Multi-Objective Optimization of Ventilation and Air Conditioning System at Interim Storage for Spent Nuclear Fuel

  

Multi-Objective Optimization of Ventilation and Air

Conditioning System at Interim Storage for Spent

Nuclear Fuel

  R. Ratiko 1 1DVUXGGLQ : :XODQGDUL $ 5RVLGL ( 0DU]XNL

  Department of Mechanical Engineering Universitas Indonesia

  Depok, Indonesia

  ratiko@batan.go.id QDVUXGGLQ#HQJ XL DF LG ZLQGD ZXODQGDUL #HQJ XL DF LG

DLQXU URVLGL#HQJ XL DF LG HGL PDU]XNL#HQJ XL DF LG

  Keywords— Multi-objective optimization; ventilation and air conditioning system; Interim Storage for Spent Nuclear Fuel.

  3

  PVC

  Present value capital cost ($)

  PVC Chiller Present value capital cost of chiller ($) PVC AHU dc

  Present value capital cost of Air Handling Unit (AHU) and ducting system ($)

  PVC Blower Present value capital cost of blower ($) PVE Present value of electricity cost ($) PVT

  Present value of the total cost ($)

  V in Inlet air volume flow from AHU (m

  3

  /h)

  V out

  Outlet air volume flow to stack (m

  /h)

  P cond

  ܥ blower in

   Power consumption of inlet air blower (kW)

  ܥ blower out

  Power consumption of exhaust air blower (kW)

  ܥ comp

  Compressor power consumption (kW)

  ܥ cond

  Condenser power consumption (kW)

  ܥ evap Evaporator power consumption (kW)

  ܥ tot

  Total power consumption of the system (kW) I.

  I NTRODUCTION Spent nuclear fuel is a bundle of nuclear fuel that has been irradiated and has reached its economic life. The nuclear fuel in the operated reactor needs to be removed and replaced periodically with new nuclear fuel. The spent fuel continues to produce radiation and heat due to its radioactive decay. The heat decreases exponentially for many years after being removed from the reactor.

  Pressure of condenser (Pa)

  Pressure of evaporator (Pa)

  N OMENCLATURE

  Contaminant concentration from room temperature (Bq/m

  C iv

  Contaminant concentration from inlet air velocity (Bq/m

  3

  )

  C o

  Total energy cost at the ¿rst year of operation

  ($/year)

  C ov

  Contaminant concentration from outlet air velocity (Bq/m

  3

  )

  C t

  3

  P evap

  )

  ECC Equivalent cooling cost ($/kWh) F Annuity factor g Gravitational acceleration (m/s

  2

  )

  g el

  In Àation rate for electricity cost (%)

  H year

  Annual operational hours (hour)

  i Interest rate (%) k

  Number of operation years (year)

  Abstract— A multi-objective optimization of ventilation and air conditioning system at Interim Storage for Spent Nuclear Fuel (ISSF) at BATAN, Serpong, Indonesia has been considered. The analysis of this research had obtained the results of three scenarios, economic single-objective, safety single-objective, and multi-objective optimizations. The original Pareto frontier between cost and safety function is determined by implementing multi-objective optimization using genetic algorithm. The cost function of the ventilation and air conditioning system was developed based on the present value of the total cost. The safety function of this system was based on contaminant concentration levels which were obtained by using air change rates, negative pressure, and temperature parameters. The economic and safety results have been obtained for three scenarios of optimized systems. The result shows that the multi-objective method by implementing multi-objective optimization using genetic algorithm satisfies the economic and safety criteria.

  Mass flow rate (kg/s)

  ۦ The Interim Storage for Spent Nuclear Fuel (ISSF) building the final results to determine the optimum design for the facility at Batan, in Serpong, Indonesia is designed to store the system. spent nuclear fuel from nuclear research reactors in Indonesia for many more years.

