**3. Results**

#### **3.1 Evidence for the presence of NHE1, NCX1, and Na/K ATPase**

#### *3.1.1 Sodium flux studies*

22Na uptake was measured in membrane vesicles from brain capillary endothelial cells, and revealed both saturable and unsaturable components (**Figure 1**). In the presence of specific inhibitors, three transport pathways were found, including: (a) a low affinity (Km = 52 mM), high capacity, dimethyl amiloride (DMA, 100 μM) sensitive sodium carrier, indicative of Na/H antiport; (b) a high affinity (Km = 4.6 mM), low capacity, DMA resistant carrier; and (c) a non-specific phenamil inhibitable cationic channel (Kd = 1.7 μl/mg/min).

#### *3.1.2 Immunoblotting*

Immunoblotting using specific antibodies (Chemicon) to NHE1 (Na/H antiporter, isoform 1) and NCX1 (Na/Ca exchanger, isoform 1) revealed that both proteins are present in isolated luminal and abluminal membranes derived from

#### **Figure 1.**

*Both saturable and unsaturable 22Na uptake was measured in plasma membrane vesicles derived from bovine brain capillary endothelial cells. A saturable component (Km = 52 mM) was dimethyl amiloride (DMA) sensitive (100 μM), indicative of a sodium-hydrogen ion exchanger.*

**203**

**Figure 2.**

*Prevention of Oxidative Injury Associated with Thrombolysis for Ischemic Stroke*

reported in the current study confirmed that it is the α3 isoform.

**3.2 Evidence that Na/H exchange elevates intracellular sodium**

**3.3 Evidence that Na/Ca exchange elevates intracellular calcium**

exchange contributed to the sustained elevation of calcium.

*homology (96.8%) for the respective isoform in human tissue.*

brain capillary endothelial cells. NHE1 appeared as a single band with a molecular weight of 106 kD. NCX1 displayed the usual two bands at 43 kD and 150 kD.

Messenger RNA was prepared from isolated bovine brain capillaries, and RT-PCR was performed with specific primers identifying NHE1, Na/K ATPase α2, and Na/K ATPase α3. The expected PCR products were found for NHE1 (**Figure 2**) and Na/K ATPase α3, and sequencing demonstrated a high level of homology to the human transporters (>90%). Na/K ATPase activity was previously measured by us in luminal and abluminal membrane vesicles derived from endothelial cells forming the blood-brain barrier [25]. Activity was found to be present predominantly at the abluminal membrane domain, and was characterized by a ouabain binding constant (Kd = 25 ± 3 nM) typical of either the α2 or α3 isoform [49]. The PCR studies

Intracellular sodium concentration was measured in cultured bovine brain capillary endothelial cells exposed to conditions simulating ischemia and reperfusion. **Figure 3A** shows that intracellular sodium was elevated (P < 0.05) during ischemia (120 minutes), and remained elevated during ischemia followed by reperfusion (90/30 minutes). Inhibiting the Na/H antiporter with 100 μM dimethyl amiloride (DMA) completely prevented the elevation of intracellular sodium observed during ischemia/reperfusion (90/30 minutes), indicating that its activity was responsible for the sustained increase in intracellular sodium observed during

Intracellular calcium was measured in cultured bovine blood-brain barrier cells exposed to conditions simulating ischemia and reperfusion. **Figure 3B** shows that calcium was significantly (P < 0.05) elevated during ischemia (120 minutes), and remained elevated during ischemia followed by reperfusion (90/30 minutes). Inhibition of the reverse mode of Na/Ca exchange (20 μM KB-R7943) reduced (P < 0.05) the level of intracellular calcium observed following reperfusion. Calcium uptake was also significantly inhibited with DMA (100 μM, not shown), indicating that both activation of Na/H antiport and reverse movement of Na/Ca

*The RT-PCR product for NHE1 was derived from bovine brain capillary endothelial cells, with a high*

*DOI: http://dx.doi.org/10.5772/intechopen.84774*

*3.1.3 PCR studies*

ischemia and reperfusion.

brain capillary endothelial cells. NHE1 appeared as a single band with a molecular weight of 106 kD. NCX1 displayed the usual two bands at 43 kD and 150 kD.


