exchangers and therefore, improves cycle efficiency This cold MR stream is subsequently passed through the shell side of the MCHE, vaporizing itself to provide refrigeration for liquefying and sub-cooling the NG feed An optimally adjusted MR composition whose evaporation curve closely mimics the cooling 19    Chapter Optimal Load Distribution in AP-XTM Refrigeration System curve of the NG feed will enhance thermodynamic efficiency (greater reversibility) of the system and therefore, reduce compressor power requirements In the classical C3MR process, the MR loop is responsible for sub-cooling the NG feed down to approximately 111 K, producing LNG However, further increases in production capacity could not be accommodated as the refrigerant compressors are already at their size and performance limits On the other hand, the refrigeration loads of the C3 and MR cycle are reduced by shifting the sub-cooling refrigeration duty away from the MR cycle to N2 cycle in the AP-XTM system This allows more scope to increase production level while still utilizing proven equipment sizes similar to that of C3MR process In the AP-XTM system, the liquefied NG feed stream exits the MCHE at approximately 165 K before entering another Spiral Wound Heat Exchanger (SWHE) which sub-cools it down to 111 K utilizing the final N2 refrigeration cycle The third cycle is a simple N2 expansion loop Nitrogen gas is compressed to high pressure and cooled to ambient temperature using cooling water Further cooling is performed through a N2-N2 heat exchanger that uses cold low pressure nitrogen returning to the compressors after providing sub-cooling duty to feed stream in SWHE It is then let down in pressure through an expander (turbine) which further lowers the temperature The expander in AP-XTM process is known as compander This compander can be used to drive refrigerant compressor and/or a generator to produce electricity The exiting cold nitrogen stream from valve is then used to provide sub-cooling duty to the feed stream According to technical publications of Air-Products, this baseline AP-XTM system is capable of producing up to 7.8 mta of LNG Further increases in capacity to 8-10 mta may be possible by incorporating Air Products’ new proprietary technology such as the 20    Chapter Optimal Load Distribution in AP-XTM Refrigeration System Split-MRTM compressor-turbine configuration33 and split propane casing arrangement32 However, such enhancements will not be considered in this work and its incorporation may be included in future extensions of the current model 3.2 Problem Statement The objective of this work is to formulate a mathematical model for the generalized APXTM refrigeration system for a given refrigeration load that is able to optimally distribute compressor load among different cycles to minimize total compressor power demanded by all the three refrigerant cycles over different operation scenarios such as different feed composition, MR composition, and cooling water temperature Each scenario produces a set of optimal operating conditions of each cycle and is able to guide the operators to optimally response by changing operating conditions to shift compressor load among different cycles at different environmental and process conditions Let NG comes from the NG processing unit at temperature Tfeed_1 and pressure Pfeed with a known mass flow rate mfeed and composition fp, where p denotes components in NG As NG passes through each of the cycles, we consider no pressure drop After passing through all refrigeration cycles, LNG leaves the final N2 cycle at storage temperature Tfeed_4 and pressure Pfeed Note that it fixes overall feed cooling, liquefaction, and refrigeration duty q_feed_total provided by the entire refrigeration system; however, feed cooling duty provided by each cycle may vary with their operating conditions, cooling water supply, MR composition, and refrigerants’ mass flow rate 21    Chapter Optimal Load Distribution in AP-XTM Refrigeration System 3.3 NLP Formulation As the objective of this work is to obtain optimal operating condition for the latest APXTM refrigeration system for LNG production, no integer variable is required Components and energy balance involved in the process force the model to be non-linear Therefore, we develop a non linear programming (NLP) that optimally distribute load among different cycles to minimize total compressor power consumption The exact design of AP-XTM refrigeration system is unavailable in