4. Estimation of saline retention in electrodes

the total energy stored. For high concentrations (>0.1 to 0.6 M), the change in C is linear with a very little slope (≤1Farad per 0.1 M). This is so because the double electric layer is almost

Regarding RS, the series resistance (Figure 8), it can be seen that it increases a lot for low concentration values, M, and presents a linear trend with small or very small slope from 0.3 M on.

completely formed for concentrations around 0.1 M.

Figure 8. RS for different configurations.

44 Desalination and Water Treatment

Figure 9. RP for different configurations.

Tests have been carried out using the CDI cell shown in Figure 4 to estimate the amount of salt that can be captured by the electrodes of this type of cells when they are completely filled with salt water. The molar salt concentration (M) is one of the parameters used to characterize the cell.

The first tests (performed at zero water flow) consist of series of charge/discharge cycles of the CDI cell using a solution with the maximum concentration considered (0.6 M).

It can be pointed out that, as shown in Figure 10, the electrodes present very high retention values taking into account that the initial concentration is 0.6 M. There is a variation throughout the series, tending to stabilize as the number of cycles increase.

In the second set of tests (Figure 11), two-electrode cells were used considering different initial concentrations, c0, and changing the distance between electrodes (d) to identify the influence of this parameter on the salt retention. The results obtained show that salt retention increases as the distance between electrodes is reduced.

This effect can be justified if we take into account that the series resistance presented in the cell increases with the distance "d," especially at concentrations below 0.3 M, which presumably results in a decrease in the effective voltage in the electric double-layer and, as a result, a lower ionic retention.

Controlling the thickness of the nanoporous carbon layer deposited on the electrode surface, the quantity in grams of activated carbon per electrode can be determined. With the results

Figure 10. Results obtained by application of several charge-discharge cycles to cell configurations of more than two electrodes.

Figure 11. Estimated relative retention for cell configurations of two electrodes at different separations (d) and for different initial concentrations (c0).

obtained in the previous test, an estimation of milligrams of salt retained per gram of activated carbon was also calculated.

magnitudes and a trade-off between them will be necessary. Figure 13 also shows the time required by the electrodes to reach saturation, after which the salt retention capability drops.

Energy Recovery in Capacitive Deionization Technology http://dx.doi.org/10.5772/intechopen.75537 47

As already mentioned, one of the most interesting aspects of CDI is the possibility of reusing the energy stored in capacitive cells or modules once the deionization phase has finished. The regenerative use of energy in CDI technology consists of using the energy stored in the CDI cell once it is saturated (the deionization process is finished and the cleaning process begins) and transferring it to other modules that begin their deionization phase. This strategy can be applied to several cells that exchange the energy involved in the process, thus defining a cycle

In order to be able to transfer the energy stored in the CDI cell to another one, it is necessary to

Figure 14. Desalination diagram with an up-down converter for energy transfer from CDI cell-1 to CDI cell-2.

5. Up/down DC/DC converter for energy recovery

Figure 13. Estimated salt retention at different charging current (iC).

include a DC/DC power converter in the system (Figure 14).

to produce clean water.

The data plotted in Figure 12 are susceptible of a logarithmic adjustment in the whole range and a linear adjustment for low concentrations. Both present a good correlation (R2 > 0.94).

One important parameter to be analyzed during the diffusion of ions in the charging/ discharging process is the amplitude of the current used.

By reducing the amplitude of the current used during the charging process of the CDI cell, ions have a longer time to move into the carbon porosities, thus increasing the salt retention. This phenomenon seems to reach a limit at which the amount of salt retained remains the same even if the current is doubled (from iC = 1 to 2 A). From this, it can be concluded that the amplitude of the charge/discharge current and the amount of salt retained are two opposed

Figure 12. Estimated relative retention for cell configurations of two electrodes and different initial concentrations (c0).

Figure 13. Estimated salt retention at different charging current (iC).

obtained in the previous test, an estimation of milligrams of salt retained per gram of activated

Figure 11. Estimated relative retention for cell configurations of two electrodes at different separations (d) and for

The data plotted in Figure 12 are susceptible of a logarithmic adjustment in the whole range and a linear adjustment for low concentrations. Both present a good correlation (R2 > 0.94).

One important parameter to be analyzed during the diffusion of ions in the charging/

By reducing the amplitude of the current used during the charging process of the CDI cell, ions have a longer time to move into the carbon porosities, thus increasing the salt retention. This phenomenon seems to reach a limit at which the amount of salt retained remains the same even if the current is doubled (from iC = 1 to 2 A). From this, it can be concluded that the amplitude of the charge/discharge current and the amount of salt retained are two opposed

Figure 12. Estimated relative retention for cell configurations of two electrodes and different initial concentrations (c0).

carbon was also calculated.

different initial concentrations (c0).

46 Desalination and Water Treatment

discharging process is the amplitude of the current used.

magnitudes and a trade-off between them will be necessary. Figure 13 also shows the time required by the electrodes to reach saturation, after which the salt retention capability drops.

#### 5. Up/down DC/DC converter for energy recovery

As already mentioned, one of the most interesting aspects of CDI is the possibility of reusing the energy stored in capacitive cells or modules once the deionization phase has finished. The regenerative use of energy in CDI technology consists of using the energy stored in the CDI cell once it is saturated (the deionization process is finished and the cleaning process begins) and transferring it to other modules that begin their deionization phase. This strategy can be applied to several cells that exchange the energy involved in the process, thus defining a cycle to produce clean water.

In order to be able to transfer the energy stored in the CDI cell to another one, it is necessary to include a DC/DC power converter in the system (Figure 14).

Figure 14. Desalination diagram with an up-down converter for energy transfer from CDI cell-1 to CDI cell-2.

uc2ð Þi ¼ uc2ð Þ i�1 þ

1 toni þ toff i

�

iRMSC1i ¼

1 toni þ toff i

Pcond ¼

Pswitch ¼

P i i 2 RMSC1i þ i

1

2 � ð Þ tr þ tf P

The parameter R represents the total series resistance along the conduction path.

ECRpi ¼ 1 RP � uc2

voltages of capacitors C1 and C2 can be recalculated as follows:

ΔEC1i ¼ 1

ΔEC2i ¼ 1

iRMSC2i ¼

switching losses can be estimated:

period "i" due to self-discharge:

The RMS currents through the cells in each switching period can also be determined by:

� ðtoni

ð toniþtoff i

toni

0

Based on the RMS current values calculated with the equations above, conduction and

2 RMSC2i � � � toni þ toff i

i

Voltage losses due to cell self-discharge are also taken into account in the model by incorporating the following expression that represents the energy lost in a cell during the switching

Once the energy dissipated in each cycle (ECcond: energy loosed during conduction stage, ECswitch: energy loosed during switching stage, ECp: energy loosed in RP) is known, the real

2 � C1 � uc2

2 � C2 � uc2

From the previous expressions, the real voltage across each capacitor can be derived:

Ttotal

Ttotal

i tONi þ tOFFi

1ð Þ iþ1 � 1

2ð Þ iþ1 � 1

2 � C1 � uc2

2 � C2 � uc2

ΔErealC1i ¼ ΔEC1i � EC1condi � EC1switchi � ECRpi (14)

ΔErealC2i ¼ ΔEC2i � EC2condi � EC2switchi � ECRpi (15)

ILmax þ ILmin

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

Imin � Imax toff i

Imax � Imin toni

� t þ Imin � �2

vuuut (7)

� ð Þþ t � toni Imax � �2

� � � R

Imin � uc1i þ uc2ð Þ iþ1 � �þ

Imax � uc1ð Þ iþ1 þ uc2i � � !

vuuuut (8)

2 � C � toff ið Þ �1 (6)

Energy Recovery in Capacitive Deionization Technology http://dx.doi.org/10.5772/intechopen.75537

� dt

� � (11)

1i (12)

2i (13)

� dt

(9)

49

(10)

Figure 15. Voltage and current waveforms in the DC/DC converter.

One possible option for the converter topology is a buck-boost that operates at the variable frequency with hysteresis control where the maximum (iLmax) and minimum (iLmin) inductor current is fixed (Figure 15).

