Muhammad Yaqoob

Gull Zaib

Mudasir Hussain

Safwan A Ali

Christophe Coquelet

Dr Javeed A Awan
email: javeedawan@yahoo.com

New measurements and modeling of electrical conductivity and pH of n-propanethiol and n-butanethiol in N-methyldiethanolamine + piperazine (MDEA-Piperazine) Aqueous solution at 306 K and 1atm

Keywords: electrical conductivity, kinetics of thiols, acid-base neutralization, thiol removal, organic sulfur species

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction:

The standard state properties, equilibrium constant, Gibbs free energy, chemical potential and enthalpy of reaction for the organic sulfur species like thiols (RSH) (also known as thiols) in gas phase, liquid phase and in aqueous phase are highly required properties for the optimized process design in oil and gas Industry. Unfortunately limited or no information about the kinetics and phase behavior is available in open literature [START_REF] Awan | Electrical Conductivity of n-Propyl mercaptan (n-PM) in N-Methyldiethanolamine (MDEA) in Aqueous Solutions at 303 K[END_REF] . The natural gas and petroleum industries are dealing with raw materials containing variable concentrations of acid gases (CO 2 , H 2 S) and traces of organic sulfur species (OSS) like thiols and dimethyl disulfide , and many others. Treatment processes have to remove not only H 2 S and CO 2 but also (thiols) thiols and prohibited compounds because worldwide regulations for environmental protection are forcing the petroleum industry to decrease the sulfur content in petroleum fluids [START_REF] Awan | Electrical Conductivity of n-Propyl mercaptan (n-PM) in N-Methyldiethanolamine (MDEA) in Aqueous Solutions at 303 K[END_REF] . Mercaptan belong to thiol-group of compound which contain an -SH group bound to a radical R. Mercaptan properties are governed to a large extent by the length of this radical [START_REF] Awan | Electrical Conductivity of n-Propyl mercaptan (n-PM) in N-Methyldiethanolamine (MDEA) in Aqueous Solutions at 303 K[END_REF][START_REF] Awan | Vapor-Liquid Equilibria of Organic Sulfur Species-Alkanolamine Aqueous Solutions in the Presence of Acid Gases[END_REF] .

In this work a new cell has been constructed for the simultaneous measurements of electrical conductivity (µS.cm -1 ) and pH of n-propanethiol and n-butanethiol in 45 wt % and 18 wt % MDEA and 4 wt % piperazine aqueous solution at 306 K and atmospheric pressure. It was observed that, as the mercaptan ions moves in MDEA and piperazine aqueous solution; electrical current flows through solutions and electrical conductivity increases with the increase in ionic concentration. The change in electrical conductivity of n-propanethiol and n-butanethiol in N-Methyldiethanolamine and piperazine aqueous solutions has been related to the change in the concentrations of ions formed inside the solution. The change in pH of n-Propanethiol and nbutanethiol in N-methyldiethanolamine and piperazine aqueous solutions has also been studies.

The reaction is acid-base neutralization so the change of pH has been related to the acid-base neutralization reaction. This information is further helpful to calculate the aforementioned properties and solubility due to chemical reaction 1 .

2.

Electrical Conductivity Model: 3

Chemical Reaction:

The chemical reaction of n-propanethiol and n-butanethiol in N-Methyldiethanolamine + piperazine aqueous solution can be written as a function of concentrations and ionic charges.

RSH + H 2 O →RH -+ H 3 O (1) RSH+R 3 N (or R 4 N) →RS -+RNH + (2) 2H 2 O → H 3 O + + OH - (3)
PZH + + H 2 O → PZ + H 3 O + (4) PZ +RSH + H 2 O → PZRS -+ H 3 O + (5) PZRS -+ RSH + H 2 O → -SRPZRS -+ H 3 O + (6) EC = F 2 (z i 2 )ϴ i C i (7)
ϴ i = 𝐷 𝐿 𝑅𝑇 𝑚𝑜𝑙 𝑐𝑚 2 𝐽 .𝑠 (8)
F = 96500 coulombs, and z i = ionic charge Where, D L (cm 2 /s) is the diffusivity of thiols in aqueous solutions. R (atm dm 3 /mol K), is the general gas constant. T (K) is the temperature in Kelvin. In case of RS-ions the value of z i is (-1). In general the corelation can be written as

EC = f(c i ,z i ) (9)
The main idea is to correlate the change in electrical conductivity as a function of time and thiol concentration as shown in equation ( 9).

3

Experimental Section:

Materials

The apparatus and materials used for the experiments with specifications and supplier are provided in the figure 1 and Table (1). Distilled water was used and no further purification was made.

[Table 1]

The experiments are performed with two different compositions of MDEA i.e 45 wt % and 18 wt % + 4 wt % piperazine aqueous solutions. The amount of n-propanethiol and n-butanethiol were added individually i-e (2.50 ml, 3.0 ml and 5.0 ml).The change in electricity conductivity (µS/cm) and pH with time at 306 K and atmospheric pressure have been studies. First of all the 70 grams of 45 wt % MDEA aqueous solution was added followed by the recalculated quantity of piperazine. The n-propanethiol and n-butanethiol were added with three different concentrations i.e 2.5 ml, 3.0 ml and 5.0 ml respectively. The same procedure was performed with different concentration of 18 wt % MDEA and 4 wt % piperazine aqueous solution by using 2.5 ml,3.0 ml and 5.0 ml of n-propanethiol and n-butanethiol respectively. Our literature review reveals that up to now no experimental data points about the electrical conductivity of thiols containing systems are available in the literature; therefore no comparison is possible.

[figure 1]

The schematic diagram of the apparatus design is presented in figure 1. The electrical conductivity meter and pH meter probes have been placed in the center of the cell, as shown in with time is due to the change in concentration of ionic species and change of pH is due to related to the acid-base neutralization reaction. This information is further helpful to calculate the thermo physical properties and solubility due to chemical reaction.

Calibration of Equipments:

The 4 electrodes ADWA 310 conductivity meter and pH meter were calibrated according to the standards mentioned in their manual books. Electrical conductivity meter probe ADWA 310 (4electrodes) was calibrated with 0.1N KCl solution. pH meter was also calibrated with

Results and Discussions:

The electrical conductivity and pH measurements of n-propanethiol and n-butanethiol in N-Methyldiethanolamine and piperazine aqueous solution are presented in this work (Tables 234567). As no other data has been found in open literature, comparison was not possible. Only the electrical conductivity of n-PM in MDEA aqueous solution has been discussed by Awan et al. [START_REF] Awan | Electrical Conductivity of n-Propyl mercaptan (n-PM) in N-Methyldiethanolamine (MDEA) in Aqueous Solutions at 303 K[END_REF] . Correlations between the electrical conductivity, pH and the change in ionic concentration with time have been suggested. First the composition of the MDEA and piperazine aqueous solution were kept constant i.e. 50 wt % and 5 wt % respectively and the composition of n-propanethiol and n-butanethiol vary from 2.5 ml, 3.0 ml to 5.0 ml. Similarly the same procedure was performed for 18 wt % MDEA and 5 wt % piperazine aqueous solutions by using 2.5 ml, 3.0 ml and 5.0 ml of n-propanethiol and n-butanethiol.

[Tables 2 to 7]

It is observed that initially electrical conductivity (EC) and the value of pH increases in N-Methyldiethanolamine + piperazine aqueous solution (no thiols has been added to cell). After the addition of thiols the electrical conductivity (EC) decreases and pH starts increasing for some time till equilibrium is achieved as shown in figures (see figures 2-9). The decrease in electrical conductivity might be due to the physical solubility phenomenon and increase in electrical conductivity might be due to the chemical reaction i.e. formation of ionic species and the decrease in pH is due to the acidbase neutralization reaction. However the results of electrical conductivity of n-propanethiol / nbutanethiol (5.0 ml) in MDEA-piperazine aqueous solution as a function of time did not show the initial decrease in electrical conductivity(EC) values (see figure 2,3,4,5).