the public domain due to patent restriction by Air Products Hence, we use a generic flow diagram for this process Figure 3.2 illustrates the schematic of AP-XTM refrigeration system with its associated nomenclature at each node that is the basis of this optimization study Each section of this basic schematic is described wherever it is applicable Note that we will use the same nomenclature for temperatures and pressures in our model formulation indicated in Figure 3.2 Figure 3.2 Basic Schematic of the AP-XTM Refrigeration System 22    Chapter Optimal Load Distribution in AP-XTM Refrigeration System As AP-XTM process uses three different cycles consecutively to sub-cool and liquefy NG, energy balance among feed and refrigerants tells us, q _ feed _ total  q _ feed _ pr  q _ feed _ mr  q _ feed _ N2 (3.1) where, q_feed_pr, q_feed_mr, and q_feed_N2 indicate feed cooling, liquefaction, and refrigeration duty supplied by PR, MR, and N2 cycles respectively First, we formulate the model for feed stream exchanging energy with three different refrigeration cycles Second, we formulate model for PR cycle Third, we formulate model for MR cycle Fourth, we formulate for N2 cycle Finally, we define the objective function for this work that includes compressor load of each of the abovementioned cycles 3.3.1 Feed Stream First, NG enters PR refrigeration pre-cooler LNG-101 where it is pre-cooled to temperature Tfeed_2 We can calculate the enthalpy change, ∆Hfeed_pr of the feed stream as it passes through PR loop as follows H feed _ pr   T feed _ T feed _1 CPfeed dT  H Rfeed _  H Rfeed _1   (3.2) where, the first term (heat capacity) indicates the ideal enthalpy change of the feed stream and the remaining part accounts deviation from ideality called residual property Value of heat capacity CP and residual enthalpy  HR can be calculated via the following heat capacity correlations incorporating mixing rule and the generalized second virial correlation  C3 p  T  CPp  C1p  C p  sinh  C3 p   T     C5 p   T   C4p    sinh  C5 p    T          23    Chapter Optimal Load Distribution in AP-XTM Refrigeration System CPfeed   f pCPp0 p   1.097 0.894  H R  R  PTC feed  PPR feed 0.083    0.139  1.6 PTRfeed PTR4.2  feed     PPRfeed   f p  PRp p PTRfeed   f p  TRp p PTC feed   f p  TCp p where, PTRfeed and PPRfeed are the pseudo-reduced temperature and pressure and PTCfeed is the pseudo-critical temperature of feed stream whereas PRp, TRp, and TCp are the reduced pressure, reduced temperature, and critical temperature of component p Note that residual property approach is slightly less precise than compressibility approach at very high pressure34; however, its relative simplicity enables its application in this work as very high pressure is not expected in AP-XTM system Let mfeed is the mass flow rate of feed NG Hence, feed cooling duty provided by propane is,   q _ feed _ pr  m feed H feed _ pr   (3.3) Note that in AP-XTM system the PR cycle is designed to provide cooling but not liquefaction Hence, the feed temperature after propane cooler remains above its dew point Tfeed_dew which is a known constant as per definition T feed _  T feed _ dew (3.4) Moreover, to ensure heat exchange compliance with minimum temperature approach between feed and PR streams, we define the following constraints T feed _  Tpr _  Tmin   (3.5) 24    Chapter Optimal Load Distribution in AP-XTM Refrigeration System T feed _1  Tpr _  Tmin (3.6) where, ∆Tmin is the minimum temperature difference among two heat exchanging streams at entrance to or exit from heat exchange However, these constraints not ensure feasible heat exchange among cold and hot streams because of nonlinear nature of cold and hot composite curves16 Hence, higher ∆Tmin should be used to avoid infeasible heat exchange Next, the MR stream liquefies pre-cooled NG in an MCHE LNG-102 where NG converts into two phases Due to multi-component nature of feed NG, its H-T curve is highly non-linear To avoid this complexity, we utilize a back calculation method through the overall energy balance of the feed stream As we are directly calculating the cooling duty of PR and N2 cycle for feed stream by means of enthalpy calculations, it facilitates optimizer to calculate not only the cooling duty by MR, but also the inlet and outlet temperature of feed stream to MR cycle indirectly To ensure feasible heat exchange in MCHE, we define T feed _  Tmr _  Tmin   (3.7) T feed _  Tmr _  Tmin   (3.8) where, Tfeed_3 indicates the temperature of