Limiting the inductor current (iLmin, iLmax) conditions the efficiency of the system, since these values are related to conduction and switching losses. The converter operation is based on transferring part of the energy stored at the input cell-1 to the inductor L during the period when transistor M1 is closed and M2 is open (tON). This period finishes when the inductor current reaches the maximum value defined by the converter control (iLmax). After this, transistor M2 is closed (M1 is opened) during time tOFF and the energy stored in the inductor is discharged on the output cell-2. This stage typically finishes when the inductor current becomes equal to zero (iLmin = 0).

Therefore, iLmax can be used as the control parameter of the up/down converter during the energy transfer between the input CDI cell, which is completely charged, and the output CDI cell, which is initially completely discharged. By increasing the value of iLmax, the time involved in the transfer will be reduced.

To optimize the efficiency of the CDI system, it is necessary to define the value of iLmax during the energy recovery process. High values of this parameter will increase conduction losses, whereas low values of iLmax will increase the transfer time and the self-discharge through the parallel resistance RP. On the other hand, the estimation of iLmax depends on the salt concentration (M) and the geometry of the CDI cells, which define the parameters RP, RS and C of the electrical model.

The DC/DC converter can be mathematically modeled assuming linear evolutions of the inductor current in each switching period (i) during the charge and discharge [13]. Therefore, when transistor M1 is conducting (M2 off), the discharge of the input cell is ideally described by:

$$\mathfrak{uuc}\_{1(i)} = \mathfrak{ucc}\_{1(i-1)} - \frac{I\_{L\max} + I\_{L\min}}{\mathfrak{2} \cdot \mathbb{C}} \cdot t\_{\mathfrak{ou}(i-1)} \tag{5}$$

Similarly, the output cell increases its voltage when M2 is on (M1 off). The equation defining such a process in each switching cycle is:

Energy Recovery in Capacitive Deionization Technology http://dx.doi.org/10.5772/intechopen.75537 49

$$\mathfrak{u}\mathfrak{c}\_{2(i)} = \mathfrak{u}\mathfrak{c}\_{2(i-1)} + \frac{I\_{\text{Lmax}} + I\_{\text{Lmin}}}{\mathbf{2} \cdot \mathbb{C}} \cdot t\_{\text{off}(i-1)} \tag{6}$$

The RMS currents through the cells in each switching period can also be determined by:

$$i\_{\rm RMS1\_i} = \sqrt{\frac{1}{t n\_i + t \theta f\_i} \cdot \int\_0^{t n\_i} \left(\frac{I\_{\rm max} - I\_{\rm min}}{t n\_i} \cdot t + I\_{\rm min}\right)^2 \cdot dt} \tag{7}$$

$$\dot{q}\_{\text{RMSC2}\_i} = \sqrt{\frac{1}{tron\_i + tgf\_i} \cdot \int\_{tou\_i}^{tou\_i + tgf\_i} \left(\frac{I\_{\text{min}} - I\_{\text{max}}}{tgf\_i} \cdot (t - ton\_i) + I\_{\text{max}}\right)^2} \cdot dt} \tag{8}$$

Based on the RMS current values calculated with the equations above, conduction and switching losses can be estimated:

$$P\_{cond} = \frac{\sum\_{i} \left( i\_{\text{RMS1i}}^2 + i\_{\text{RMS2i}}^2 \right) \cdot \left( ton\_i + tof\_i \right) \cdot R}{T\_{total}} \tag{9}$$

$$P\_{switch} = \frac{\frac{1}{2} \cdot (tr + tf) \sum\_{i} \left( \frac{I\_{\min} \cdot \left( \mu c\_{1i} + \mu c\_{2(i+1)} \right) +}{I\_{\max} \cdot \left( \mu c\_{1(i+1)} + \mu c\_{2i} \right)} \right)}{T\_{total}} \tag{10}$$

The parameter R represents the total series resistance along the conduction path.

One possible option for the converter topology is a buck-boost that operates at the variable frequency with hysteresis control where the maximum (iLmax) and minimum (iLmin) inductor

Limiting the inductor current (iLmin, iLmax) conditions the efficiency of the system, since these values are related to conduction and switching losses. The converter operation is based on transferring part of the energy stored at the input cell-1 to the inductor L during the period when transistor M1 is closed and M2 is open (tON). This period finishes when the inductor current reaches the maximum value defined by the converter control (iLmax). After this, transistor M2 is closed (M1 is opened) during time tOFF and the energy stored in the inductor is discharged on the output cell-2. This stage typically finishes when the inductor current

Therefore, iLmax can be used as the control parameter of the up/down converter during the energy transfer between the input CDI cell, which is completely charged, and the output CDI cell, which is initially completely discharged. By increasing the value of iLmax, the time

To optimize the efficiency of the CDI system, it is necessary to define the value of iLmax during the energy recovery process. High values of this parameter will increase conduction losses, whereas low values of iLmax will increase the transfer time and the self-discharge through the parallel resistance RP. On the other hand, the estimation of iLmax depends on the salt concentration (M) and the geometry of the CDI cells, which define the parameters RP, RS and C of the electrical model.

The DC/DC converter can be mathematically modeled assuming linear evolutions of the inductor current in each switching period (i) during the charge and discharge [13]. Therefore, when transistor M1 is conducting (M2 off), the discharge of the input cell is ideally described by:

Similarly, the output cell increases its voltage when M2 is on (M1 off). The equation defining

2 � C � ton ið Þ �1 (5)

uc1ð Þi ¼ uc1ð Þ i�1 � ILmax þ ILmin

current is fixed (Figure 15).

48 Desalination and Water Treatment

Figure 15. Voltage and current waveforms in the DC/DC converter.

becomes equal to zero (iLmin = 0).

involved in the transfer will be reduced.

such a process in each switching cycle is:

Voltage losses due to cell self-discharge are also taken into account in the model by incorporating the following expression that represents the energy lost in a cell during the switching period "i" due to self-discharge:

$$E c\_{\mathcal{R}p^i} = \frac{1}{\mathcal{R}\_P} \cdot \mu c\_i^2 \left( t\_{\mathcal{ON}\_i} + t\_{\text{OFF}\_i} \right) \tag{11}$$

Once the energy dissipated in each cycle (ECcond: energy loosed during conduction stage, ECswitch: energy loosed during switching stage, ECp: energy loosed in RP) is known, the real voltages of capacitors C1 and C2 can be recalculated as follows:

$$
\Delta E\_{C1i} = \frac{1}{2} \cdot \mathbf{C1} \cdot \boldsymbol{\omega} c\_{1(i+1)}^2 - \frac{1}{2} \cdot \mathbf{C1} \cdot \boldsymbol{\omega} c\_{1i}^2 \tag{12}
$$

$$
\Delta E\_{\rm C2i} = \frac{1}{2} \cdot \mathbf{C2} \cdot \mu c\_{2(i+1)}^2 - \frac{1}{2} \cdot \mathbf{C2} \cdot \mu c\_{2i}^2 \tag{13}
$$

$$
\Delta\text{Freal}\_{\text{C1i}} = \Delta E\_{\text{C1i}} - E\_{\text{C1cond}\_i} - E\_{\text{C1swidth}\_i} - E\_{\text{Cg}\_{\overline{\text{\gamma}}}} \tag{14}
$$

$$
\Delta\text{Freal}\_{\text{C2i}} = \Delta\text{E}\_{\text{C2i}} - \text{E}\_{\text{C2cond}\_i} - \text{E}\_{\text{C2switch}\_i} - \text{E}\_{\text{C}\_{\text{N}\_i}} \tag{15}
$$

From the previous expressions, the real voltage across each capacitor can be derived:

$$uc\_{1(i+1)\_{val}} = \sqrt{\frac{2 \cdot \left(\Delta E real\_{\text{C1}i} + \frac{1}{2} \cdot \mathbf{C1} \cdot \mu c\_{1i\_{val}}^2\right)}{\mathbf{C1}}} \tag{16}$$

$$uc\_{2(i+1)\_{ml}} = \sqrt{\frac{2 \cdot \left(\Delta \text{Freal}\_{\text{C2}i} + \frac{1}{2} \cdot \text{C2} \cdot \text{uc}\_{2i\_{ml}}^2\right)}{\text{C2}}} \tag{17}$$