UILDING ESCRIPTION

  II. B D The radioactive gaseous contaminants that could be

  The Interim Storage for Spent Fuel (ISSF) building facility released from the spent fuel inside the interim storage building as shown in Fig. 1 below was built in Batan, Serpong, in Serpong are Caesium-137 (CS-137), Iodine-131 (I-131), and Indonesia in 1998. The ISSF building facility is utilized to store

  Xenon-133 (Xe-133). In addition to these Radon-222 (Ra-222), the spent fuel from nuclear research reactors in Serpong, a natural radioactive gas from uranium-radium radioactive

  Bandung and Yogyakarta. The ISSF building consists of a decay series, can present itself in the walls of buildings and storage pool area and office area. The spent fuel is stored in the soil. These gaseous contaminant concentrations can be reduced storage pool, which is filled with water. The dimensions of the with ventilation and air conditioning system. pool are 14 x 5 meters and 7.5 meter depth (the base pond is 11.2 x 5 meters ) and 9 meter depth (the deep section pond is

  A lot of research is now focusing on optimizing ventilation 2.8 x 5 meter ) from the ground floor to the basement as shown and air conditioning (VAC) system by combining many in Fig. 2 below. variables. Optimization methods include single-objective and multi-objective types. . The multi-objective method had been

  Based on design documents, the ISSF facility can studied by Sayyaadi [1] in a cooling tower assisted by a vapor accommodate spent fuel discharged from 25 years of Serpong compression refrigeration system and by Sanaye [2] in a reactor operations. The maximum storage capacity is 1448 ventilation and air conditioning system with a focus on the spent fuel elements including the spent element control rod [8]. compressor, the condenser, the evaporator, and the axial fans. Thermal and economic parameters were the objective functions in this optimization. The same methods were used by Rezayan [3] for thermoeconomic optimization and as an exergy analysis of CO

  2 /NH 3 cascade refrigeration system for VAC. The

  cooling capacity, ambient temperature (ambient) and cold room temperature were the constraints in this optimization. The total annual cost for this system including input exergy cost to the system and initial capital cost of the system was the objective function. Input exergy to the system was the power consumption of compressors and fans and the capital cost.

  Jain [4] also applied multi-objective optimization of a vapor compression-absorption cascaded refrigeration system by combining thermodynamics, economic parameters and non- dominated environments using Sort Genetic Algorithm-II (NSGA-II). The same objective function had been studied by

  Fig. 1. Interim Storage for Spent Fuel building in Batan

  Sadeghi [5]. The exergy and economical on waste heat from the exhaust gases of the engine HCCI (homogeneous charge compression ignition) were used to drive the ejector refrigeration system.

  A developed ecological function for absorption refrigerators with four-temperature levels had been optimized by Ahmadi [6]. Multi-objective evolutionary approaches (MOEAs) using the NSGA-II method and the frontier of Pareto optimum were applied in this study.

  An optimization of the combination cycle for power and cooling cycle using binary organic working fluid had been studied by Abed [7]. Turbine work, cooling capacity, and thermal efficiency were considered as three important conflicting objectives used to find the best combination.

  There are still many researches on ventilation and air conditioning system optimization, however an optimization study of the ventilation and air conditioning system at a nuclear facility is still rare. This paper presents ventilation and air

  Fig. 2. Design of Interim Storage for spent nuclear fuel in Batan

  conditioning system optimization at the Interim Storage for Spent Fuel (ISSF) building facility in Batan, Serpong, Indonesia with the variation of objective function for cost and

  The 3-storey building is about 10.5 high and has a safety parameters. The frontier Pareto will be used for selecting basement, ground floor and first floor. The building is

EFRIGERATION AND

• PVC

  C = C

  Total Power Consumption

  ٷ

  tot ). The formula is stated as below:

  el

  .H

  year .

  ٷ

  tot

  (6)

  Total power consumption of the system can be obtained using the formulas as written below: ٷ

  el

  tot =

  ٷ

  comp +

  ٷ

  evap +

  ٷ

  cond +

  ٷ

  blower in +

  ٷ

  ), the annual operation hours, and the total power consumption of the system (

  k

  ] (5) The total electricity cost at the first year of operation comprises the price of electricity for each kWh (C

  BJECTIVE

  classified into three zones. Each zone has a different negative pressure. The negative pressure is highest in the zone with high potential radioactive contamination.

  III. R

  VAC S

  YSTEM

  S

  PECIFICATION

  A chiller with 213.6 kW (60.7 TR) cooling capacity is provided to maintain the pool water temperature and the room temperature inside the ISSF building. A reciprocating compressor drives the system with R-22 as refrigerant. The condenser is a shell and tube heat exchanger. The evaporator is also a shell and tube heat exchanger with the refrigerant placed on the shell side and water on the tube side. The cooled water from the evaporator is streamed into the air handling cooling coil.