feed NG after passing through MCHE Finally, heat exchanger LNG-103 in N2 cycle is responsible for sub-cooling of liquid NG to its target / storage temperature coming from MCHE This enables the LNG to be stored and transported in liquid state by special tankers The enthalpy change of the liquid feed NG can be calculated as follows H feed _ N2   T feed _ T feed _1 CPL0, feed dT 25    Chapter Optimal Load Distribution in AP-XTM Refrigeration System Correction term that accounts for deviation from ideality is not mandatory for liquid stream as pressure has negligible effect on liquid properties; hence, the omission of the correction term should not result any significant deviation with respect to reality However, accuracy problem while calculating enthalpy was encountered because of N2 presents in feed stream The above correlation for N2 is only valid for a certain range of temperature which does not include predicted process conditions and thus, is not utilized in this work To circumvent this problem, the following empirical method is utilized Enthalpy data is collected from Aspen HYSYS 7.1 for a certain range of process condition of liquid feed stream Then, the following enthalpy-temperature correlation is regressed from the collected data utilizing MATLAB’s curve fitting tool H feed _ N   C 6T feed _  C 7T feed _  C 8T feed _  C 9T feed _  C10T feed _  C11   C12T feed _  C13T feed _  C14T feed _  C15T feed _  C16T feed _  C17  (3.9) Then, cooling duty provided by N2 cycle to sub-cool NG to target / storage condition is expressed as follows   q _ feed _ N  m feed H feed _ N2   (3.10) Moreover, the following constraints ensure temperature feasibility across LNG-103   T feed _  TN _  Tmin   (3.11)   T feed _  TN _  Tmin   (3.12) 3.3.2 PR Cycle Now, we model PR cycle which is the simplest refrigeration cycle among threes It only pre-cools feed and MR stream but not liquefies due to the fact that below 230 K its vapor pressure is less than atmospheric pressure Overall energy balance in PR cycle tells us,   q _ comp _ pr  q _ evap _ pr  q _ cooler _ pr  q _ valve _ pr (3.13) 26    Chapter Optimal Load Distribution in AP-XTM Refrigeration System N2 Cycle Feed Refrigeration Load 198 92 196 91 194 90 192 89 190 88 188 87 186 86 184 182 N2 Cycle Feed Refrigeration Load (MW) N2 Cycle Compression Load (MW) N2 Cycle Compression Load 85 1A                1B                 1C                 1D                 1E Feed Composition sets   Figure 3.6 N2 cycle compression and refrigeration load vs feed compositions 3.5.3 Case Study 2-Effect of MR Compositions Through Case Study 1, the model’s utility in optimizing the compression load in AP-XTM system through a range of operating feed conditions was demonstrated However, it could not be denied that the optimized power requirements are still on the higher side as compared to the reported 275 to 285 MW range by Air Product One of the probable causes of this discrepancy was the non-optimal MR compositions used in the current model Therefore, an improvement in the optimality of the MR compositions used in the optimization is of high priority, such that a more representative AP-XTM model can be constructed before a further case study on the effect of cooling water temperature is carried out in Case Study An optimal or near optimal MR composition will ensure closer matching of the hot and cold composite curves in heat exchanger Such small temperature driving forces 52    Chapter Optimal Load Distribution in AP-XTM Refrigeration System minimize thermodynamic irreversibility leading to greater system efficiency and lower power requirements A non-optimal MR composition, on the other hand, will result in large temperature differences This necessitates the increase in the difference between the condensing and evaporating pressures to prevent temperature crosses, thus more compression power is required Note that set 1A from case study one is used for NG composition Table 3.3 Sets of MR composition used in case study Sample MR Composition (mol fraction) Component 2A 2B 2C 2D 2E 2F 2G 2H 2I Methane 0.337 0.317 0.297 0.277 0.257 0.237 0.217 0.197 0.177 Ethane 0.306 0.326 0.346 0.366 0.386 0.406 0.426 0.446 0.466 Propane 0.225 0.225 0.225 0.225 0.225 0.225 0.225 0.225 0.225 Nitrogen 0.132 0.132 0.132 0.132 0.132 0.132 0.132 0.132 0.132 methane-ethane Sample MR Composition (mol fraction) Component 2J 2K 2L 2M 2N 2O 2P Methane 0.177 0.177 0.177 0.177 0.177 0.177 0.177 Ethane 0.446 0.426 0.446 0.486 0.506 0.526 