The model described allows users to obtain a large amount of information related to the behavior of the converter: currents, voltages, transfer times, performance. But it can also provide insight into the influence that the desalination cells will have on these parameters. In order for this to be possible, the previous expressions must include the influence of the distance between electrodes "d," the number of electrodes "n," the molarity "M," and the S surface.

$$E\_{Rsm(i)}(d,n,M,S) = R\_S(d,n,M,S) \cdot \left(\mathbf{i}\_{RMSC1\_{(i)}}\right)^2 \cdot t\_{on} \tag{18}$$

$$E\_{\mathcal{R}s\mathcal{G}'(i)}(d,n,M,\mathcal{S}) = \mathcal{R}\_{\mathcal{S}}(d,n,M,\mathcal{S}) \cdot \left(\mathbf{i}\_{\text{RMSC2}\_{(i)}}\right)^2 \cdot \mathbf{t}\_{\text{off}} \tag{19}$$

$$E\_{R\varsigma(i)}(d,n,M,S) = E\_{R\varsigma on(i)}(d,n,M,S) + E\_{R\varsigma off(i)}(d,n,M,S) \tag{20}$$

$$E\_{R\_{\mathcal{P}(i)}}(d, n, M) = \int\_0^{T\_{(i)}} \frac{\left[\mu\_{\mathcal{C}(i)}(t)\right]^2}{Rp(d, n, M)} \cdot dt \tag{21}$$

By controlling the switching times of the semiconductors it is possible to control that the maximum current through the inductor follows the profile defined by the optimum current for each specific cell. Figure 18 shows the efficiency obtained in several cases when the optimal current is used and the maximum voltage is 1.5 V. It is important to point out the necessity of reducing the series resistance because it limits the maximum efficiency that can be obtained.

Energy Recovery in Capacitive Deionization Technology http://dx.doi.org/10.5772/intechopen.75537 51

Figure 16. Optimum current iLmax estimation flow chart per switching period.

$$E\_{\Gamma(i)}(d, n, M, S) = E\_{\mathbb{R}\_{\bar{S}(i)}}(d, n, M, S) + E\_{\mathbb{R}\_{\bar{V}(i)}}(d, n, M, S) + P\_{\text{coml}(i)}(d, n, M, S) \cdot T + P\_{\text{switch}(i)}(d, n, M, S) \cdot T \tag{22}$$

From the equation system described above, it is possible to determine the overall efficiency of the DC/DC converter together with the desalination cells [14].

Once the equations to calculate losses have been established, it is possible to determine the optimum iLmax current in each switching period by implementing an iterative process according to the flow chart of Figure 16. The procedure consists in increasing the value of iLmax in each switching period until the maximum efficiency is obtained for that switching period. After that, a new switching period is considered and a new iteration with iLmax is carried out in order to derive the optimum iLmax value for the new switching period. The process is repeated until the input CDI cell is completely discharged.

The process mentioned was applied to several cell configurations consisting of four electrodes of 250�250�5mm placed at different distances from one another, in which the salt concentration was also a parameter under control [14]. As an example, Figure 17 shows the optimal current when the cell parameters associated with this configuration are: C = 0.1F, Rs = 25mΩ, and Rp = 40 Ω. Taking these parameters into account, the calculation of the optimum current as indicated above gives rise to the evolution shown in Figure 17.

Energy Recovery in Capacitive Deionization Technology http://dx.doi.org/10.5772/intechopen.75537 51

Figure 16.  But it can also provide insight into the influence that the desalination cells will have on these parameters. In order for this to be possible, the previous expressions must include the influence of the distance between electrodes "d," the number of electrodes "n," the molarity "M," and the S

ERSon ið Þð Þ¼ d; n; M; S RSð Þ� d; n; M; S iRMSC1ð Þi

ERSoff ið Þð Þ¼ d; n; M; S RSð Þ� d; n; M; S iRMSC2ð Þi

ð Tð Þi

0

ET ið Þð Þ¼ d; n; M; S ERS ið Þ ð Þþ d; n; M; S ERP ið Þ ð Þþ d; n; M; S Pcond ið Þð Þ� d; n; M; S T þ Pswitch ið Þð Þ� d; n; M; S T

From the equation system described above, it is possible to determine the overall efficiency of

Once the equations to calculate losses have been established, it is possible to determine the optimum iLmax current in each switching period by implementing an iterative process according to the flow chart of Figure 16. The procedure consists in increasing the value of iLmax in each switching period until the maximum efficiency is obtained for that switching period. After that, a new switching period is considered and a new iteration with iLmax is carried out in order to derive the optimum iLmax value for the new switching period. The

The process mentioned was applied to several cell configurations consisting of four electrodes of 250�250�5mm placed at different distances from one another, in which the salt concentration was also a parameter under control [14]. As an example, Figure 17 shows the optimal current when the cell parameters associated with this configuration are: C = 0.1F, Rs = 25mΩ, and Rp = 40 Ω. Taking these parameters into account, the calculation of the optimum current as

ERP ið Þ ð Þ¼ d; n; M

the DC/DC converter together with the desalination cells [14].

process is repeated until the input CDI cell is completely discharged.

indicated above gives rise to the evolution shown in Figure 17.

By controlling the switching times of the semiconductors it is possible to control that the maximum current through the inductor follows the profile defined by the optimum current for each specific cell. Figure 18 shows the efficiency obtained in several cases when the optimal current is used and the maximum voltage is 1.5 V. It is important to point out the necessity of reducing the series resistance because it limits the maximum efficiency that can be obtained.

and the mathematical characterization of the DC/DC converter, it is possible to identify energy losses in the cell either by self-discharge or due to the current handled during the energy

Energy Recovery in Capacitive Deionization Technology http://dx.doi.org/10.5772/intechopen.75537 53

As a result, a clear identification of the power losses in all the system components is obtained. This makes it possible to identify the optimum current to minimize losses and optimize

The feedback parameter used by the converter control strategy described is the maximum current through the inductor, iLmax. This parameter is calculated at each switching period so as to obtain the optimum value that maximizes the efficiency of the energy transfer between

[1] UN-Water. Water in a Changing World. The United Nations World Water Development

[2] Younos T, Tulou KE. Overview of desalination techniques. Journal of Contemporary

[3] Fraidenraich N, Vilela OC, Lima GA, Gordon JM. Reverse osmosis desalination: Model-

[4] Oren Y. Capacitive deionization (CDI) for desalination and water treatment-past, present

[5] Gao Y, Li HB, Cheng ZJ, Zhang MC, Zhang YP, Zhang ZJ, Cheng YW, Pan LK, Sun Z. Electrosorption of cupric ions from solutions by carbon nanotubes and nanofibres film electrodes grown on graphite substrates. In: Proceedings of the IEEE Nanoelectronics

[6] Hwang S, Hyun S. Capacitance control of carbon aerogel electrodes. Journal of Non-

[7] Xu P, Drewes JE, Heil D, Wang G. Treatment of brackish produced water using carbon aerogel-based capacitive deionization technology. Water Research. 2008;42:2605-2617

recovery processes, together with the power losses in the converter.

CDI cells.

Author details

References

Francisco J. Álvarez-González

University of Oviedo, Gijón, Spain

\*Address all correspondence to<EMAIL_ADDRESS>
Water Research and Education. 2009;132:3-10

and future (a review). Desalination. 2008;228:10-29

Crystalline Solids. 2004;347:238-245

process efficiency for any salt concentration once the cell geometry is defined.

Alberto M. Pernía\*, Miguel J. Prieto, Juan A. Martín-Ramos, Pedro J. Villegas and

Report 3. UNESCO Publishing; Mar 2009. http://publishing.unesco.org/

ing and experiment. Applied Physics Letters. 2009;94:124102-124103

Conference INEC 2008; Shanghai, China; 24–27 March 2008. pp. 242-247

Figure 17. Optimum current iLmax estimated.

Figure 18. System efficiency operating at optimum current (red) and average optimum current during the energy transference (green).

The green plot determines the average of the optimal current during the whole energy transference process (right-hand scale).

The efficiency improvement depends on the CDI cell geometry and the salt concentration (M) because these magnitudes condition the values of the parameters that define the electrical model of the cell. Actual measurements confirmed that, by applying this control strategy, the efficiency was improved by 10% in most of the cases as compared to that obtained when using a constant current value during the charge/discharge process.