[Figures 2 to 9]

This might be due to the bigger value of chemical solubility in comparison to the physical solubility; as the common observation does support with the hypothesis that there is a physical as well as chemical solubility of n-propanethiol and n-butanethiol in MDEA and piperazine aqueous solutions by using different composition small change in the data was observed. The change in electrical conductivity and pH of 18 wt % was low as compared to the 45 wt % MDEA and 4 wt % piperazine aqueous solution as shown in the following figures (8, 9).

[figure 8]

[figure 9]
According to these experiments, we can confirm the existence of a chemical reaction between n-propanethiol and n-butanethiol in MDEA and piperazine aqueous solution. However more experiments must be performed in a Lewis type kinetic cell, for better understanding of the phenomenon. Electrical conductivity of n-PM in pure water has been reported in literature [START_REF] Awan | Vapor-Liquid Equilibria of Organic Sulfur Species-Alkanolamine Aqueous Solutions in the Presence of Acid Gases[END_REF] showing that electrical conductivity in pure water is higher with respect to pure MDEA. In the literature there is no or limited information is available about the kinetics of thiols in water and/or MDEA-piperazine aqueous solution. Only the ionization constants for thiols were reported by Yabroff and White 3 . The equilibrium constant of nbutyl mercaptan (n-BM) at alkaline conditions in water at 301K, K eq =K a /K w =2489 was reported by Matsis et al. [START_REF] Matsis | Removal of n-butyl mercaptan using stripping with an inert gas: A nonequilibrium approach via mass balances[END_REF] . This value indicates a minor dissociation of n-propanethiol and n-butanethiol in aqueous solution. The permittivity of a solvent like alkanol amine may affect the reaction rate between the solvent itself and thiols, as discussed by Yakupov et al. [START_REF] Yakupov | The Solvent effect on the rate of reaction between Propanethiol and Chlorine Di-oxide[END_REF] .The solvent may therefore also affect the rate of dissociation of mercaptan. The solubility of thiols in MDEA has been discussed by Bedel land Miller 6 as the sum of physical and chemical solubility on the basis of the acid-base neutralization approach in an alkanol amine solution. Methyl mercaptan and ethyl mercaptan (EM) are weak acids, but higher thiols behave like hydrocarbons as the alkyl group increases [START_REF] Kohl | Gas Purification[END_REF][START_REF] Bishnoi | the Thermodynamics of Piperazine/Methyldiethanolamine/Water/Carbon Dioxide[END_REF][START_REF] Jou | Vapour-liquid equilbria in the system ethanethiol+methyldiethanolamine+water in the presence of acid gases[END_REF] . According to these experiments, we have confirmed the existence of a chemical reaction between n-propanethiol and nbutanethiol in MDEA-piperazine aqueous solutions. It was not possible to give an indication of the possible mechanism of the chemical reaction; just we can confirm that ions are produced with time before the equilibrium is achieved. Moreover during their action, negative and positive charged ions are created. The negative charge is supposed to be on sulfur atom (CH 3 -CH 2 -CH 2 -S -), CH 3 -CH 2 -CH 2 -CH 2 -S -). It is also concluded that organic part (CH 3 -CH 2 -CH 2 + ) carries hydrophobic and (S -) part carries hydrophilic properties. Similarly in the case of pH, at higher value of thiols (i-e 5.0 ml) the solutions show more acidic behavior. So, there is a chance of fast chemical reaction and there will be greater rate constant. Consequently n-propanethiol and n-butanethiol molecules group together to form a drop. This drop has a negative global charge. So all the drop push away together (they don't collapse together) Tables: The standard uncertainties u are u( T, k=2) = 0.03 K , u( pH, k=2) = 0.05, u.(, k=2)=0.1; where  stands for electrical conductivity The standard uncertainties u are u( T, k=2) = 0.03 K , u( pH, k=2) = 0.05, u.(, k=2)=0.1; where  stands for electrical conductivity The pH of n-butanethiol in 18 wt % N-Methyldiethanolamine + 4 wt% piperazine aqueous solution at 306 K and 1 atm. where symbols presents experimental data: ; 2.5 ml npropanethiol,  ; 3.0 ml n-propanethiol, ; 5.0 ml n-propanethiol added. the solid lines show the trend lines. Error band;  5 % experimental uncertainty.

figure 1 .

 1 figure 1. The change in electrical conductivity and pH has been measured with time. Only the electrical conductivity of n-propylmercaptan into MDEA aqueous solution is reported by Awan et al. 1 and the ionization constants for thiols were reported by Yabroff and White 3 . At first MDEA and piperazine aqueous solution was prepared gravimetrically under vacuum. A known quantity of MDEA and piperazine aqueous solution (140 cm 3 ) was loaded into the cell, which already contains magnetic stirrer for stirring. Once Electrical conductivity and pH values are stable then n-propanethiol or n-butanethiol were introduced into the cell through a special injector arrangement. Finally, the cell is placed inside a liquid water bath where temperature is maintained at 306 K. The probes of the electrical conductivity meter, pH meter and temperature were placed inside the cell, through a Teflon cap, inside it a special type of O-ring to avoid entrance of air. A vacuum connection to the cell was used for removing air from the cell. It was observed as the mercaptan ions moves in MDEA and piperazine aqueous solution. Electrical current flows through solutions and electrical conductivity increases with the increase in ionic concentration. The change in electrical conductivity of n-propanethiol and n-butanethiol in N-

  buffer solution having pH 4, 7 and 10.0 at 303.15 K. The calibration readings of electrical conductivity and pH are presented in tables S1 and S2 of supporting document. The calibration curves are shown in figures S1 and S2 of supporting document. The values of pH and electrical conductivity with their respective uncertainties can be obtained using the two following four equations (10 to 13): 𝑝𝐻 = 𝑎𝑝𝐻 𝐸𝑥𝑝 + 𝑏 (10) 𝐸𝐶 = 𝑐𝐸𝐶 𝐸𝑥𝑝 + 𝑑 (11) u pH = u b 2 + pH Exp u a 2 (12) u EC = u d 2 + EC Exp u c 2 (13) With a=1.285, b=-1.025, u(a)=0.126, u(b)=0.841, c=0.720, d=436, u(c)=0.038 and u(d)=61.

  Electrical conductivity and pH measurements of n-propanethiol and n-butanethiol in N-Methyldiethanolamine and piperazine aqueous solution at 306 K and atmospheric pressure have been presented and discussed. The existence of chemical reaction between n-propanethiol /n-butanethiol in MDEA-piperazine aqueous solutions has been highlighted. The changes in electrical conductivity (EC) with time have been related to the change in concentration of ionic species and change of pH has been related to the acid-base neutralization reaction. Supporting Information: In the supporting document tables S1 and S2 presents calibration of 4 electrodes ADWA 310 electrical conductivity temperature range from 303.15 K to 310.15 K and calibration of pH meter at 303.15 K respectively. The figures S1 and S2 presents Calibration curve; the theoretical electrical conductivity of standard solution 0.1 N KCl solution as a function of experimental electrical conductivity at 303.15 K and 1 atm and Calibration curve; experimental (pH) of 4, 7 and 10 buffer solution is plotted as a function of theoretical pH at 303.15 K and 1 atm respectively.

Figures:Figure 1 :

 1 Figures:

Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :

 23456 Figure 2: Electrical conductivity of n-propanethiol in 45 wt % MDEA + 4 wt % piperazine aqueous solution at 306K and 1 atm.

Figure 7 :

 7 Figure 7 :The pH of n-butanethiol in 45 wt % N-Methyldiethanolamine + 4 wt% Piperazine aqueous solution at 306 K and 1 atm. where symbols presents experimental data: ; 2.5 ml npropanethiol,  ; 3.0 ml n-propanethiol, ; 5.0 ml n-propanethiol added. the solid lines show the trend lines. Error band;  5 % experimental uncertainty.

Figure 8 :Figure 9 :

 89 Figure 8 :The pH of n-propanethiol in 18 wt % N-Methyldiethanolamine + 4 wt% piperazine aqueous solution at 306 K and 1 atm. where symbols presents experimental data: ; 2.5 ml npropanethiol,  ; 3.0 ml n-propanethiol, ; 5.0 ml n-propanethiol added. the solid lines show the trend lines. Error band;  5 % experimental uncertainty.