0.546 Propane 0.245 0.265 0.225 0.225 0.225 0.225 0.225 Nitrogen 0.132 0.132 0.152 0.112 0.092 0.072 0.052 ethane-propane ethane- nitrogen Therefore, Case Study aims to show how this optimization model can help to provide insights in determining a near optimal MR composition However, it should be noted that this problem formulation has yet to possess the capability to automatically 53    Chapter Optimal Load Distribution in AP-XTM Refrigeration System search for the optimum MR composition It is also worth mentioning that the current problem formulation does not enforce minimum approach temperatures throughout the composite curves16 Therefore, in theory, it is possible to have minimum temperature violations along the composite curves that are not detected However, the possibility of this occurring is minimal due to the relatively large approach temperature of K set for this work In addition, temperature profile feasibilities are also verified using Aspen HYSYS 7.1 Now, in this case study, the process conditions shall be optimized with different MR compositions Through this iterative process, a more optimal MR composition may be determined, thus allowing the current formulation to better reflect the power requirements of the actual AP-XTM system, a shortfall experienced in Case Study Without any details on the compositional range of MR used in the AP-XTM system, the iterative approach will attempt to vary the compositions of any two components while maintaining others at their current levels Due to the overwhelmingly large number of possible combinations, an exhaustive search is impractical Optimizations of different MR compositions shall be carried out until the aim of this case study is deemed to be adequately satisfied, i.e to determine a more optimal MR composition such that the problem formulation better reflects the actual power requirements in the field as well as a more optimal MR composition for greater process efficiency Table 4.3 details the MR compositions used in this case study Firstly, the methane-ethane pair is varied, followed by the ethane-propane pair and the ethanenitrogen pair 54    Chapter Optimal Load Distribution in AP-XTM Refrigeration System Total Feed Refrigeration Load 250 Total Compression Load (MW) 350 340 240 330 320 230 310 220 300 290 210 280 200 270 190 260 250 180 Total Feed Refrigeration Load (MW) Total Compression Load 2A        2B        2C        2D        2E        2F         2G        2H        2I MR composition sets  Figure 3.7 Total compression and refrigeration load vs MR compositions Figure 3.7 presents a summary of the optimization results for the methane-ethane pair A decreasing trend in compressor power requirement (from Set 2A to 2I) is observed as the MR becomes richer in ethane at the expense of methane For instance, Set 2I (17.70% methane and 44.60% ethane) draws only a total of 307.530 MW while 331.650 MW is required for Set 2A (33.70% methane and 30.60% ethane) This is a decrease of nearly 7.3% in net power consumption In fact, the bulk of this reduction comes from the MR and nitrogen cycle Figure 3.8 show that the PR cycle’s compressors experienced an increase in power consumption from Sample 2A to 2I, a trend contrary to the overall In the optimized state, this is due to the increase in propane mass flow rate necessary to pre-cool only the flow of mixed refrigerant as the feed cooling load remained constant Second, Figure 3.9 illustrates that the MR compressor power demand 55    Chapter Optimal Load Distribution in AP-XTM Refrigeration System C3 Cycle Feed Cooling Load 67 52 65 50 63 48 61 46 59 44 C3 Cycle Feed Cooling Load (MW) C3 Cycle Compression Load (MW) C3 Cycle Compression Load 42 57 2A          2B          2C          2D         2E          2F           2G         2H          2I MR Compostion Sets   Figure 3.8 C3 cycle compression and refrigeration load vs MR compositions MR Cycle Feed Liquefaction Load 100 MR Cycle Compression Load (MW) 85 75 90 65 80 55 45 70 35 60 25 MR Cycle Feed Liquefaction Load (MW) MR Cycle Compression Load 2A          2B          2C          2D         2E          2F           2G         2H    MR Composition Sets   Figure 3.9 MR cycle compression and refrigeration load vs MR compositions 56    Chapter Optimal Load Distribution in AP-XTM Refrigeration System decreased 23.23% from Sample 2A to 2I (79.919 MW to 61.347 MW) while at the same time providing 7.43% more feed cooling capacity (71.321 MW to 76.622 MW) The change in MR composition