#### 6. Conclusions

The method presented allows the electrical characterization of the CDI cell in terms of salt concentration in the water and cell geometry. A model proposed is based only on three parameters RP, RS, C, which simplifies mathematical calculations. Using this electrical model and the mathematical characterization of the DC/DC converter, it is possible to identify energy losses in the cell either by self-discharge or due to the current handled during the energy recovery processes, together with the power losses in the converter.

As a result, a clear identification of the power losses in all the system components is obtained. This makes it possible to identify the optimum current to minimize losses and optimize process efficiency for any salt concentration once the cell geometry is defined.

The feedback parameter used by the converter control strategy described is the maximum current through the inductor, iLmax. This parameter is calculated at each switching period so as to obtain the optimum value that maximizes the efficiency of the energy transfer between CDI cells.


2.2.4. Scanning Electron Microscopy

Scanning electron microscopy was performed using the high-resolution scanning electron microscope (HR FE-SEM, TESCAN-MIRA3, Brno, Czech Republic) at the Kongju National University. The sample was coated with platinum (Pt) using an ion-coating machine at an accelerated voltage of 10.0 kV.


*Communication* **Characterization of a Mass-Produced SiPM at Liquid Nitrogen Temperature for CsI Neutrino Coherent Detectors**

**Fang Liu 1 , Xiaoxue Fan 1,2, Xilei Sun 2,\*, Bin Liu 1 , Junjie Li 2 , Yong Deng 2 , Huan Jiang 2 , Tianze Jiang 1 and Peiguang Yan 3**

**Abstract:** Silicon Photomultiplier (SiPM) is a sensor that can detect low-light signals lower than the single-photon level. In order to study the properties of neutrinos at a low detection threshold and low radioactivity experimental background, a low-temperature CsI neutrino coherent scattering detector is designed to be read by the SiPM sensor. Less thermal noise of SiPM and more light yield of CsI crystals can be obtained at the working temperature of liquid nitrogen. The breakdown voltage (Vbd) and dark count rate (DCR) of SiPM at liquid nitrogen temperature are two key parameters for coherent scattering detection. In this paper, a low-temperature test is conducted on the massproduced ON Semiconductor J-Series SiPM. We design a cryogenic system for cooling SiPM at liquid nitrogen temperature and the changes of operating voltage and dark noise from room to liquid nitrogen temperature are measured in detail. The results show that SiPM works at the liquid nitrogen temperature, and the dark count rate drops by six orders of magnitude from room temperature (120 kHz/mm2 ) to liquid nitrogen temperature (0.1 Hz/mm2 ).

**Keywords:** SiPM; breakdown voltage; dark count rate; liquid nitrogen temperature

#### **1. Introduction**

Silicon photo-multipliers (SiPM) have been developed rapidly in recent years as an effective alternative for conventional Photo-multiplier Tubes (PMT). The SiPM sensor has many excellent characteristics [1], such as: compact size, easy to develop into detector arrays, works under the low bias voltage (Vbias) and a strong ability to resist external magnetic and the electric field [2]. The key parameters, including the working voltage, dark count, quantum efficiency and gain for PMT and SiPM, are shown in Table 1 [3]. In addition, SiPM has high photon detection efficiency (PDE) and performs with an excellent single photon resolving ability. Due to these advantages, SiPM arrays are used for longrange high-speed light detection and the ranging (LiDAR) technique to achieve automotive, machine vision and spacecraft navigation [4,5]. The SiPM was used as read-out unit in the Circular Electron Positron Collider (CEPC) experiment for developing accurate measurements of the Higgs Boson [6], and the SiPM arrays were used to detect dark matter at liquid argon temperature [7]. The photon emission of SiPM from the avalanche pulses that were generated has been investigated in dark conditions [8].

**Citation:** Liu, F.; Fan, X.; Sun, X.; Liu, B.; Li, J.; Deng, Y.; Jiang, H.; Jiang, T.; Yan, P. Characterization of a Mass-Produced SiPM at Liquid Nitrogen Temperature for CsI Neutrino Coherent Detectors. *Sensors* **2022**, *22*, 1099. https://doi.org/ 10.3390/s22031099

Academic Editors: Jin Li and Marco Carminati

Received: 22 November 2021 Accepted: 28 January 2022 Published: 31 January 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Table 1.** Key parameters of PMT and SiPM.

The coherent elastic neutrino-nucleus scattering (CEνNS) method was first theorized by Freedman in 1974 [9,10] and was the dominated interaction for neutrinos in the energy range below 100 MeV. The COHERENT collaboration firstly detected the phenomenon of CEνNS by using CsI(Na) crystal detector to detect the neutrinos from the spallation neutron source at the Oak Ridge National Laboratory (ORNL) in 2017 [11,12]. The neutrinos produced from different neutrino sources presented different energy spectra, and as such the energy of the corresponding recoil nucleus from coherent scattering and the requirements for detection threshold and background are different [13]. The energy of neutrinos from the reactor source is in a low energy range, usually below 10 MeV, and the energy of the corresponding recoil nucleus is about a few keV. In addition, the count rate decreases exponentially as the detection threshold increases. Therefore, the threshold of the detector needs to be lower than 10 keV or less.

In order to achieve the low threshold detector, we propose a low temperature detector scheme using CsI crystals and SiPMs readout. CsI crystals have the highest light yield among mass produced crystals at low temperatures and can reach around 100 photons/keV [14]. However, the output signals of SiPM can effectively suppress its dark noise by controlling SiPM at low temperature, which can help to lower the detection threshold and obtain a high signal-to-noise ratio. The combination of CsI and SiPM can significantly reduce the detector threshold. Low temperatures can effectively suppress the shortcomings of the high dark count rate (DCR) of SiPMs. In older to reach the requirement of a low threshold detector, DCR should be lower than 0.1 Hz/mm2 at the liquid nitrogen temperature [15]. Selecting a SiPM that can work normally at the liquid nitrogen temperature is a key step in the detector scheme. It is best to be a mass-produced product for easy procurement. ON Semiconductor's J-Series SiPM is a product widely used in the industry, and we have successfully applied it to the GECAM project [15].

In this study, we design a cryogenic system that can cool the SiPM measurement system to liquid nitrogen temperature and keep the SiPM working environment within a certain temperature range. In this research, we investigate this mass-produced SiPMs at liquid nitrogen temperature to determine whether it can be used in a coherent scattering experiment to detect neutrinos at low threshold.

#### **2. Experiment Setup**

A set of self-made automatic temperature feedback control cryogenic systems is designed for cooling SiPM in an ease speed, because it will become brittle when the temperature of SiPM changes too fast, as shown in Figure 1a. It consists of a stainless-steel chamber, liquid nitrogen Dewar, temperature controller (AI-808P), temperature sensor, liquid nitrogen tank, solenoid valve and liquid nitrogen pipeline. We use three temperature sensors in this cryogenic system. One sensor (PT100) is placed at the outside bottom of the chamber to serve as the feedback of the temperature controller, and the other two (Probe C1 and C2, with distance of 5 cm) are put inside the chamber nearby SiPM to monitor the temperature in real time. The temperature controller, which adopts the proportionalintegral-derivative (PID) control, controls the amount of liquid nitrogen entering the Dewar through the solenoid valve according to the set cooling rate. The photo of the stainlesssteel chamber is shown in Figure 1b, with cable penetration and nitrogen replacement ports on the top. Before cooling down, the inside is replaced with nitrogen and sealed to avoid frost. The stable temperature keeping ability of the cryogenic system is important for the measurement accuracy. We measured the temperature retention ability of this

system at the setting temperature 0 ◦C, and recorded the real-time change of temperature within 8000 s. The result indicates that the temperature is stable at 0 ◦C with a variation range from −0.3 ◦C to 0.3 ◦C. The temperature control and cooling curve of the system is shown in Figure 2 and the black solid curve is the setting temperature, with the dash line and dash-dot line showing the value of two temperature probes at different moments. The temperature changes at Probe C1 and C2 are gentle, about 0.5 degrees per minute, and finally reaching a stable plateau with temperature of C1 is −193 degrees, and C2 is −196 degrees. The temperature of C2 is close to the surface temperature of the SiPM circuit board, which represents the temperature of SiPM. The SiPM is J-60035, size 6 × 6 mm2 [16].