Table 1 : CAS numbers, purities, and suppliers of materials

 1

	Chemical Name	CAS No.	Purity %	Supplier
	N-Methyldiethanolamine	107-03-9	99 + GC	SIGMA-ALDRICH
	(MDEA)
	piperazine	110-808-3	≥ 98 ,0%	SIGMA-ALDRICH
	n-butanethiol	109-79-5	99%	SIGMA-ALDRICH
	n-propanethiol	107-03-9	99%	SIGMA-ALDRICH

Table 2 : Electrical conductivity and pH of n-propanethiol and n-butanethiol (2.5 ml by vol.) in 45 wt% N-Methyldiethanolamine+ 4 wt% piperazine aqueous solution at 306K and atmospheric pressure. No. of data points Time/min n-propanethiol n-butanethiol pH EC Experimental (µS/cm) EC Theoretical (µS/cm) pH EC Experimental (µS/cm) EC Theoretical (µS/cm)

 2

	1	0	10.63	1606.5	934.5	10.56	1555.5	1098.0
	2	10	10.73	1570.8	742.3	10.66	1504.5	872.1
	3	20	10.83	1581	589.6	10.72	1514.7	759.6
	4	30	10.7	1596.3	795.4	10.52	1514.7	1204.0
	5	40	10.46	1591.2	1382.1	10.5	1504.5	1260.7
	6	50	10.45	1586.1	1414.6	10.49	1484.1	1290.1
	7	60	10.44	1581.1	1447.6	10.49	1479.1	1290.1
	8	70	10.43	1581.1	1481.3	10.48	1479.1	1320.1
	9	80	10.38	1591.2	1662.1	10.46	1479.1	1382.4
		90	10.38	1591.2	1662.1	10.45	1484.1	1414.6
		100	10.37	1601.4	1700.1	10.45	1484.1	1414.6
		110	10.36	1616.7	1740.4	10.43	1489.2	1481.3
		120	10.36	1632.0	1740.4	10.43	1499.4	1481.3
		130	10.37	1647.3	1700.8	10.42	1509.6	1515.8
		140	10.37	1652.4	1700.8	10.39	1535.1	1624.2
		150	10.35	1652.4	1780.9	10.39	1555.5	1624.2
		160	10.34	1657.5	1822.4	10.37	1606.5	1700.8
		170	10.34	1662.6	1822.4	10.35	1683.1	1780.9
		180	10.34	1667.7	1822.4	10.34	1759.5	1822.4
		190	10.34	1672.8	1822.4	10.34	1769.7	1822.4

Table 3 : Electrical conductivity and pH of n-propanethiol and n-butanethiol (3.0 ml by vol.) in 45 wt% N-Methyldiethanolamine+ 4 wt% piperazine aqueous solution at 306K and atmospheric pressure.

 3

	No. of data points	Time (min)	pH	n-propanethiol EC Experimental (µS/cm) EC Theoretical (µS/cm)	pH	n-butanethiol EC Experimental (µS/cm) EC Theoretical (µS/cm)
	1	0	10.78	1650.1	1102.6	10.98	1060.8	927.5
	2	10	11.02	1600.1	1050.5	11.27	958.8	475.6
	3	20	11.05	1580.1	1475.3	11.3	907.8	443.9
	4	30	10.46	1530.9	1728.0	11.35	851.7	395.6
	5	40	10.45	1479.2	1768.3	11.35	836.4	395.6
	6	50	10.44	1479.3	1809.5	11.12	918	671.9
	7	60	10.43	1479.2	1851.6	10.84	831.3	1280.4
	8	70	10.4	1484.1	1984.1	10.75	1009.8	1575.3
	9	80	10.38	1500.9	2077.6	10.66	1229.1	1938.2

Table 4 : Electrical conductivity and pH of n-propanethiol and n-butanethiol (5.0 ml by vol.) in 45 wt% N-Methyldiethanolamine+ 4 wt% piperazine aqueous solution at 306K and atmospheric pressure.

 4

	No. of data points	Time (min)	pH	n-propanethiol EC Experimental (µS/cm) EC Theoritical (µS/cm)	pH	n-butanethiol EC Experimental (µS/cm) EC Theoritical (µS/cm)
	1	0	10.63	1800.2	1214.9	10.34	1953.1	1214.9
	2	10	10.88	1708.5	683.1	10.67	1973.7	568.2
	3	20	11	1545.3	518.1	10.73	1861.5	494.9
	4	30	10.8	1519.8	821.3	10.76	1846.2	461.8
	5	40	10.7	1514.7	1034.0	10.76	1836.2	461.8
	6	50	10.63	1504.5	1214.9	10.65	1779.9	594.9
	7	60	10.57	1524.9	1394.0	10.6	1718.7	667.6
	8	70	10.55	1580.1	1460.7	10.54	1718.7	766.5
	9	80	10.53	1620.2	1529.5	10.49	1739.1	860.0

Table 5 : Electrical conductivity and pH of n-propanethiol and n-butanethiol (2.5 ml by vol.) in 18 wt% N-Methyldiethanolamine+ 4 wt% piperazine aqueous solution at 306K and atmospheric pressure.

 5

	No. of	Time		n-propanethiol		n-butanethiol
	data points	(min)	pH	EC Experimental (µS/cm)	EC Theoretical (µS/cm)	pH	EC Experimental (µS/cm)	EC Theoretical (µS/cm)
	1	0	10.63	1606.5	1012.8	10.56	1555.5	1098.0
	2	10	10.73	1570.8	742.3	10.66	1504.5	872.2
	3	20	10.83	1581.1	589.6	10.72	1514.7	759.6
	4	22	10.83	1606.5	589.6	10.72	1509.6	759.6
	5	24	10.83	1596.3	589.6	10.72	1509.6	759.6
	6	26	10.83	1596.3	589.6	10.62	1509.6	956.3
	7	30	10.7	1596.3	795.4	10.52	1514.7	1204.0
	8	40	10.46	1591.2	1382.4	10.5	1504.5	1260.8
	9	50	10.45	1586.1	1414.6	10.49	1484.1	1290.1
		60	10.44	1581.2	1447.6	10.49	1479.1	1290.1
		70	10.43	1581.1	1481.3	10.48	1479.2	1320.2
		80	10.38	1591.2	1662.1	10.46	1479.3	1382.4
		90	10.38	1591.2	1662.1	10.45	1484.1	1414.6
		100	10.37	1601.4	1700.8	10.45	1484.1	1414.6
		110	10.36	1616.7	1740.4	10.43	1489.2	1481.3
		120	10.36	1632.2	1740.4	10.43	1499.4	1481.3
		130	10.37	1647.3	1700.8	10.42	1509.6	1515.8
		140	10.37	1652.4	1700.8	10.39	1535.1	1624.2
		150	10.35	1652.4	1780.9	10.39	1555.5	1624.2
		160	10.34	1657.5	1822.4	10.37	1606.5	1700.8
		170	10.34	1662.6	1822.4	10.35	1683.3	1780.9
		180	10.34	1667.7	1822.4	10.34	1759.5	1822.4
		190	10.34	1672.8	1822.4	10.34	1769.7	1822.4

Table 6 : Electrical conductivity and pH of n-propanethiol and n-butanethiol (3.0 ml by vol.) in 18 wt% N-Methyldiethanolamine + 4 wt % piperazine aqueous solution at 306 K and atmospheric pressure.

 6

	No. of data points	Time (min)	pH	n-propanethiol EC Experimental (µS/cm)	EC Theoretical (µS/cm)	pH	n-butanethiol EC Experimental (µS/cm)	EC Theoretical (µS/cm)
	1	0	10.23	2167.5	1950.08	10.45	1693.2	1414.0
	2	10	10.42	1856.4	1757.90	10.6	1540.2	1001.4
	3	20	10.52	1744.2	1602.00	10.61	1509.6	978.6
	4	30	10.52	1713.6	1602.00	10.6	1504.5	1001.4
	5	40	10.3	1734.1	1699.16	10.6	1509.6	1001.4
	6	50	10.15	1744.2	1711.44	10.55	1519.8	1123.6

Table 7 : Electrical conductivity and pH of n-propanethiol and n-butanethiol (5.0 ml by vol.) in 18 wt% N-Methyldiethanolamine+4 wt% piperazine aqueous solution at 306K and atmospheric pressure.