allowed the optimizer to better match the composite curves in the MCHE This reduces thermodynamic irreversibility and enhances cooling efficiency of the MR cycle Finally, as all other system parameters are held constant, the decrease in nitrogen power drawn in Figure 3.10 is an indirect consequence of the changes in MR composition The 5.19% decrease (190.005 MW to 180.137 MW) in power is in respond to the 6.12% decrease (86.513 MW to 81.212 MW) in feed cooling load placed upon the N2 Cycle Compression Load (MW) N2 Cycle Compression Load 205 N2 Cycle Feed Refrigeration Load 120 195 110 185 100 175 90 165 80 70 155 2A          2B          2C          2D         2E          2F           2G         2H    N2 Cycle Feed Refrigeration Load (MW) cycle as the optimizer shifts the load away to the more efficient MR cycle MR Composition Sets            Figure 3.10 N2 cycle compression and refrigeration load vs MR compositions  For the second set of optimization trials on the ethane-propane pairing, the results are as shown below in Figures 3.11 and 3.12 Only three extra data points were obtained for this pairing due to insignificant improvements (0.81%) in power requirement despite 4% change in MR composition The propane cycle showed no significant changes over the three data points The MR cycle has similar compression power requirements (57    Chapter Optimal Load Distribution in AP-XTM Refrigeration System 0.19%) but serving slightly larger cooling loads (+1.80%) due to improved efficiency from Set 2I to 2K The nitrogen cycle experienced a corresponding decrease in both compression power (-1.43%) and cooling load (-1.70%) Compression Load (MW) Total Compression Load MR Cycle Compression Load C3 Cycle Compression Load N2 Cycle Compression Load 300 250 200 150 100 50 2I 2J MR Composition Sets 2K   Figure 3.11 Compression loads vs MR compositions (Ethane-Propane pair) Total Compression Load C3 Cycle Compression Load MR Cycle Compression Load N2 Cycle Compression Load 325 300 275 Compression Loads (MW) 250 225 200 175 150 125 100 75 50 25 2L                   2I                     2M                   2N                   2O                   2P   MR Composition Sets    Figure 3.12 Cooling loads vs MR compositions (Ethane-Propane pair) 58    Chapter Optimal Load Distribution in AP-XTM Refrigeration System Moving on to the last data set of Set 2L to 2P inclusive of 2I, the optimized power requirements are as shown in Figures 3.13 and 3.14 Compression Loads (MW) Total Compression Load MR Cycle Compression Load C3 Cycle Compression Load N2 Cycle Compression Load 325 300 275 250 225 200 175 150 125 100 75 50 25 2L                   2I                     2M                   2N                   2O    MR Composition Sets   Figure 3.13 Compression loads vs MR compositions (Ethane-N2 pair) Total Feed Cooling Load MR Cycle Feed Cooling Load C3 Cycle Feed Cooling Load N2 Cycle Feed Cooling Load 210 Feed Cooling Loads (MW) 180 150 120 90 60 30 2L                    2I                     2M                  2N                   2O    MR Composition Sets   Figure 3.14 Cooling loads vs MR compositions (Ethane-N2 pair) 59    Chapter Optimal Load Distribution in AP-XTM Refrigeration System From the results, it can be observed that by increasing the ethane composition (44.60% to 54.60%) with a corresponding decrease in nitrogen content (15.20% to 5.20%), the net power requirement can be further reduced from 314.360 MW (Set 2L) to 287.810 MW (Set 2P) This is a reduction of nearly 8.45% The MR composition in Set 2P (17.70% methane, 54.60% ethane, 22.50% propane and 5.20% nitrogen) is taken to be the new reference MR composition in this work This is because it yielded a power requirement that is sufficiently close to the 280+ MW documented in presentations on the new AP-XTM system commissioned in Qatar recently In addition, its cooling load distribution of 21.60%, 41.61% and 36.79% among the propane, mixed refrigerant and nitrogen cycle is consistent with the values obtained by Hasan et al.13 Although this is not the optimal MR composition, it provides an insight into what the optimum MR composition may be In addition, it also helps to raise the level of confidence in the ability of this model to mimic that of the actual system This creates a strong platform onto which future improvement and extension works can be carried out 3.5.4 Case Study 3-Effect of Cooling Water Temperature According to technical publications by Air Products, a world leader in LNG