**Figure 1.** (**a**) Scheme of the cryogenic system; (**b**) photo of the stainless-steel chamber.

**Figure 2.** The temperature control and cooling curve of the system.

A Pico-ammeter (Keithley 2450) is used to measure the current versus voltage (I-V) curve of SiPM. In particular, at the liquid nitrogen temperature, an LED (500 nm) light source inside the top of the chamber is turned on; otherwise the breakdown voltage (Vbd) of SiPM cannot be measured.

The dark noise measurement scheme is shown in Figure 3. The SiPM signal enters the FIFO (fin in and fin out) (N625) [17] after preamplification, and one fan-out signal enters the low-threshold discriminator (N841) [18] for triggering of the data acquisition system (DT5751) [19], and another fan-out signal is directly connected to the data acquisition system. DT5751 has a counting mode and a waveform acquisition mode, which are used for DCR measurement and single photon spectrum measurement, respectively. The threshold for dark noise measurement is set to 0.5 single photoelectron. The over voltage of SiPM is kept unchanged when comparing noise measurements at different temperatures. The over voltage can be calculated as following:

$$\mathbf{V}\_{\text{over}} = \mathbf{V}\_{\text{bias}} - \mathbf{V}\_{\text{bd}} \tag{1}$$

where Vover is the over voltage, Vbias is the working voltage and Vbd is the breakdown voltage.

**Figure 3.** The dark noise measurement scheme.

#### **3. Measurements and Analysis**

#### *3.1. I-V Curve*

The breakdown voltage (Vbd) is the bias point at which the electric field strength in the depletion region is large enough to induce a Geiger discharge. The Vbd point is clearly identified on a current versus voltage plot by the sudden increase in current. The I-V curve measured by the Pico-ammeter is shown in Figure 4a, where the typical curves at room temperature are shown. The Pico-ammeter shows the bias of the SiPM, and meanwhile records the currents of SiPM in the working voltage range. We set the step of working voltage change as 0.5 V, and the Pico-ammeter can display the current value varying with the voltage increasing. It can be seen that the Vbd of SiPM is around 25 V. Since the step size setting cannot be infinitesimally small, the accurate Vbd still needs to be obtained through data processing. The accurate Vbd is determined as the value of the voltage intercept of a straight line fit to a plot of √ *I* versus V [20] as shown in Figure 4b, and the fitting result at room temperature is 24.49 V which is very close to the report data (24.7 V) from ON Semiconductor's J-Series SiPM product sheet [17]. It shows that the experimental process and data processing method are reliable and effective. The Vbd at different temperatures measured in this way are shown in Figure 5. We can see that the Vbd decreases with the decrease of temperature. This change is almost linear when the temperature is higher than −120 ◦C, and the change rate is approximately 0.022 V/◦C. When the temperature becomes lower, the rate of Vbd decrease becomes slower.

The thermal vibration of the semiconductor lattice weakens with the decrease of temperature, which results in the widening of the barrier layer in the P-N junction. As such, the mean free path of the carrier movement increases and the energy obtained by the acceleration of the external electric field before colliding with the atom increases [21]. This leads to the enhancement of the chance of collision and ionization, and the probability of avalanche collision increases. Due to this condition, avalanche breakdown is more likely to occur. This is the reason that the Vbd becomes lower. Therefore, the Vbd of SiPM

decreases when the environmental temperature becomes lower. This is consistent with the experimental results.

**Figure 4.** (**a**) Current vs. bias voltage plot at room temperature; (**b**) √ *I*/ √ *A* vs. bias voltage plot at room temperature.

**Figure 5.** Vbd at different temperature, from 20 ◦C to −196 ◦C.

#### *3.2. Dark Noise*

SiPM has the ability to discern a single photon. Within the scope of certain intensity, the output of SiPM charge is proportional to the photon number and SiPM possess the function of the photon counter. SiPM with better performance can see clear single-photon peaks and multi-photon peaks in the spectrum of dark noise. As shown in Figure 6a, the energy spectra at room temperature and at liquid nitrogen temperature are indicated. In both cases, single-photon peaks can be seen. Since SiPM has lower dark noise at liquid nitrogen temperature, the resolution of single photon peaks is better than that at room temperature. This change shows in the peak-to-valley ratio. The resolution of single photon peak increases with increase of the peak-to-valley ratio. It can be seen from Figure 6 that the peak-to-valley ratio at room temperature is about 6 and this parameter is 12 at liquid nitrogen temperature. The single photoelectron and double photoelectron peaks can be seen clearly in the energy spectrum of SiPM. This indicates that SiPM works perfectly at liquid nitrogen temperature.

**Figure 6.** (**a**) Dark noise spectrum of SiPM at room temperature, Vover = 2.5 V; (**b**) dark noise spectrum of SiPM at liquid nitrogen temperature, Vover = 2.5 V.

Dark noise mainly refers to the dark current. That is, the internal current when the device works in a dark environment. DCR refers to the number of pulses output per unit of time under darkness condition. It directly influences the signal-to-noise ratio of the output signal from SiPM and heavily affects the energy resolution of the crystal scintillator detector. The dark current of SiPM is mainly composed of three parts, which are the surface leakage current, the thermal current caused by the lattice defects in the depletion zone, and the tunneling current caused by the tunnel effect. Dark counting is mainly caused by thermal effects and tunneling effects. It is unavoidable.

Thermal current refers to the current generated by triggering an avalanche during the transition from the valence band to the conduction band of electrons. These electrons are generated by thermal excitation in the depletion zone under the action of an electric field. The calculation formula of the thermal current is as follows:

$$I\_{\text{thermal}-current} = AT^2 \exp(-E/k\_B T) \tag{2}$$

where *A* is a constant, *T* is the temperature, *E* is the energy range between conduction band and valence band, and *kB* is Boltzmann constant. It can be seen from the formula that the thermal current is related to the temperature and the current increases with the rise of temperature, and this means a higher DCR. The measured DCR versus temperature curve is shown in Figure 7. DCR dropped by six orders of magnitude from room temperature (120 kHz/mm2 ) to liquid nitrogen temperature (0.1 Hz/mm2 ). The DCR decreases rapidly with the decrease of temperature from normal temperature to −100 ◦C, and the decrease gradually becomes slower after −100 ◦C.

Tunneling current means that the electrons act in a strong electric field to free themselves from the valence band and tunnel to the conduction band, causing an avalanche. The tunneling current is mainly affected by the electric field. The dark current caused by the tunneling effect becomes greater with a higher applied voltage of SiPM, as well as DCR. As shown in Figure 7, when at the same temperature, DCR at Vover = 6 V is higher than it at Vover = 2.5 V.

The change curve of DCR versus the threshold with 6 V over voltage at liquid nitrogen temperature is shown in Figure 8, and the obvious step structure can be seen, which corresponds to the separation structure of the dark noise spectrum. From the curves shown in Figure 8, the DCR reduces in a step-wise manner with the increase of the threshold. At the stage of low threshold, referring to the threshold of single photoelectron, DCR decreases rapidly. When the threshold range is in the two-photon to three-photon stage, DCR falls into a slower rate. The reason for this phenomenon is that, in the absence of light, most of the collected signals are single photoelectron signals. When the threshold is between single photoelectron and double photoelectrons, the single photoelectron signals get stuck, leaving only a small proportion of multi-photoelectron signals.

**Figure 7.** DCR vs. temperature, Vover = 2.5 V and Vover = 6 V.

**Figure 8.** DCR vs. threshold with 6 V over voltage at liquid nitrogen temperature.

#### **4. Conclusions**

Knowing the parameters of SiPM, like the Vbd, is a prerequisite for using SiPM at different temperatures. The Vbd provides a suitable operating voltage range for the use of SiPM. Dark noise will directly affect the signal-to-noise ratio, which is very important in the detection and discrimination of single-photon weak signals using SiPM.