 7

	No. of data points	Time (min)	pH	n-propanethiol EC Experimental	EC Theoretical	pH	n-butanethiol EC Experimental	EC Theoretical
				(µS/cm)	(µS/cm)		(µS/cm)	(µS/cm)
	1	0	10.63	1545.3	1401.5	10.98	1892.1	1765.5
	2	10	10.88	1519.8	1200.2	11.27	1856.4	1655.2
	3	20

6.

References:


Nicolas Dupin
email: nicolas.dupin@velux.com

Bruno Peuportier
email: bruno.peuportier@mines-paristech.fr

Karsten Duer
email: karsten.duer@velux.com

Michael Cohen

Berenger Favre

Olfa Mejri

Eric Vorger

; Peuportier

; Duer

; Cohen

Berenger (BF ; Favre

; Mejri

; Vorger

Evaluation of ventilative cooling in a single family house -Characterization and modelling of natural ventilation

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Low energy buildings, especially nearly Zero Energy Buildings (nZEB), are subject to significant overheating risks during summer and mid-season [START_REF] Lomas | Summertime temperatures in 282 UK homes: thermal comfort and overheating risk[END_REF] [START_REF]Overheating in new homes. A review of the evidence[END_REF]. Thermal simulations as well as experimental studies has shown the large potential of ventilative cooling; nevertheless, these studies were mainly focused on potential energy savings in office or commercial buildings [START_REF] Artmann | Climatic potential for passive cooling of buildings by night-time ventilation in Europe[END_REF] [START_REF] Yao | Assessing the natural ventilation cooling potential of office buildings in different climate zones in China[END_REF] and only few papers were published on the cooling potential of natural ventilation in dwellings.

One major barrier against the use of natural ventilation in the design process of a house is the difficulty of evaluating air flows through windows opening. The use of appropriate hypothesis and relevant coefficients regarding windows is crucial to perform building thermal simulations and accurately evaluate indoor thermal comfort of occupants.

The present work aims at studying a characterization and modelling process allowing natural ventilation to be accounted for in the evaluation of energy performance and thermal comfort of buildings. The approach is tested using the monitoring system of the zero energy Active House Maison Air et Lumière, located near Paris.

The project went through the following steps:  A test bench has been used to characterize the air flow features of a roof window (such features of roof windows are not well described in literature)  These air flow features have then been used for numerical simulations of the air flows and air temperatures in the building  On site measurements of air flows and air temperatures have been performed in order to get realistic data about natural ventilation and its contribution to summer comfort  Comparisons between simulations and on site measurements have been performed in order to validate the models used to evaluate air flow rates through windows. These comparisons are also used for checking the relevancy of numerical simulations in terms of summer comfort

Laboratory measurements

A test bench has been built in a laboratory in order to evaluate aerodynamic features of a roof window. A ventilator is used to create a regulated pressure difference between inside and outside environments of the roof window.

Air flow rates and pressure differences have been varying in order to get a curve and to derive C and n characteristics from the below empirical equation (powerlaw) [START_REF] Axley | Modeling the Performance of a Naturally Ventilated Commercial Building with a Multizone Coupled Thermal/Airflow Simulation Tool[END_REF] [6]: The flow coefficient 𝐶 is depending on the free opening area (geometrical area), which is linked to the window size and opening angle. The flow coefficient can then be evaluated using the free opening area S, a discharge coefficient 𝐶 𝑑 and the air density 𝜌, using the following equation An example of a fully opened window is shown in Figure 2.

𝑄 = 𝐶. (𝑃 2 -𝑃 1 ) 𝑛 = 𝐶. (∆𝑃) 𝑛 (1)
𝐶 = 𝐶 𝑑 . 𝑆. ( 2
It shows a log-linearization of measurements for a full opening of the window (maximum length of the actuator), measured for a large range of ∆𝑃.

In this figure, 𝑛 = 0.45 and 𝐶 = 0.32.

Similar measurements are performed for different openings. All 𝐶 and 𝑛 coefficients are shown in Table 3.

With the assumption that the opening area S is around 0.07 m² (, we find a

Cd value 𝐶 𝑑 = 𝐶 𝑆 . ( 𝜌 2
) 0.5 of around 0.75, which is near the standard values [START_REF] Etheridge | Building Ventilation: Theory and Measurement[END_REF] [START_REF] Etheridge | Building Ventilation: Theory and Measurement[END_REF].

On site measurements

The house extends over 2 storeys, 135 m² and is designed as a net zero energy house. Concrete slab on the ground floor and first floor are providing its thermal mass. The window-to-living-area is 1/3. All windows are equipped with dynamic solar protection and the operation of all systems in the building is fully automated.

The house is equipped with a detailed monitoring system providing event-logged data for both indoor and outdoor environments: indoor temperature, humidity, light level, as well as outdoor wind speed and direction, sun radiation and temperature, are continiously monitored and recorded. Windows and awning blinds positions are continiously recorded in order to bring a complete description of the house: these input data will then be used with the dynamic simulation software Pléiades+Comfie for evaluating the complete approach of ventilative cooling.

The evaluation of on site air flow rates passing through this house were performed with tracer gas measurements using CO2. In addition, wind velocity and pressure sensors were placed within the free opening areas of windows in order to validate the precision of the simplified models used for simulations (i.e. the "powerlaw" described in equation ( 1)).

Where: 𝑉 where 𝐶 is a constant.

 𝐶 𝑖𝑛𝑡 (𝑡)
After log-linearization, the relative tracer gas concentration can be plotted against time and a linear regression gives the corresponding air change rate (being the slope of the line).

Air flow simulations

Thermal numerical simulations have been carried out in order to understand the behaviour of the building. The study of natural ventilation is then improved by using both thermal and airflow models. Air flow simulations requires data about wind pressure on external sides of windows. These pressures can be evaluated according to wind velocity and direction using pressure coefficients 𝐶 𝑝 . The software "Cp generator"1 was used in this study to evaluate 𝐶 𝑝 values of the windows.

Air flow simulations have been performed using CONTAM 7, allowing infiltrations, windows and room-to-room airflows to be evaluated, taking into account mechanical systems, wind pressures on windows and buoyancy effects.

CONTAM simulations were performed using laboratory measured 𝐶 𝑑 and 𝑛 for roof windows -0.75 and 0.5, respectivelyand default CONTAM values for vertical windows -𝐶 𝑑 = 0.6 and 𝑛 = 0.65 [9].

Each tracer gas measurement period has been simulated using CONTAM. Table 6 shows the comparisons between on-site measured values and simulations output.

South

Thermal simulations

A dynamic thermal simulation tool was used to evaluate temperature profiles in the house [START_REF] Peuportier | Simulation tool with its expert interface for the thermal design of multizone buildings[END_REF] based on measured outdoor conditions. It turns out that air change rates have a large influence on temperature profiles.

Different measurement and simulation conditions were used to compare typical cases and present the direct influence of natural ventilation on indoor thermal environment: 7 shows simulation and measurement results during the second period (internal doors open). There is a good correlation between simulated (blue) and measured temperature profiles (green), whereas the impact of night ventilation can easily be seen from the external temperature curve (red line). As a reference, simulations with closed windows have been carried out in order to evaluate the potential of ventilative cooling in a single family house. Figure 8 shows a comparison between "free-running" temperature (windows closed, in green), natural ventilation (windows open, in blue) and exterior temperature (in red).

Feedback on occupation period

In order to confirm the house indoor climate, especially its summer thermal comfort, a family of 4 moved into the house from September 2012 to August 2013. This family lived inside the house for one year without any restrictions on their behaviour and use of the automated system: wall-mounted push buttons were installed in order to allow a by-pass of windows openings or blinds moves. Figure 9 shows a comparison between a free running system (summer 2012, no occupants) and a "real case" scenario (summer 2013, 4 people inside the house) during a heat wave of the summer period.