liquefaction technologies, LNG facilities require flexibility to operate under various conditions47 One of the most common fluctuations is the change in cooling water temperatures, due to seasonal change as well as day/night ambient temperature cycle Fluctuations in cooling water temperature are expected to have a sizeable impact on LNG facility performance as it is the major heat sink for the refrigeration processes Cooling water used is usually sourced from the sea or other major water bodies For instance, Brunei LNG takes up approximately 2000 tons of raw water per hour from 60    Chapter Optimal Load Distribution in AP-XTM Refrigeration System Badas River and up to 135,000 m3/hr of water being circulated in an open cooling water system48 As a result, the cooling water is subjected to variations in temperature due to ambient and seasonal conditions Total Compressor Load (MW) 320 315 310 305 300 295 290 285 280 298 299 300 301 302 303 304 305 Cooling Water Temperature (K)   Figure 3.15 Effect of cooling water temperature on total compressor load In light of the above, this case study is focused on optimizing the generalized APXTM model for different cooling water temperatures and to investigate the general effect on process power requirements This case study will focus on a tropical environment where the cooling water temperature is assumed to vary from 298 K to 305 K49 Set 1A in Case Study is used as NG composition in this study For each of these temperatures, the system is optimized for minimal net power consumption and the results are presented in Figure 3.15 The results indicate that when optimized, the net power requirement increases from 287.810 MW at 298 K to 307.900 MW at 305 K This increase is inevitable because given that all other variables are held constant, an increase in cooling water temperature reduces the maximum amount of heat that can be removed by the cooling water from 61    Chapter Optimal Load Distribution in AP-XTM Refrigeration System each cycle To compensate, higher refrigerant pressures and thus, higher energy requirements are necessary Therefore, the optimization aims to reduce this power increase to the minimum C3 Cycle Feed Cooling Load 80 56 54 75 52 70 50 65 48 46 60 44 55 42 50 Feed Cooling Load (MW) C3 Cycle Compression Load (MW) C3 Cycle Compressor Load 40 298 299 300 301 302 303 Cooling Water Temperature (K) 304 305   Figure 3.16 Effect of cooling water temperature on C3 cycle compressor load In addition, the results showed that for every degree increase in cooling water temperature, the net power consumption also increase by 0.96% to 0.99% The converse is true as well This correlates very well with the claims by Thomas and Chretien that for every degree decrease in cooling water temperature, an improvement of up to 1.0% in energy usage can be achieved50 Although their claim was not made specifically for the AP-XTM system, it does give a benchmark against which the realism of this model can be judged upon Based on this result, there shall be greater assurance on the realism of the current model Figure 3.16 above illustrates the effect of cooling water temperature on the propane cycle It is observed that the propane cycle experienced an increase in power consumption of 8.39% (59.692 MW to 64.703 MW) from 298 K to 305 K There are two 62    Chapter Optimal Load Distribution in AP-XTM Refrigeration System possible causes First, it is the increase in compressor discharge pressure necessary for the condensation of the propane stream at increased cooling water temperature Second, there is also an observed increase in the optimal propane mass flow through the cycle As for the feed cooling load trend, there is a minor decrease of 3.63% (43.682 MW to 42.095 MW) as more cooling load is shifted away from the feed towards pre-cooling the MR stream which now exits the coolers at higher temperatures MR Cycle Feed Cooling Load 53.0 52.5 52.0 51.5 51.0 50.5 50.0 49.5 49.0 48.5 48.0 86 86 85 85 84 84 83 Feed Cooling Load (MW) MR Cycle Compression Load (MW) MR Cycle Compression Load 83 298 299 300 301 302 303 Cooling Water Temperature (K) 304 305   Figure 3.17 Effect of cooling water temperature on MR cycle compressor load The MR compressor power requirement also follows a slight increasing trend of 2.91% (51.212 MW to 52.700 MW) with respect to increasing cooling water temperatures, as shown in Figure 3.17 With less heat being removed by the coolers and the higher resultant exit temperatures, the optimal MR