In order to measure the performance of SiPM at liquid nitrogen temperature, we design a cooling system that can maintain the experiment environment at the setting temperature, and achieve a smooth cooling process of SiPM. The experimental results denote that the system can achieve stable control of the setting temperature ranging from the room temperature to liquid nitrogen temperature. The measured result shows that when the setting temperature is 0 ◦C, the temperature of the control system can fluctuate within the temperature band of −0.3 ◦C to 0.3 ◦C. Compared with the conventional temperature, we prefer the ultra-low temperature range. After fitting the experimental result, the rate of decline is less than 0.5 ◦C/min, which will not cause mechanical damage to the SiPM. We

measure the changes of temperature near SiPM when the system temperature decreases, the experimental results show that the mass-produced J series SiPM works properly at liquid nitrogen temperature. The changes of Vbd and DCR with temperature are measured in detail; both of them decrease with the decrease of temperature, and the change is relatively fast at the beginning, and gradually slows down after −100 ◦C. DCR drops by six orders of magnitude at liquid nitrogen temperature to about 0.1 Hz/mm2 and this meets the requirement of a low threshold detector designing scheme. Preliminary results indicate that the J series SiPMs is one of the candidate devices for neutrino coherent detectors.

**Author Contributions:** Methodology, X.F. and X.S.; formal analysis, B.L.; investigation, J.L. and X.S.; resources, X.S. and F.L.; data curation, Y.D.; writing—original draft preparation, X.F.; writing—review and editing, X.S. and F.L.; visualization, H.J.; supervision, B.L.; project administration, P.Y. and T.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the National Natural Science Foundation of China (11775252) and State Key Laboratory of Particle Detection and Electronics (SKLPDE-ZZ-202116).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Available on request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**3.2 Actuators**

The force applied to a vehicle due to the various actuators of a vehicle can be formulated as (34),

$$
\pi = T\mathbb{K}u \tag{34}
$$

where, for an AUV operating with 6 DoF with *n* actuators, *T* is the actuator configuration matrix of size 6 × *n* , *K* is the diagonal force coefficient matrix of size *n n* × , and *u* is the control input of size 1 *n*× . Actuators are the physical components that apply the desired force to the vehicle, and the particular configuration of these actuators will determine the size and structure of *T*, *K* and *u*, with each column of *T*, denoted *ti*, in conjunction with the corresponding element on the main diagonal of *K*, representing a different actuator.

A vast array of actuators are available to underwater vehicle designers, the more typical of which include propellers, control fins and tunnel thrusters, and each has their own properties that make them desirable for implementation within AUVs. For all the following actuator descriptions *lx* defines the offset from the origin of the actuator along the x-axis, *ly* defines the offset along the y-axis, and *lz* defines the offset along the z-axis.

#### **3.2.1 Propellers**

Propellers are the most common actuators implemented to provide the main translational force that drives underwater vehicles. These are typically located at the stern of the vehicle and apply a force along the longitudinal axis of the vehicle. The structure of *ti* for a propeller is given in (35).

$$t\_i = \begin{bmatrix} 1 & 0 & 0 & 0 & l\_z & -l\_y \end{bmatrix}^T \tag{35}$$

As can be seen from (35), if the propeller is positioned such that there is no y-axis or z-axis offset, the force produced will be directed entirely along the x-axis of the vehicle, with no rotational moments produced.

#### **3.2.2 Control surfaces**

Control surfaces, or control fins, are actuators that utilise Newton's Third Law of motion to apply rotational moments to the vehicle. These surfaces apply a force to the water which causes a deflection in the water's motion. Hence, the water must also apply a force to the control surface. Due to this force being applied at a distance from the centre of gravity of the vehicle, a rotational moment is produced that acts on the vehicle. The typical configuration for control surfaces on an AUV is to have four independently controlled fins arranged in two pairs orientated horizontally and vertically at the stern of the vehicle. The structure of *ti* for the horizontal fins is given in (36),

$$t\_i = \begin{bmatrix} \mathbf{0} & \mathbf{0} & \mathbf{1} & l\_y & -l\_x & \mathbf{0} \end{bmatrix}^T \tag{36}$$

and for vertical fins given in (37).

158 Autonomous Underwater Vehicles

The role of the control allocation module is to generate the appropriate signals to the actuators in order for the generalised force from the control law to be applied to the vehicle. Since the vehicle under consideration is over-actuated, which means multiple actuators can apply forces to a particular DoF, the control allocation is responsible for utilising all available actuators in the most efficient way to apply the desired force to the vehicle. Power consumption is of particular importance for all autonomous vehicles, as it is a key factor in determining the total mission duration. The control allocation is therefore responsible for

The force applied to a vehicle due to the various actuators of a vehicle can be formulated as

where, for an AUV operating with 6 DoF with *n* actuators, *T* is the actuator configuration matrix of size 6 × *n* , *K* is the diagonal force coefficient matrix of size *n n* × , and *u* is the control input of size 1 *n*× . Actuators are the physical components that apply the desired force to the vehicle, and the particular configuration of these actuators will determine the size and structure of *T*, *K* and *u*, with each column of *T*, denoted *ti*, in conjunction with the

A vast array of actuators are available to underwater vehicle designers, the more typical of which include propellers, control fins and tunnel thrusters, and each has their own properties that make them desirable for implementation within AUVs. For all the following actuator descriptions *lx* defines the offset from the origin of the actuator along the x-axis, *ly*

Propellers are the most common actuators implemented to provide the main translational force that drives underwater vehicles. These are typically located at the stern of the vehicle and apply a force along the longitudinal axis of the vehicle. The structure of *ti* for a propeller

As can be seen from (35), if the propeller is positioned such that there is no y-axis or z-axis offset, the force produced will be directed entirely along the x-axis of the vehicle, with no

Control surfaces, or control fins, are actuators that utilise Newton's Third Law of motion to apply rotational moments to the vehicle. These surfaces apply a force to the water which causes a deflection in the water's motion. Hence, the water must also apply a force to the control surface. Due to this force being applied at a distance from the centre of gravity of the vehicle, a rotational moment is produced that acts on the vehicle. The typical configuration

*T i z y t* = ⎡ *l l* − ⎤ ⎣ ⎦ (35)

= *TKu* (34)

applying the desired forces to the vehicle, while minimising the power consumed.

τ

corresponding element on the main diagonal of *K*, representing a different actuator.

defines the offset along the y-axis, and *lz* defines the offset along the z-axis.

1000

**3.1 Role**

**3.2 Actuators**

**3.2.1 Propellers**

is given in (35).

rotational moments produced.

**3.2.2 Control surfaces**

(34),

$$t\_i = \begin{bmatrix} 0 & 1 & 0 & -l\_z & 0 & l\_x \end{bmatrix}^T \tag{37}$$

These structures of *ti* show that horizontal surfaces produce a heave force as well as roll and pitch moments, while vertical surfaces produce a sway force as well as roll and yaw moments. What must be considered here is that the force being produced by control surfaces relies on the vehicle moving relative to the water around it. If the vehicle is stationary compared to the surrounding water, control surfaces are ineffective. However, if the vehicle is moving relative to the surrounding water, these actuators are capable of applying forces and moments to the vehicle while consuming very little power.

### **3.2.3 Tunnel thrusters**

The previously mentioned limitation of control surfaces can be overcome by the use of tunnel thrusters. These thrusters are usually implemented by being placed in tunnels transverse to the longitudinal axis of the vehicle. Similar to control surfaces, the typical arrangement is to position two horizontal tunnel thrusters equidistant fore and aft of the centre of gravity, and two vertical tunnel thrusters also equidistant fore and aft of the centre of gravity.

The structure of *ti* for horizontal thrusters is given in (38),

$$t\_i = \begin{bmatrix} 0 & 1 & 0 & -l\_z & 0 & l\_x \end{bmatrix}^T \tag{38}$$

while the structure for vertical thrusters is given in (39).

$$t\_i = \begin{bmatrix} 0 & 0 & 1 & l\_y & -l\_x & 0 \end{bmatrix}^T \tag{39}$$

What can be observed here is that horizontal thrusters provide a sway force as well as a roll and yaw moment, while vertical thrusters provide a heave force as well as roll and pitch moment. In general, horizontal tunnel thrusters are located such that *lz* is zero, and vertical thrusters are located such that *ly* is zero. The result of this choice is that no roll moment is produced by these actuators.