  IV. M ETHODOLOGY This study examines a multi-objective optimization of ventilation and air conditioning system at Interim Storage for

  Spent Nuclear Fuel (ISSF) at BATAN, Serpong. The objective functions of this research are cost and safety of the system. The flow diagram of the research method can be seen in Fig. 3.

  Fig. 3. Flowchart diagram of research process

  F

  The compressor power consumption can be calculated using Matlab Software connected with Refprop software to calculate the thermodynamic properties of the refrigerant. After the values of the refrigerant enthalpy are determined, then the total power consumption of the compressor can be calculated using mass flow rate.

  UNCTION Cost Function

  Power consumption of the evaporator, condenser, blower in and blower out can be calculated from break horse power (BHP) of the blower motor power as in the formula stated below:

  The total cooling load has to be determined for calculating the mass flow rate. The building is modelled using Sketch-up, then the weather data for one year in Serpong is calculated using Energy plus. The result of its calculation showed the average cooling is 142.04 kW. In addition to this, the spent nuclear fuel still produces heat which has a cooling load of around 6 kW. Thus, the total cooling load used in the calculation of mass flow is 148 kW.

  PVT = PVC + PVE (3) The initial cost of the chiller package, air handling unit

  (AHU) and ducting system are included into the present value capital cost (PVC).

  PVC = PVC

  Chiller

  AHU dc

  (4) The operational cost is the electricity cost for the compressor, evaporator pump, blower for condenser and blower for AHU. Thus, the operation cost will be focused on the present value of electricity cost (PVE) for those components. The inflation rate in each year of the annual electricity cost will increase non-uniformly, this issue has to be predicted and calculated using the formula as below:

  PVE = C / (i-g el ) [1 - ((1+i) / (1+g el ))

  blower out (7)

V. O

  ) (2) The present value of the total cost called as PVT is stated below:

  ECC = F.PVT (1) F = i / (1 - (1 + i)

  BHP = WHP / Ș

  blower motor

  (8)

  Safety Function

  Besides the cost function the safety function is another objective in this study. As this building is to function as Interim Storage for Spent Nuclear Fuel in Batan, safety is of paramount importance in this area.

  The aim of this system is to reduce the contaminant concentration to the allowable limits. There are four influence parameters that effect safety. Air change rate (ACH), negative pressure, temperature, and humidity are the parameters which

  The cost function in this study is the equivalent cooling cost (ECC). The ECC consists of the capital investment and electricity costs of the whole system. Electricity cost has to be expressed as a function of the power consumption of the whole system. The total power of the system can be determined from the power required of each component such as the compressor and blower in air handling.

• k

have to be considered in safety criteria. Air change rate and negative pressure are more influential than the other two.

  Caesium-137, Iodine-131, Radon-222, and Xenon-133 are the contaminants that need to be considered in this research. Those four types of contaminants are analyzed using multi- zone airflow and contaminant transport software, Contam.

  From the analysis, it is known that the concentration of Iodine- 131 decreases slower than the other three contaminants due to its density being greater than the others. Thus, Iodine-131 is chosen to represent air change rate, negative pressure and temperature analysis. Based on an effective dose limit of 20 mSv in a year, standard breathing rate and a maximum average occupation of ISSF building of 500 h per year, the concentration limit of radioiodine–131 in air can be calculated Fig. 5. Concentration of Iodine-131 [Bq/m3] vs. negative pressure

  3 with a value of 1666.67 Bq/m [9,10].

  The objective function of safety can be stated as below: Fig. 5 above shows that the concentration level of Iodine- 131 decreases with an increase of negative pressure. Using the

  Safety = C + C + C (9)

  iv ov t

  curve fitting, the equation is stated below:

  Air Change Rates

• 11 4 -6

  3

  2 V out + 1.909x10

  V out -0.01494V out + C ov = -9.141x10 Air change rates greatly affect the contaminant concentration in

  4

  51.82V out - 6.684x10 (11) the building. The result of concentration analysis of Iodine-131

  Temperature

  to the effects of air change rates (air volumetric flow) using CONTAM is shown as Fig. 4.