pressure before expansion tends to edge higher (25.994 bar to 27.282 bar) to compensate and ensure approach temperature feasibility in the MCHE Therefore, greater compression power is required With increasing compressor power, the amount of feed cooling provided by the MR cycle 63    Chapter Optimal Load Distribution in AP-XTM Refrigeration System therefore undertake a corresponding decrease from 84.137 MW to 82.876 MW, a reduction of 1.50% N2 Cycle Feed Cooling Load 95 175 90 165 85 155 80 145 75 135 70 298 299 300 301 302 303 304 Feed Cooling Load (MW) N2 Cycle Compression Load (MW) N2 Cycle Compression Load 185 305 Cooling Water Temperature (K)   Figure 3.18 Effect of cooling water temperature on N2 cycle compressor load Finally, from the overall optimization results illustrated in Figure 3.18, it seems that the nitrogen cycle is better suited to accommodate fluctuations in cooling water temperature Both propane and MR cycle experience a slight decrease in feed cooling load which is taken up by the nitrogen cycle As a result, it experienced a 6.97% increase in compressor power requirement from 167.404 MW to 179.079 MW with a corresponding 3.83% increase in cooling load from 74.403 MW to 77.251 MW From this case study, it can be observed that cooling water temperature does have a considerable effect on the efficiency of the AP-XTM system All three cycles experienced a drop in efficiency with increasing cooling water temperatures As a consequence, maintaining a low and constant cooling water temperature through proper engineering and maintenance is essential in maximizing plant efficiency If fluctuations 64    Chapter Optimal Load Distribution in AP-XTM Refrigeration System are inevitable, process optimization then plays a critical role in minimizing the shortfall in efficiency This case study has also shown that this model is capable of producing optimized process parameters at which the LNG plant should be operated for minimum compressor power consumption under different cooling water temperature scenarios In addition, the model’s level of realism was also given a boost, given the consistency with claims of energy savings associated with changes in cooling water temperature 3.6 Summary Modern LNG plants require flexibility in handling various process fluctuations such as feed temperature, pressure, and composition as well as cooling water temperature Handling such process fluctuations essentially involve running the system away from the optimal design point resulting in lower efficiencies and higher power consumption This model under various operating scenarios demonstrated its utility to handle aforementioned diverse situations through the generation of sets of optimal operating conditions for different scenarios This indicates the potentiality of this model to form the groundwork for future studies and enhancements However, due to the lack of access to proprietary system information, only basic schematic and general arrangement of the system from Air Products and filed patent are used to form the model basis with reasonable estimates of process conditions Although comparisons with actual AP-XTM power requirement could not be made due to lack of proprietary operational data, the optimized values are already highly competitive compared to those reported in the literature despite possible future improvements Nevertheless, based on the case studies and optimization results in the above sections, the generalized model has demonstrated 65    Chapter Optimal Load Distribution in AP-XTM Refrigeration System that it is capable of generating sets of optimal operating conditions to a level of realism and accuracy acceptable for this preliminary study Finally, it is fair to mention that the model is generalized to different process conditions and able to offer the insight of APXTM refrigeration process 66    ... (MW) 32 0 31 5 31 0 30 5 30 0 295 290 285 280 298 299 30 0 30 1 30 2 30 3 30 4 30 5 Cooling Water Temperature (K)   Figure 3. 15 Effect of cooling water temperature on total compressor load In light of the... Ethane 0 .30 6 0 .32 6 0 .34 6 0 .36 6 0 .38 6 0.406 0.426 0.446 0.466 Propane 0.225 0.225 0.225 0.225 0.225 0.225 0.225 0.225 0.225 Nitrogen 0. 132 0. 132 0. 132 0. 132 0. 132 0. 132 0. 132 0. 132 0. 132 methane-ethane... after compressor (K) 33 6.992 33 5.560 0.425 Tmr_8 MR temperature after intercooler (K) 30 3.000 30 3.000 0.000 Tmr_9 MR temperature after compressor (K) 33 5.984 34 0.810 -1. 436 43? ?   Chapter Optimal