It can be seen from Figure 9 that indoor temperature was 11°C lower than exterior temperature during unoccupied period, while the difference during the occupied period was 8°C. This deviation could be explained by the "real use" of the house, which leads to frequent opening of doors and sliding-doors, letting the outside warm air enter into the house. The presence of four people inside the house led to higher internal gains which added a significant heat source inside the house. In addition, the family got used to limit window openings during night time so that they were not disturbed by the wind effects while sleeping.

The indoor thermal comfort, as described before, has been described by the family after summer 2013:"When we were arriving inside the house, the heatwave still was present and the exterior air was above 30°C, and the temperature was 24 to 25°C inside the house without any other cooling system than natural cooling" 2 .

Conclusion

Measurements performed in Maison Air et Lumière have shown that, without ventilation, indoor temperature can reach 35°C. Ventilative cooling allowed a fast decrease of indoor temperature, keeping rooms below 27°C most of the time: this has shown that ventilative cooling was very efficient during this period.

During typical indoor and outdoor conditionswind speeds between 2 to 3 m/s and temperature a difference lower than 3 °C -Air Change Rates between 10 to 22 h -1 have been achieved. Air In addition, dynamic thermal simulations have reproduced the indoor air temperature quite precisely, with an average difference between calculated and measured values around 1K. The accuracy of these simulations has allowed to evaluate the potential of ventilative cooling, by comparing 2 similar cases with and without natural ventilation during night. Indoor temperatures have then been reduced by 5°C on average thanks to ventilative cooling.

This work shows the feasability of modeling ventilative cooling and its impact on indoor thermal comfort for a project: it has been illustrated in this work, and could be extended to the design process of a project, assuming that relevant weather data are available. Natural ventilation provides a relevant way to achieve carbon neutral passive cooling in nZEB houses; thermal simulations have shown good correlation on temperature and the relevant air flow models used in this study could now be implemented into national regulatory softwares assuming the use of an efficient thermal model.

Figure 1 :

 1 Figure 1: Test bench: aerodynamic chamber and regulated ventilator

.

  Measurements were performed for ∆𝑃 varying from 0.05 to 10 Pa. The loglinearization of equation (1) gives 𝐿𝑜𝑔(𝑄) = 𝐿𝑜𝑔(𝐶) + 𝑛. 𝐿𝑜𝑔(∆𝑃)

Figure 2 :Figure 4 :

 24 Figure 2: Logarithm of the air flow rate with an opening of 100%

Figure 5

 5 Figure 5 shows as an example the relative CO2 concentrations (CO2 concentration above outdoor level), measured in the living room for the two scenarios: internal doors being opened (blue curve) or closed (green curve). The continuous lines and the dashed lines respectively correspond to the measurements during the morning and the afternoon. The calculation of the air change rate is deduced from the general tracer gas mass balance equation [8]: 𝑉. 𝑑𝐶 𝑖𝑛𝑡 (𝑡) 𝑑𝑡 = 𝑆(𝑡) -𝑄(𝑡). (𝐶 𝑖𝑛𝑡 (𝑡) -𝐶 𝑜𝑢𝑡 (𝑡))(2)

  is the CO2 concentration of indoor air (ppm)  𝐶 𝑜𝑢𝑡 (𝑡) is the CO2 concentration of outdoor air (ppm)  𝑆(𝑡) is the injection rate of tracer gas into the room (m 3 /h)  𝑉 is the indoor air volume (m 3 )  𝑄(𝑡) is the air flow rate (m 3 /h) Integration of equation (2) leads to an exponential function where the concentration is a function of time (𝐶 𝑖𝑛𝑡 (𝑡) -𝐶 𝑜𝑢𝑡 (𝑡)) = 𝐶. 𝑒 -𝑄.𝑡

Figure 5 :

 5 Figure 5: Relative CO2 concentration in the living room during CO2 decay test



  From 23 rd to 26 th of July 2012 without any natural ventilation  From 27 th of July to 3 rd of August with natural ventilation (all windows open) and internal doors open  From 4 th to 7 th August with natural ventilation (all windows open) and internal doors closed.

Figure

  Figure7shows simulation and measurement results during the second period (internal doors open). There is a good correlation between simulated (blue) and measured temperature profiles (green), whereas the impact of night ventilation can easily be seen from the external temperature curve (red line).

Figure 7 :

 7 Figure 7: Comparision of simulated and measured indoor temperature in the ground floor bedroom. All windows open and all internal doors closed

Figure 8 :

 8 Figure 8: Comparison on simulation cases. In green: all windows closed. In blue: all windows open. In red: outdoor air temperature

2

  Quote from inhabitant of Maison Air et Lumière. Final conference Decembre 2013 « L'habitat de demain : audelà des expériences. Retour sur l'expérience Maison Air et Lumière VELUX MODEL HOME 2020 »

Figure 9 :

 9 Figure 9: Comparison of ventilative cooling benefits during a heat wave in unoccupied (left) and occupied periods (right). Blue: indoor temperature. Red: outdoor temperature

Table 3 :

 3 Air flow characteristics of the roof window

Table 6 :

 6 Measured and simulated Air Change per Hour (ACH)

			bedroom temp [°C]	North bedroom temp [°C]	Bathroom temp [°C]	Wind speed [m/s]	Tracer Gas [ACH]	CONTAM simulation [ACH]
	Morning	Closed door Open door	23.7 23.7	21.3 21.3	22.5 22.5	3.6 2.8	13.4 22.5	13.9 20.6
	Afternoon	Closed door Open door	27.1 27.1	26.5 26.5	26.2 26.2	2.3 2.3	13.2 19.8	16.6 19.5
	Morning	Closed door Open door	24.2 24.2	22.5 22.5	23.3 23.3	3.6 3.6	13.4 14.6	14 17.4
	Afternoon	Closed door Open door	26.5 27	25.2 26.1	25 25.6	2.9 2.8	10.6 13.1	13.2 17

cpgen.bouw.tno.nl/cp/


G Sibiude

S Lasvaux

A Lebert

S Nibel

B Peuportier
email: bruno.peuportier@mines-paristech.fr

R Bonnet
email: r.bonnet@bouygues-construction.com

J L Senegas
email: jl.senegas@izuba.fr

T Raquin
email: traquin@maisons-qualite.com

Survey on LCA results analysis, interpretation and reporting in the construction sector

Keywords: Life cycle assessment, energy efficient building, building environmental assessment, renewable energy, allocation, dynamic

Life Cycle Assessment (LCA) is widely recognized as the most relevant tool to evaluate building environmental impacts. Previous works have proposed different building LCA tools to spread LCA in the building sector practices. However, as initial questions are different depending on the user profiles, answers should be different from a building LCA tool developer's perspective. Indeed, one single LCA tool embedding a single methodology and a single way to analyse and express the results will not fulfil the needs of all stakeholders. In this study, a national survey was conducted in order to better understand the needs of these building stakeholders to feed back LCA tool developers. Focus was put on the interpretation and reporting steps of the LCA method as defined in ISO 14040. Results showed different practices, expertises, and scope of studies. Stakeholders not familiar with LCA prefer easy-togo interpretation while experts still prefers keeping detailed results. Outcomes of this survey can now be used by building LCA tool developers to better ajust the analysis and interpretation tools in their software to match the user needs.

Introduction

The building sector is a major contributor to the environmental impacts including climate change, energy consumption, waste generation and air pollution [START_REF] Kellenberger | Relevance of simplifications in LCA of building components[END_REF]. In that context, the Life Cycle Assessment (LCA) method can serve as an instrument to assess the environmental performance of buildings and building products [START_REF] Peuportier | Life cycle assessment methodologies in the construction sector: the contribution of the European LORE-LCA project[END_REF] providing a holistic overview of their life cycle. LCA is standardized through the ISO 14040-14044 standards and, for the construction sector, the European standards EN 15804 and EN 15978 provide general calculation rules for LCA of building products, technical equipment and buildings [START_REF]Sustainability of construction works -Sustainability assessment of buildings -calculation method[END_REF]. In the construction industry, LCA is increasingly used in research projects and in daily practice. Building certification schemes (e.g. BREEAM, HQE or DGNB) have started relying on LCA to assess the environmental performance of buildings. To ease the operational practice, guidance documents have proposed study types to adapt the levels of details of the LCA calculation rules to the project stages and objectives (e.g. screening, simplified and complete LCA) e.g., in the EeBGuide Infohub for building LCA [START_REF]EeBGuide Info Hub[END_REF].