  The third parameter that affects the concentration level of contaminants in the building is temperature. The result diagram Fig. 4 shows that the concentration level of Iodine-131 is shown in Fig. 6 below. decreases with an increase of volumetric flow. By using these data, Matlab software with curve fitting method can obtain the equation as written below:

• 2E-4*Vin

  C iv = 935.84.e (10)

  Fig. 6. Concentration of Iodine-131 [Bq/m3] vs. temperature

  The fitting equation of the above diagram is:

  6 C = -1.1402x10

  V + 693.99 (12)

  t cond Fig. 4. Concentration of Iodine-131 [Bq/m3] vs. air change rates

  Decision Variables Negative Pressure

  Seven variables are chosen as decision variables in this study. Those variables are as described in the list below: The second parameter that also affects the concentration level of contaminants in the building is the negative pressure.

  T : Condensing temperature (K)

  cond

  Using CONTAM software, the effect of the negative pressure T evap : Evaporating temperature (K) on contaminant concentration would be analyzed. The result is

  Sub : Subcooling temperature (K)

  ¨T plotted and shown as Fig. 5.

  Sup : Superheating temperature (K)

  ¨T

  3 V in : Inlet air volumetric flow from AHU (m /h)

  3 V out : Outlet air volumetric flow to stack (m /h)

  T room : Room temperature (K)

ESULTS AND

  ISCUSSION Fig. 9 is the result of the multi-objective optimization

  VI. R D scenario, where the data is plotted on the Pareto frontier The optimization procedure and flowchart of cost function diagram. The x-axis is the first objective function which is the calculation by implementing multi-objective optimization cost function. The y-axis is the second objective function or using Genetic Algorithm is presented in Fig. 7. While, the safety function. In deciding the multi-objective optimization, it optimization procedure and flowchart of safety function needs a process for deciding the optimized scenario. calculation by implementing multi-objective optimization using Genetic Algorithm is presented in Fig. 8.

  An approach method using multiple criteria in the decision making process for optimization of problems which involve more than one objective to be optimized simultaneously is called a multi-objective optimization. Each objective at the ideal point is optimized with maximum value regardless of the satisfaction of other objectives likewise each objective at nadir point reaches minimum value. The Pareto frontier gives a trade-off solution between obtained solutions of various chosen objectives. [10].

  Genetic algorithm method is used to obtain the eight decision variables (T cond , T evap , Sub , Sup , V in , V out , T room ) ¨T ¨T and five constraint equations (Eq. (7) – (12)). Based on the number of decision variables and the number of constraint

  Fig. 9. Pareto frontier result

  equations, then the results will be plotted in to the Pareto frontier.

  The next process is to select an ultimate optimum output data from the results. Below is the result of three scenarios for economic optimization, safety optimization, and multi- objective optimization. The following results are seven decision variables in various scenarios:

  Table 1. The values of decision variables in the various optimization scenarios.

  Decision Economic Safety Multi- Variables Optimized Optimized objective Optimized T (K) 303.15 306.07 303.41 cond

  T evap (K) 285.15 284.28 285.08

  9.86

  9.98

  ǻT sub (K) 10.00 sup (K) 3.42

  9.92

  5.64

  ǻT

  3 V (m /h) 2621.14 14055.86 6861.18 in

  3 V out (m /h) 5131.95 5498.91 5419.08 Fig. 7. The optimal design procedure for cost function

  T (K) 306.53 310.15 308.34 room

  Table 1 indicates the value of decision variables obtained for three scenarios including economic optimized, safety optimized, and multi-objective optimized.

  Table 2. The results of the energy analysis of various designs

  Economic Safety Multi- Optimized Optimized objective Optimized

  12.90

  ܥ comp (kW) 12.59 15.85 (kW) 3.37

  18.07

  8.82

  ܥ blower in

  7.07

  6.97

  ܥ blower out (kW) 6.60 Fig. 8. The optimal design procedure for safety function

  3 C (Bq/m ) 560.73

  59.20 233.72

  iv

  3

  207.88

  C ov (Bq/m ) 239.11 199.13

  Single-objective optimization thermodynamics, economics

  3 C t (Bq/m ) 344.48 340.36 342.42

  single-objective optimization, and multi-objective of the combined objective had been considered as three optimized systems.