The advantage of tunnel thrusters is that forces and moments can be produced even if the vehicle is stationary with respect to the surrounding water. This greatly increases the manoeuvrability of the vehicle, as control of the vehicle when moving at low speeds is possible. However, there are limitations associated with the use of these actuators. Firstly, these actuators consume more power when activated compared to control surfaces. This is due to force being produced by the thrusters only when the thruster itself is activated. In contrast, control surfaces consume power when the deflection angle is altered, but require very little power to hold the surface in place once the desired angle has been achieved.

Fully Coupled 6 Degree-of-Freedom Control of an Over-Actuated Autonomous Underwater Vehicle 161

A third scheme that is proposed here for the implementation of the control allocation is to break the control allocation problem into two smaller sub-problems, as seen in Figure 2, where the first sub-problem addresses control allocation to the main propeller and control surfaces, and the second addresses control allocation to the tunnel thrusters. Using this type of scheme, the control surfaces can firstly be used to their full extent in order to realise the generalised force as closely as possible. Only after full utilisation of the control surfaces occurs will the tunnel thrusters be introduced to provide forces and moments that the surfaces alone cannot produce. Using this methodology, the low power consumption control surfaces will be used as much as possible, while the higher power consumption tunnel thrusters will be called upon only when required to provide the extra manoeuvring capabilities that they possess. Furthermore, in a situation where accurate manoeuvring is not required, such as traversing from one waypoint to the next with no concern for what trajectory the vehicle follows, the second stage can be disabled such that no tunnel thruster is used, and control is performed entirely by the main propeller and control surfaces. However, if trajectory tracking is desired, the thruster allocation module can still be enabled. This will allow for the situation when the control surfaces provide inadequate force to the vehicle, and therefore the thrusters can assist in providing the extra force required to

**3.3.3 2-Stage scheme**

maintain the vehicle tracking the desired trajectory.

Fig. 2. 2-Stage Control Allocation Scheme Block Diagram.

control surfaces over tunnel thrusters.

Implementation of this scheme would look somewhat like a 2-stage non-optimal scheme, as seen in Figure 2. The first stage would require the matrix operation (*TK*)*-1τ* for the main propeller and control surfaces in order to obtain as much force required from these actuators. An estimate of the force produced for this particular set of control values would then be calculated such that this force estimate can be subtracted from the total force required. Any residual force requirement would then become the input to the second stage

thrusters in order for these actuators to provide any extra force that the control surfaces cannot deliver alone. Therefore, the computational requirement for this scheme is quite minimal compared to the quadratic programming scheme, yet still heavily biases the use of

*TK* τ

− for the tunnel

of the control allocation, which would perform the matrix operation ( ) 1

Secondly, thruster efficiency is reduced when the vehicle is moving. Under certain conditions, an area of low pressure is produced at the exit of the tunnel, which has the effect of applying a force to the vehicle in the opposite direction to which the water jet from the tunnel thruster is attempting to provide. The result is less total force being applied to the vehicle, and therefore reduced performance when moving at non-zero forward speeds (Palmer et al., 2009).

### **3.3 Allocation methods**

As previously mentioned when reviewing the various actuators for underwater vehicles, if the vehicle is stationary, control surfaces are ineffective while tunnel thrusters are very useful. Conversely, if the vehicle is moving, control surfaces are very efficient in providing force relative to power consumption compared to tunnel thrusters. The role of the control allocation is therefore to find a compromise between all actuators that both applies the desired generalised forces and moments to the vehicle while minimising power consumption. This balancing act allows the vehicle to maintain the manoeuvrability provided by all actuators, while at the same time allowing for as long a mission duration as possible.

#### **3.3.1 Non-optimal scheme**

One of the most straightforward methods for control allocation is application of the inverse of (34), i.e., (40) (Fossen, 2002).

$$
\mu = \left(TK\right)^{-1}\tau\tag{40}
$$

This method is very simple to implement as it consists of a single matrix multiplication. Therefore it is easy and efficient to implement within the computational processing constraints of an AUV. However, due to its simplicity, no attempt is made to minimise power consumption. If the vehicle contains both control surfaces and tunnel thrusters, both of these types of actuators will be utilised equally, even when the vehicle is moving at maximum velocity with respect to the surrounding fluid. However, since it is much more efficient to utilise control surfaces rather than tunnel thrusters while the vehicle is moving with respect to the water, a more intelligent approach is desired for implementing control allocation to minimise power consumption.

### **3.3.2 Quadratic programming**

The limitations of the aforementioned non-optimal scheme can be overcome by formulating a quadratic programming optimisation problem to solve for actuator inputs (Fossen, 2002, Fossen et al., 2009). By introducing a weighting matrix into the problem statement, actuator usage can be biased towards utilising control surfaces over tunnel thrusters. Therefore, the generalised force desired from the control law can be realised by the actuators while minimising power consumption. There are however, limitations associated with this scheme. Firstly, although it is possible to calculate an explicit solution to this problem, in the event of actuator reconfiguration, such as an actuator failure, this explicit solution would need to be recalculated, which can be computationally intensive. Iterative approaches, such as sequential quadratic programming, can be implemented that allow for actuator failures, but this method has the potential to require several iterations of the programming problem be solved at each control sample interval. Again, this can be a computationally intensive task.


Microgrid Application in Algeria Saharian Remote Areas

*Mounir Khiat, Sid Ahmed Khiat, Mohamed Mankour and Leila Ghomri*

## **Abstract**

This paper presents a model and simulation for the development of microgrids in remote areas of the Algerian Sahara, including micro power plants, photovoltaic panels, wind farms, diesel energy and storage facilities. The climate of the Algerian Sahara, located on both sides of a tropical region, is hot, sunny and arid. Daytime temperatures are very high and can exceed 50°C, while the thermal amplitude between day and night is often above 350 or 400°C. In addition, there are many microclimates that are characterised by very high wind speeds. This means that wind energy and photovoltaic energy are both widely appropriate in this field, especially if we assume that the distribution of the population is very dispersed. The creation of microgrids for consumption will be an interesting solution to provide energy to the local population. The microgrid is part of the electrical system and is very dynamic. Production and supply forecasts will lead to reshipment, demand and price effects on regional markets. These feedback effects must be modelled and understood to achieve a stable energy system based on renewable energy.

**Keywords:** remote areas, design microgrid, modelling, real-time simulation

### **1. Introduction**

In the south of Algeria, temperature passes through two extreme values, i.e., from −10 to 34°C (14–93°F), and in some years can reach 49°C (120°F). We see that temperature variation in a day can be more than 44°C (80°F).

On the other hand, winds in the same area are frequent and strong. Rainfall is distributed in irregular manner along the year [1, 2].

The Sahara region is divided into several areas, such as, Bas-Sahara [1], which represents more than half of the population; the western and southern extremities, which are still sparsely populated (less than 0.2 h/km2 ), as shown in **Table 1**; followed by Central Western Sahara, where four regions have just over 500,000 inhabitants; and, the northeast Sahara, which represents the most populated region of the Sahara: 1,900,000 inhabitants, or more than two thirds of the Saharan population, live in an area of less than 10% of the desert, with an average density of more than 10 inhabitants/km2 [3].

These data show that it is impossible to supply energy from conventional power plants. This is why we propose a microgrid model that will be autonomous and selfcontrolled to ensure continuity of service.

Microgrid power supply networks are emerging to produce, distribute and regulate the flow of electricity at the local level. Microgrids are ideal for university

#### *Micro-Grids - Applications, Operation, Control and Protection*

#### **Table 1.**

*Evolution of forecasting power demand.*

campuses, military bases, remote sites, office buildings and industrial sites. Improving power quality and reducing transmission losses, robustness and resilience are the main characteristics of microgrid systems [4].

The electricity market in Algeria is very important. Sonelgaz (National Society for Electricity and Gas) is the main supplier of electricity and gas. The country's power plants are open-cycle gas turbines, combined-cycle gas turbines, conventional steam turbines and, more recently, renewable energy sources. Recently, the coverage capacity of the electrical installation's network has reached 98%, with more than 80% in the north [5].

The objective of the real-time simulation is to test the various electrical equipments, under the most natural conditions possible: as if they were connected to the real physical systems associated with them.

The results obtained by this tool will allow us to have a very precise vision of the functioning of microgrids, in terms of power flow or default responses.