Reviews of LCA tools for buildings are available in Ortiz et al. [START_REF] Ortiz O | Sustainability in the construction industry: A review of recent developments based on LCA[END_REF], Bribian et al. [START_REF] Bribian Iz | Life cycle assessment in buildings: Stateof-the-art and simplified LCA methodology as a complement for building certification[END_REF] and more recently in Lasvaux et al. [START_REF] Lasvaux | Requirements for building LCA tool developers[END_REF]. These state-of-the-art of building LCA tools in Europe revealed different levels of maturity between the tools and different choices for the databases, the methodological rules and the environmental indicators. Indeed, no existing tools match all the user needs because they have been developed with different starting points e.g. as decision making tools for architects or thermal anlyisis engineers or in opposite for certification purposes. Despite the high number of available tools, using LCA in practice is still not a common practice [START_REF] Saunders | Analyzing the Practice of Life Cycle Assessment: Focus on the Building Sector[END_REF]. While a complete harmonization of the different philosophies, there is a need to better link users' expectations and the interpretation and reporting aspects within building LCA tools. Very few research works have been conducted in the field of LCA results interpretation for the specific case of the building sector. Saunders et al. [START_REF] Saunders | Analyzing the Practice of Life Cycle Assessment: Focus on the Building Sector[END_REF] analyzed the practice of LCA in the US building sector. The authors concluded that even though stakeholders are aware that LCA provides information about environmental impacts, the lack of building related metrics was highlighted as a prominent barrier.

In this study, a survey is conducted to better understand the link between the different user profiles of LCA in the building sector and the types of interpretation and reporting methods. The next section presents the assumptions used to build the survey and section 3 presents the results.

Methodology

2.1. Online survey A web-based survey using Google Drive TM was set up and diffused using differen tools to increase the visibility (e.g. mailing lists, social networks, expert forums, etc). No particular restriction was adopted since the study objective was to reach various user profiles and stakeholders interested in LCA. The survey went live from the 07/15/2013 to the 02/10/2014 and collected a total of 121 responses. Except questions on LCA result display, the survey contains parts to characterize the sample at both levels: general profile information (professional duty and organization size) and LCA knowledge.

Sampling methods

The results of the survey were grouped according to two criteria:  Type of stakeholders (see Table 1)

 Level of LCA expertise (see Table 2)
The first grouping criterion is based on work typology. Table 1 presents the stakeholders' groups and the number of answers collected per group. The respondents come from a wide range of professions in the building. A second grouping criterion was proposed according to the level of LCA expertise of the survey's participants. Considering:

Thereafter, calculating both indicators:

The factor "3" applied on the number of performed LCA study allows increasing the weighting of LCA practitioners. Then, these two conditions are considered:

Wherein:

Finally, the sum of both test results gives the expertise level:

The global sample can be splited up in three groups as shown in Table 2 and helpful to study:  The possible modification of interpretations from a group to another,  The posible modification of interpretations from a group to the whole simple.

Topics included in the survey

Next to the general information asked to better characterize the panel, a complete set of LCA indicators was included in the survey. The stakeholders are asked to give priority to each environmental aspect (e.g., climate change, raw material extraction and depletion, water consumption).

Similarly, they are asked to give their opinion in the normalisation and aggregation steps of a LCA. These two steps enable to summarize the LCA results into single score and into equivalent units to be able to identify the most relevant actiosn to take. For instance, such tools can help focusing on energy consumption rather than eutrophication in the building sector due to the high share of this sector in the global energy consumption.

Results

3.1. LCA understanding, knowledge, practice and goals Figure 1 presents the relationship between the theoretical knowledge and the operational practice of the different respondents. Average values are represented for each group. Despite an important proportion of low-practical level respondents (43 % responding 1 or 2 whathever the group, not shown in Figure 1), a majority of people states having a pretty good theoretical level as shown by the averages values in Figure 1 (from 2,5 to 4,5).We notice a linear relation of the results in X (theoretical knowledge) and in Y (operational practice). The least knowledge is reported for the group "economist" (only 1 respondent) while the highest knowledge and operational practices correspond to the building manufacturers. About 25% of the answers came from design office engineers both energy and environmental experts. It is important to mention that in every group a substantial variability of the knowledge and practice exists though the average value should be taken as indicative. The next section presents the results of the survey. To the question "would you estimate yourself relevant to understand and use building LCA results?" 48 % of the respondents answer "yes". That highlights two notable points:

 Half of the respondents would give relevant trails thanks to their knowledge and understandings of the difficulty.  Half of the respondents would give relevant trails by clearly underline the lack and hotspot without any background.

The last question concerns the goal and scope of the LCA. Most LCA practitioners mentioned eco-designing, decision support and certification/labeling as primary goals.

LCA interpretation: environmental indicators' ranking

In the survey, a list of environmental indicators has been proposed among the most commonly indicators found in LCA tools for buildings in France. People were asked to qualify their priorities on a four-grade scale (1: not useful, 2: not very important, 3: moderately important, 4: highly important). Based on the percentage got for the four ranking grades of each indicator among the sample of construction stakeholders', two types of weighting factors are calculated. The first one is only based on the 1 st and 2 nd rankings while the second one is based on all rankings. These factors are calculated with the formula below: With: W i : weighting factors for indicator i j ∈ 1;4 R j : grade of ranking j P j : percentage of ranking j Even if the weighting factors are only indicative, they describe stakeholder's priorities. As an illustration, Figure 3 presents the scatterplots of the two types of weighting factors for the LCA indicators used in French building LCA tools according to stakeholders' choices. Two groups of indicators can be separated based on the results of Figure 3. The first one gathers four indicators: the primary energy, the global warming, the water consumption and the waste generation. The weighting factors vary from 0,75 to 0,92. The other group has all the other indicators with weighting factors around 0,5 (from 0,4 to 0,63). Interestingly, the top four indicators match with the public policies priorities in France following the "Grenelle de l'Environnement". The results are relatively stable whatever the level of expertise of the respondents.

Other results for the LCA interpretation: normalisation and weighting

Normalization is the prior step to weighting and aggregation. In this survey, people were asked to give their opinion on two types of normalization factor systems. Two choices were proposed: the first one is a customized factor system (i.e., each practitioner could set his own factors) and the second one is a default factor e.g., based on LCA tool developers assumptions or other national or European statistics (energy consumption, CO2 emissions in the building sector etc.). We found that t presents the answers of both questions about the needs of customizable normalization factor system and default normalization factors in the tools. We found that the two solutions are approved by the majority of respondents. Similarly, Table 4 presents the results of the stakeholders' needs in single score's indicators. Table 4 shows the interest in aggregated score to express building LCA results (percentages of "yes" between 51 to 56%). It seems that people are slightly more interested in getting default aggregation factors than having the possibility to create their own factor set.

LCA reporting

The last point of the survey consists in exposing users to LCA reporting document examples. The aim is to test several display formats and get an appraisal about each. Four different documents were proposed. Figure 4 shows two examples of display solutions exposed in the survey. The top one is predominantly preferred as it gives on the same succinct insert an easily understandable classified system for non experts and the raw values for higher expertise level.

A third document gives a well-appreciated solution with a radar chart for comparison to a reference building. This helps people to evaluate and put into perspective a project to the common practice. A solution to match all needs with a synthetic format could be to provide on the same LCA report the classified/raw values table (as in Figure 4 top) and a radar chart.

Discussions and conclusions

Since the questionnaire was sent exclusively in France, the findings of this study are particularly relevant for this national context though some general conclusions are valid whathever the context. According to the survey's results, stakeholders judge important to focus on primary energy, greenhouse gases emissions, water consumption and waste generation. The inclusion of normalization factors is preferred by a majority of respondents of this survey. Indeed, such a system enables to identify the most relevant indicator to work on in terms of impacts' shares for the building sector (e.g., expressed in equivalent inhabitants per year). Another way to normalize is to compare the LCA results to the actual construction practice mean impacts. Generally speaking, they prefer the use of aggregation system but with the possibility to modify the weighting. Similarly, they agree on sets of weighting e.g., based on default values (or based on public policies).