  Table 2 indicates the value of energy analysis for various designs including economic optimized, safety optimized, and multi-objective optimized. Some useful data are listed in this table.

  . It was concluded that the multi-objective method by implementing multi-objective optimization using Genetic Algorithm satisfies the economic and safety criteria.

  1963. [10] Jiemwutthisak, P., et al., Air Monitoring to Control the Intake of Airborne Radioiodine-131 Contaminants by Nuclear Medicine Workers.

  National Nuclear Energy Agency. 1996. [9] INTERNATIONAL ATOMIC ENERGY AGENCY. A basic toxicity classification of radionuclides. Techical report series 15. Vienna: IAEA;

  [7] Abed, H., et al., Thermodynamic optimization of combined power and refrigeration cycle using binary organic working fluid. International Journal of Refrigeration, 2013. 36(8): p. 2160-2168. [8] Design of interim storage for spent fuel (ISSF), BATAN, Serpong.

  [5] Sadeghi, M., S.M.S. Mahmoudi, and R. Khoshbakhti Saray, Exergoeconomic analysis and multi-objective optimization of an ejector refrigeration cycle powered by an internal combustion (HCCI) engine. Energy Conversion and Management, 2015. 96: p. 403-417. [6] Ahmadi, M.H., et al., Thermodynamic and thermo-economic analysis and optimization of performance of irreversible four-temperature-level absorption refrigeration. Energy Conversion and Management, 2014. 88: p. 1051-1059.

  2011. 36(2): p. 888-895. [4] Jain, V., et al., Thermo-economic and environmental analyses based multi-objective optimization of vapor compression–absorption cascaded refrigeration system using NSGA-II technique. Energy Conversion and Management, 2016. 113: p. 230-242.

  B. Noble, and I.N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529-551, April 1955. (references) [2] Sanaye, S. and H.R. Malekmohammadi, Thermal and economical optimization of air conditioning units with vapor compression refrigeration system. Applied Thermal Engineering, 2004. 24(13): p. 1807-1825. [3] Rezayan, O. and A. Behbahaninia, Thermoeconomic optimization and exergy analysis of CO2/NH3 cascade refrigeration systems. Energy,

  International Journal of Refrigeration, 2011. 34(1): p. 243-256.G. Eason,

  [1] Sayyaadi, H. and M. Nejatolahi, Multi-objective optimization of a cooling tower assisted vapor compression refrigeration system.

  R EFERENCES

  3

  Fig. 10 indicates the result of economic analysis of three scenarios. It explains that the minimum present value of total cost for the system belongs to the economic optimized system.

  The result in Fig. 10 shows that the present value of total cost obtained for the multi-objective optimization is USD 302.205,94 for fifteen years of operation. And the concentration level of contaminants can be reduced into784.03 Bq/m

  The configuration of the optimization was developed with seven decision variables and five constraint equations. The multi-objective optimization was developed from single objective optimization of cost and safety simultaneously. The final result of multi-objective optimization was decided to attain the optimum cost and safety. The economic optimization is focused on limitation of power consumption on the system. Whereas, the safety function is considered by the limitation of concentration levels of contaminants. However, the multi- objective optimization is concerned with limited power consumption and limitation of contaminant concentration levels.

  The three optimization scenarios in this study are economic single-objective, safety single-objective, and multi-objective of cost and safety objective. The cost function of the VAC system was developed based on the present value of the total cost (PVT). The safety function of this system was built based on the level of contaminant concentration which is affected by air change rates, negative pressure, and temperature.

  ONCLUSION

  VII. C

  . But, if the concentration of contaminant needs to be reduced into the very safe limits, the cost will be higher. By applying multi-objective optimized, middle values can be attained whereby the cost is not so high and there is relatively low contaminant concentration.

  3

  Fig. 10 clearly shows that low cost is attained in the single-objective of economic optimized. However, as seen in Fig. 11, the contaminant concentration is higher than the safety optimized method, although still below the limit of 1666.67 Bq/m

  Fig. 10. Comparison of cost in different optimization scenarios Fig. 11. Comparison of safety in different optimization scenarios

  Fig. 11 indicates the result of economic analysis of three scenarios. It explains that the minimum concentration of contaminants belong to the safety optimized system.

  [11] Kumar, R., et al., Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making. Ain Shams Engineering Journal, 2016.