The remaining part of the paper is organised as follows: Section 2 describes the evolution of renewable energies in Algeria. In Section 3, we discussed the modelling of a microgrid in Adrar area, located southwest of Algiers, and Section 4 presents a real-time simulation. Finally, Section 5 summarises the results of this work and perspectives of future works.

### **2. Renewable energy evolution in Algeria**

Algeria's national priority is to achieve the multiplication of energy produced by renewable sources. Algeria has great potential in solar energy and may become a leader in the MENA region, and an interesting partner in the world.

Solar and wind energies tend to have become a more serious and efficient solution to enhance supply security and reliability of the whole power system. It contributes too to increasing energy conversion efficiency, transmission and distribution [6].

Since the 1970s, the national programme has strongly supported the use of solar and wind energies; solar is positioned in the first position, but now wind power production is also being strengthened, as shown in **Figure 1**.

Economically, conventional power supply by extending the networks is not suitable for remote centres. This is true for the Sahara regions with its surface area of about 2 million km2 , and only an autonomous mean of supply is to be considered. For this reason, microgrid is the appropriate solution.

**Table 1**, published by the Algerian National Society for Electricity and Gas (SONELGAZ), shows the evolution of the power forecasts in southern Algeria and in particular in the Adrar area studied.

**27**

**Figure 2.**

*Microgrids architecture.*

*Microgrid Application in Algeria Saharian Remote Areas*

**3. Modelling a microgrid in the Adrar study area**

A simulation model is presented in **Figure 2** (**Table 2**).

energy advisor to ensure the best possible result.

utilities must be taken into account.

*Algerian renewable energy distribution.*

**3.1 General architecture**

itself [10].

**Figure 1.**

Microgrids must adapt existing frameworks. For this reason, the project must be carefully coordinated between the proponent, the utility and the proponent's

For electricity interconnection with the grid and to optimise reliability, resilience and efficiency of the grid, utility tariffs and franchise fees must be adapted. When designing the microgrid stage, the interconnection specifications of local

In the first step to designing our microgrid, we will begin by the wind farm of Kabertene (70 km north of Adrar), which produce about 10 megawatts (MW). This parc is composed of 12 wind turbines, each one have a unit capacity of 0.85 MW. It is interconnected with the 220/30 KV substation located in the locality

*DOI: http://dx.doi.org/10.5772/intechopen.86352*

*Microgrid Application in Algeria Saharian Remote Areas DOI: http://dx.doi.org/10.5772/intechopen.86352*

*Micro-Grids - Applications, Operation, Control and Protection*

Maximal power needed from interconnected

Maximal power from isolated south network

*Evolution of forecasting power demand.*

network

(kw)

**Table 1.**

campuses, military bases, remote sites, office buildings and industrial sites. Improving power quality and reducing transmission losses, robustness and resil-

The electricity market in Algeria is very important. Sonelgaz (National Society for Electricity and Gas) is the main supplier of electricity and gas. The country's power plants are open-cycle gas turbines, combined-cycle gas turbines, conventional steam turbines and, more recently, renewable energy sources. Recently, the coverage capacity of the electrical installation's network has reached 98%, with

**Power Realisation Evolution rate**

Maximal power from Adrar pole (kw) 261 279 288 8.2

2014 2015 2016

10927 12380 28390 3.7

214 227 242 25.5

**(%)**

The objective of the real-time simulation is to test the various electrical equipments, under the most natural conditions possible: as if they were connected to the

The results obtained by this tool will allow us to have a very precise vision of the

The remaining part of the paper is organised as follows: Section 2 describes the evolution of renewable energies in Algeria. In Section 3, we discussed the modelling of a microgrid in Adrar area, located southwest of Algiers, and Section 4 presents a real-time simulation. Finally, Section 5 summarises the results of this work and

Algeria's national priority is to achieve the multiplication of energy produced by renewable sources. Algeria has great potential in solar energy and may become a

Since the 1970s, the national programme has strongly supported the use of solar and wind energies; solar is positioned in the first position, but now wind power

, and only an autonomous mean of supply is to be considered.

Economically, conventional power supply by extending the networks is not suitable for remote centres. This is true for the Sahara regions with its surface area

**Table 1**, published by the Algerian National Society for Electricity and Gas (SONELGAZ), shows the evolution of the power forecasts in southern Algeria and

Solar and wind energies tend to have become a more serious and efficient solution to enhance supply security and reliability of the whole power system. It contributes too to increasing energy conversion efficiency, transmission and

functioning of microgrids, in terms of power flow or default responses.

leader in the MENA region, and an interesting partner in the world.

production is also being strengthened, as shown in **Figure 1**.

For this reason, microgrid is the appropriate solution.

in particular in the Adrar area studied.

ience are the main characteristics of microgrid systems [4].

more than 80% in the north [5].

perspectives of future works.

distribution [6].

of about 2 million km2

real physical systems associated with them.

**2. Renewable energy evolution in Algeria**

**26**

**Figure 1.** *Algerian renewable energy distribution.*


2.1.2. Signature-Based IDS

In signature-based IDS, known threats previously discovered and identified from the system help to identify threats in the future. This technique is used to compare the incoming network pattern. An intrusion is detected when the incoming network pattern matches the signature. The advantage of this type of detection method is that it is easy to develop and understand by knowing the network behavior signatures. This technique has very high accuracy and a minimum number of false positives in detecting known attacks. Also, it can add new signatures into the database without changing or modifying existing ones.

The main drawback issue of this IDS technique is that these types of intrusion detecting systems are not able to detect new types of attacks or unknown attacks; a slight change in the pattern or slight variation can fool it. Figure 2 presents the general architecture of signature-based IDS. A few examples of related work that applied signature-based IDS are listed here.

**Figure 2.** Signature-based IDS architecture [13].

Saraniya [21] developed a network intrusion detection system (NIDS) to secure the network using a signature-based IDS algorithm. It succeeded in capturing packets sent across the entire network using mixed mode and comparing traffic to designer attack signatures. It secured the network and reduced the memory space in the environment. Signature-based IDS are not able to detect emerging and unknown attacks because the signature database must be manually reviewed for every new type of intrusion discovered.

Gao and Morris [22] presented a study on cyberattacks and signature-based IDS for MODBUS based industrial control systems. The rules described were designed to detect the attacks described earlier. The rules were divided into two types: independent and statebased rules. Independent rules were those that analyzed one MODBUS packet, looking for a specific signature match. Standalone rules were those that were enforced with the Snort intrusion detection tool.

Uddin et al. [23] proposed a signature-based distributed IDS using a mobile agent, to transfer the signature database from a large complementary database to a small signature database, then update the databases regularly as new signatures are discovered. The results of the proposed model indicated that IDS worked better than regular systems that used only one database of chain signatures.

Kumar and Gobil [24] implemented a signature-based IDS. They used three tools to develop this IDS system: SNORT, BASE, and TCP REPLAY. This system could detect and analyze intrusions in the network traffic in real time.

The main characteristics and limitations of anomaly-based, signature-based, and hybrid IDS are listed in Table 1.

**Table 1.** Summary of intrusion detection techniques [17].


**5. Role of SOD in other fungal infection**

Superoxide of pathogenic fungus are cofactors with Cu/Zn or Mn metals. The enzymes are localized in the cytosol as well as in mitochondria and involved in cell differentiation and multi-stress conditions. Mitochondrial Mn-SODs prevent the damages of oxidative stress, osmotic and thermal stresses in yeast cells. SODs protein has been shown to contribute to the virulence of many intracellular pathogenic fungi, such as *C. neoformans* [60], and *H. capsulatum*, both are capable to some degree of neutralizing the lethal levels of ROS produced by the host cells [64]. *C. neoformans* have Zn-SOD and Mn-SOD, while *H. capsulatum* has Cu/Zn-SOD. However, some fungal pathogens and fungal-like oomycetes have a unique SOD, such as Cu-SODs (SOD5). SOD5 are closely associated with the ubiquitous class of Cu/Zn-SODs but lack a Zn cofactor [34] and are believed to act on substrate level [131–133]. Unlike Cu/ZnSODs, which is found in both intra- and extracellularly, Cu-SODs are found exclusively in extracellular, and they appear primarily appended to the GPI anchors protein of cell surface [134, 135]. Cu-SODs have been proved to protect pathogens from the oxidative burst of the host regulated by immune cells [9] **Table 1**.