Last but not least, the findings of this survey reinforce the need to have public consultation and practitioners' meeting during the development (or the update) of a LCA tool of building to be sure that the user needs will be taken into account.

Figure 1 :

 1 Figure 1: Stakeholders' group respondents' knowledge and practice in LCA. The size of the bubbles indicates the number of respondents in each group. The centre of the bubble represents the average score for the theoretical knowledge and operational practice.

Figure 2

 2 Figure 2 presents the shares of the type and number of LCA studies either conducted or red by the diferent stakeholders. First, a large part concerns building LCA and product LCA, only

Figure 2 :

 2 Figure 2: Shares of the type of LCA study conducted or red by the stakeholders.

Figure 3 :

 3 Figure 3: Representation of the weighting factors for each LCA indicators based on stakeholder's priorities

Figure 4 :

 4 Figure 4: Two examples of impact indicators display submitted in the survey one with both classified and raw values (left) and a very synthetic one with only classified values (right)

Table 1 :

 1 Number of answers based on professions

	Stakeholders groups based on their professions	Sample
	Architects	3
	Consulting engineers for contracting authority	13
	Design office	36
	Trade unions	8

Table 2 :

 2 Number of answers based on LCA expertise

	Stakeholders groups based on LCA experience	Sample
	Low expertise level	64
	Middle expertise level	21
	High expertise level	41

Table 3 :

 3 Stakeholders' needs for normalisation factors

	Stakeholders answers

Table 4 :

 4 Stakeholders' needs for an aggregation system and single score display

	Stakeholders answers


Roux

; Peuportier

Considering temporal variation in the life cycle assessment of buildings, application to electricity consumption and production Speakers

Keywords: Dynamic Life-cycle Assessment, Buildings, Electricity production, consequential life cycle assessment

Existing tool for environmental impact assessment of buildings and districts according to the LCA procedure are based upon a static method, considering yearly average processes and impacts. A dynamic method has been developed in order to model the temporal variation of electricity production and allocate environmental impacts to different uses. It allows to evaluate low-energy and plus energy buildings on a more precise basis.

Based upon data from the French electricity grid manager, the model evaluates the production mix in terms of temperatures in French climates and several periodic functions corresponding to variation frequencies identified by a Fourier analysis. In a second step, specific production mixes are derived for different uses: heating, cooling, domestic hot water, domestic appliances and office appliances.

The model has been tested on a single family passive house, allowing solar hot water and photovoltaic systems to be assessed using a dynamic method.

Introduction

In France in 2011 almost 60% of the total electricity production was consumed in buildings [START_REF] Rte | Forecast assessment of the supply and demand balance : the safety of power supply secured up to 2015[END_REF]. 33% of dwellings and 25% of office buildings are heated by electricity [START_REF]Building sector key features[END_REF] and more than 45% of dwellings also use electricity to produce hot water [START_REF]Building sector key features[END_REF]. This consumption is highly time-dependent, from summer to winter, from day to night and from week to week-ends. Production of electricity follows this variability. For instance, electric heating induces a seasonal peak demand in winter with a high dependency to temperature, which is increasing every year [START_REF] Rte | Forecast assessment of the supply and demand balance : the safety of power supply secured up to 2015[END_REF]. Local production of electricity, such as photovoltaic panels on buildings roofs, also has a time-dependent production. However, standard LCA practice is based on an average annual electricity mix, neglecting this variation. The purpose of the work presented here is to improve the precision of LCA, by integrating a dynamic electricity mix, disaggregated on different uses. The model developed to calculate the electricity mixes is first exposed. Then we explain how it was integrated in Building LCA tool. Afterwards we show implications of this modification in terms of environmental impacts assessment of buildings. Finally we discuss limitations of the model and future possible developments.

Building LCA

From the 80's, several tools have been developed around the world to evaluate environmental impacts of buildings according to the LCA method. Thanks to research projects like REGENER [START_REF] Peuportier | European project REGENER, life cycle analysis of buildings[END_REF], and a common methodology basis has been sketched out for building LCA tools. Eight European tools have then been compared as part of the thematic network PRESCO [START_REF] Peuportier | Inter-comparison and benchmarking of LCA-based environmental assessment and design tools[END_REF].

One of these tools is EQUER [START_REF] Polster | Evaluation of the environmental quality of buildings towards a more environmentally conscious design[END_REF], [START_REF] Peuportier | Eco-design of buildings using thermal simulation and life cycle assessment[END_REF]. It was developed to model the life-cycle of buildings, from construction to dismantling, through utilization and renovation phase. It also includes an extension to urban district evaluation. Due to the importance of energy issues in buildings environmental balance, it is linked to a dynamic termal simulation tool, COMFIE [START_REF] Peuportier | Simulation Tool with Its Expert Interface for the Thermal Design of Multizone Buildings[END_REF].

After an analysis of sustainability objectives performed in the E-Co-Housing Project [START_REF] Jonge | Towards sustainable neighbourhoods, the eco-housing guidlines[END_REF], it considers eleven indicators, mostly from the CML2000 and Ecoindicator 99 methods to get a comprehensive set of environmental impacts (see table 1). The electricity production changes according to the seasonal, weekly and daily variation of the consumption. Fourier analysis allows the different frequencies composing a signal to be identified. This method has therefore been applied. The main frequencies correspond to 12 h, 24 h, 168 h (i.e. one week), 4392 h (half a year, 2008 being bissextile), and 8784 h (one year). The electricity production is then expressed as a sum of periodic functions corresponding to the identified frequencies (daily, weekly, seasonal and yearly variations). Due to the importance of the electric heating in France, the production also depends on climatic conditions, and mainly external temperatures. As production data is available on a national scale, a reference temperature has been evaluated as an average between several locations (MeteoFrance data), weighted according to the corresponding population. The production P is then expressed as a function of this average temperature T av and of time t:

ܲሺ,ݐ ܶ ௩ ሻ ൌ ∑ ሺܺ ሺܶ ௩ ሻ * cosሺݓ * ݐ ܻ ሻሻ ܼሺܶ ௩ ሻ (1)
Where w i are the identified frequencies.

Parameters ܺ ሺܶ ௩ ሻ, ܻ and ܼሺܶ ௩ ሻ are identified by a least square method (quasi-Newton algorithm) in order to minimize the discrepancy between calculated and measured production values. Yi are assumed constant.

The average discrepancy between calculated and measured production is 4%. The discrepancy is higher (11%) when comparing the year 2009 values (see the discussion session).

A part of the consumed electricity (around 6% of production) is imported. RTE data include hourly imported quantities from different countries: Germany, Switzerland, Spain, Italy, UK and Belgium. This has been integrated to the model, so that a complete electricity production mix is derived.

Construction of use specific electricity mixes

The total impacts of electricity production can be evaluated at each hour using the model presented above. But electricity is used for several purposes: lighting, domestic appliances, cooling, heating, tertiary uses, domestic hot water production etc. These different uses can be regarded as co-products, and different allocation methods can be applied. If electricity is consumed during a winter night both for heating and hot water, considering the same production mix would lead to account for high CO 2 emissions also for electric hot water production, though these high emissions are due to the seasonal peak induced mainly by heating. It seems therefore more relevant to differentiate the production mix according to the different uses.

A yearly constant base load mix is first identified according to the yearly minimum values of nuclear and hydro power production.This mix is used to evaluate the environmental impacts related to hot water production, the solar hot water contribution being marginal at the moment in France. Subtracting the yearly minimal production from the weekly minimal production allows a mix to be identified for heating and cooling. This mix is evaluated for each week. A correction based upon a linear approximation is applied at each hour to account for climatic variation within a week [START_REF] Peuportier | Evaluation of electricity related impacts using a dynamic LCA model[END_REF]. The weekly minimum production is then subtracted to the hourly production values in order to study other domestic and professional uses (i.e. other than heating, cooling and hot water). Week-end production is assumed, by approximation, to correspond to domestic uses. This allows the domestic and professional contributions to be identified. This process is applied to each production type (nuclear, hydro-power, thermal plants) so that hourly electricity production mixes are evaluated for each use type. The same grid efficiency (9% losses) is considered for all production types.

Integration in Building LCA

The output of the model described above consists of 5 different hourly mixes for the 5 uses considered adapted to a typical climatic year.

In a first approach, usages have been associated to their major variation scheme:

-Seasonal consumption : Heating and cooling -Base load : Domestic hot water, stand-by consumption, cold appliances -Daily consumption : Other Domestic appliances -Weekly consumption : Professional appliances -Average hourly mix (not disaggregated by use), eg. for a local electricity production In order to be integrated in the building LCA tool EQUER, the model has been recalibrated on a typical year (mean temperature over twenty years), so that it is compatible with the simulation tool COMFIE, in terms of temperature used to evaluate the heating and cooling loads, provided as hourly values. The energy consumed for appliances generally contributes to heat the building, and this heat gain is defined in an hourly scenario in COMFIE.

Dynamic LCA seems particularly adapted to assess Plus energy buildings because local production is generally variable (e.G. in terms of solar radiation or wind).

Local electricity production is firstly consumed in the building. The allocation to each use is proportional to its share in total electricity consumption at each hour. Residual production is then evaluated as exported to the grid and corresponds to an avoided grid production evaluated using the general hourly mix at the same hour. The corresponding avoided impacts are subtracted from the total impacts of the building. Impacts linked with the production of equipments (Production and transport of photovoltaic modules for instance) are entirely allocated to the building.

Average hourly mix, appliances

Nuclear Hydraulic Thermal

Case study

This model has been tested on a case study. A family house has been chosen, situated on the Incas platform, near Chambery [START_REF] Cea | SIMINTHEC L1-1, Plateforme INCAS, Description des bâtiments test et des éléments et systèmes contenus[END_REF]. It was built to respect the passive house standard. The heated floor area is 90m². Using the thermal dynamic simulation tool COMFIE, the calculated heating load (19°C temperature set point) is 18 kWh/m 2 /year.

Two renewable energy systems, added to a "base" house, have been evaluated using the annual average and hourly electricity mix models:

-4m² of solar thermal panels, facing south, 71.7° slope -39.3 m² of polycrystalline photovoltaic Si panels, facing south and with 26.5° slope Electric air heating is used for space heating. Hot water is produced by an electric hot water tank. The annual consumption for other uses (lighting, ventilation, domestic appliances) is set to 2700kWh; corresponding to an average consumption per household in Europe [START_REF] Grinden | Analysis of monitoring campaign in Europe[END_REF]. Final energy consumption for each use satisfied by electricity is reported in the following table. Regarding life cycle assessment, fabrication of materials is evaluated considering a 5% surplus added in order to account for on-site processes, broken elements and purchased quantities. An average 100 km transport distance by truck is considered from the factories to the building site, 20 km from the building site to incineration facilities and 2 km to landfill.

Life spans considered are 10 years for building finishes, 30 years for windows and doors and 80 years for the other elements and the whole building. The photovoltaic system has been evaluated with a 25 years lifespan and the thermal solar panels with a 20 years lifespan. At end-of-life, all demolition waste is assumed to be landfilled. 4 inhabitants are considered, consuming each 100 l of cold water and 40 l of hot water (at 50°C) per day.

As the objective is to study impacts related to the electricity consumed in the building, domestic waste management and daily mobility of inhabitants are not included. We have used Ecoinvent 2010 database [START_REF] Frischknecht | The ecoinvent database system: a comprehensive web-based LCA database[END_REF] for the life-cycle inventory data.

Results

Life-cycle impacts have been calculated for the base case and the three alternatives. After normalization procedure, results for photochemical Oxidant Formation and damaged caused to ecosystem have been removed as all studied systems had a very small contribution in both of these impacts.

The following graphs show for each alternative the relative discrepancy between a calculation using a yearly average mix and a use-specific hourly mix: Using the hourly mix method tends to increase environmental impacts related to thermal electricity generation technologies (Climate and Resources for instance) of more than 7% (and up to 30%) and decreases environmental impacts related to nuclear production (Primary Energy demand and Radioactive waste). Only base case result show small differencies between the two methods. Results thus indicate that the amount of thermal technologies used to generate this electricity is higher than what is suggested using an annual mix. Differencies are the highest for the alternative integrating thermal solar panels, which is explained by the base load mix (nuclear and hydraulic power), associated with water heating.

Discussion

The results show how important it is to consider time-variation in energy consumption and production, especially when assessing low and plus-energy buildings.

The model has been calibrated on the year 2008 and tested on 2008 and 2009. At the time of development, it was the only years for which detailed data were available. The model is currently being updated since new detailed datais now available for the years 2012 and 2013. Economic parameters are not included. As an example, in 2009 in France, the economic crisis has had a large influence on electricity consumed by industry. The decrease of electricity consumption from industry has made nuclear capacity available for other uses. This is one explanation of the discrepancy between results of the model and measured electricity production in 2009. A larger period of study may reduce discrepancy due to economic variations.

The model has shown difficulties to treat extreme weather events and intermediate season when dependency to temperature is lower. Furthermore it only takes into account temperature but it would be useful to integrate a solar radiation parameter. The model is based on two main parameters: temperatures and frequencies of uses. However, these two parameters are not completely independent, which means that a part of variation linked with temperatures is allocated to frequencies. The reference temperature is calculated on the basis of three meteorological locations in France. This number could be increased, e.g. the grid manager (RTE) uses 32 locations in its model. There is work in progress to build up a new reference temperature based on 8 locations. Holidays are not taken into account, which introduces some singularity neglected by the model.

Future evolution of the mix (planned dismanling of obsolete plants for instance, planned construction of new capacities) has not been taken into account. This is of importance considering that average lifespan of buildings is around 80 years but it is also very uncertain. Therefore LCA results are more reliable for short term periods, e.g. dividing the total impacts by the duration of the period provides yearly impacts and this value is more reliable for the next years than for a far future.

Consequential LCA is defined as a modeling technique aiming at evaluating consequences of a decision (Earles et Halog 2011; Ekvall et Weidema 2004; [START_REF] Zamagni | Lights and shadows in consequential LCA[END_REF]. This method is of great interest when feedback loops of important magnitude occur between the studied system (here a building or urban settlement) and background processes (e.g. electricity production). The study presented here is a first step towards integration of consequential parameters in life cycle assessment of buildings. After increasing the robustness of the model and integrating new dataset available, further steps may include feedback loops such as modification of the general mix because of the influence of buildings (short-term, utilization of installed capacities), scenarios integration (mid or long-term, influence on investment on new capacities), and economic parameters (electricity market merit order, elasticity).

Conclusion and perspective

Choosing an hourly or annual production mix model to perform building life cycle assessment has a large influence on impact evaluation. Technologies such as photovoltaic modules, heat pumps, cogeneration, solar domestic hot water systems, or new control strategies influence the electricity consumption and production over time. Using an annual model is therefore not precise, and life-cycle simulation is more adapted.

A consequential LCA method could be further developed to better understand feedback loops mechanisms between the building sector and the overall electricity production system. Economic mechanisms, resources constraints or scenarios may be added. This would allow a better understanding of short and long-term environmental consequences of electricity consumption, and more globally improve the reliability of building eco-design tools.

Figure 1 :

 1 Figure 1: Results of the model presented as average hourly mixes

Figure 2 :

 2 Figure 2: One of the INCAs House, in Chambéry[START_REF] Cea | SIMINTHEC L1-1, Plateforme INCAS, Description des bâtiments test et des éléments et systèmes contenus[END_REF]

Figure 3 :

 3 Figure 3: Relative discrepancy between a yearly averaged mix and a use-specific hourly mix

Table 1 :

 1 Environmental indicators in EQUER

	Impact indicator

Table 2 :

 2 Annual final consumption and production of electricity

		Space heating (kWh)	Water heating (kWh)	Specific electricity (kWh)	PV production Electricity (kWh)
	Base	1790	2544	2700	0
	Base+Solar thermal (SolTh)	1790	551	2700	0
	Base+PV (PV)	1790	2544	2700	5021
	Base+
	SolarThermal+PV	1790	551	2700	5021
	(SolThPV)