3.2.4. pH and Conductivity

Monitoring pH value is an indicator of the degree of biological and biochemical decomposition [33]. Electrical conductivity is a commonly used parameter for monitoring the amounts of nutrients, salts and impurities in the solution [55]. Since the composition of substrates could significantly change during pretreatment, pH and conductivity values could also significantly fluctuate. pH and conductivity measurements in the analyzed samples are shown in Figure 5.

pH values were generally higher in the untreated samples, and decreased during pretreatment, especially for the grass samples. This can be explained by the fact that, because of the degradation of organic compounds during thermal pretreatment, amino acids, ammonia and fatty acids are formed, which cause a drop in pH [29]. At higher temperatures, amino acids could also be degraded, and the pH increases again [56]. This can be clearly noticed in the sample with the combination of sewage sludge and grass (G + S), where an increase in pH is noticed at 80 ◦C, as compared with the pH value at 36.8 ◦C. Another interesting observation is that, in the case of samples containing only sewage sludge (S), the pH value after thermal pretreatment at 38.6 ◦C increased slightly and decreased after pretreatment at a higher temperature (80 ◦C). This could be related to the release of NH4 <sup>+</sup>, which could increase the pH of the solution when present in higher concentrations.

When biological pretreatment was performed, pH increased slightly with the addition of rumen fluid, most likely because of the slightly alkaline environment of rumen fluid (the pH of untreated sample was 7.5). For high degradation efficiency, maintaining a pH value in the optimal range is important, and the natural buffering ability of rumen fluid plays a major role in that process [57]. However, the optimal pH for most lignocellulose-degrading enzymes should be between 4.5 and 6.0 [58], and for methanogenic bacteria, the optimal range is between 6.6 and 7.6 [59], although a wider

range (between 6.5 and 8.2) has also been reported [60]. Hu et al. [52] reported that acidogenesis of cattail by rumen cultures is possible at higher pH (pH of 6.9). Similar values for treated samples were found in the present study (pH values between 5.1 and 7.2).

**Figure 5.** pH and conductivity (μS/cm) measurements.

Conductivity of the samples increased with pretreatment at lower temperatures (38.6 ◦C) and decreased with pretreatment at higher temperatures (80 ◦C). This can be confirmed by the findings of some authors who stated that electrical conductivity increases at temperatures up to 50 ◦C and becomes linear over time with any further increase in temperature [61].

The highest conductivity was measured in the grass sample (G) after pretreatment at 38.6 ◦C, because ions were dissolved from the grass, which thus increased conductivity. In one of the previous studies [61], it was likewise found that with the temperature increase, the conductivity in vegetable materials increases up to four times. Since the composition of these materials is comparable to grass, similar findings could be expected.

High conductivity values were also noted in the case of a mixture of grass and sludge (G + S), and a combination of grass, sludge and rumen fluid (G + S + R). This is due to the presence of grass, since sludge exhibits smaller conductivity values. In all the samples with rumen fluid (G + R, S +R and G + S + R), conductivity decreases as compared to the samples without rumen fluid (G, S and G + S). On the other hand, the conductivity of untreated rumen fluid is relatively high, owing to dissolved ions. The decrease in conductivity in mixtures with rumen fluid could be connected to the buffering capacity of rumen fluid [62].


4. Application to crack growth prognostics

The code can be modified easily by the users for various applications. In this section, an example of crack growth is used to explain how the MATLAB code BM.m can be modified.

### 4.1 Model definition: crack growth

In addition to the trace of samples shown in Figure 6, it is possible to plot the histogram of model parameters using the same samples. The following commands

When the true degradation model is known, it is possible to compare the predicted degradation with the true one. The following MATLAB script plots the median and confidence intervals of the predicted degradation along with the true

The degradation curves up to 50 weeks are shown in Figure 8 for the battery example. The true degradation with b ¼ 0:012 is shown with the black curve. The red curves show 5, 50 (median) and 95 percentiles of the predicted degradation, which are caused by signiLevel = 5 (line 14). The plot also shows the threshold (green line) and measurement data (blue asterisk marks). Based on the true model, the end of life of the battery is tEOL ¼ 29:72 weeks, and thus, the true RUL should

be tRUL ¼ 20:72 weeks. The prediction shows that the median of RUL is

plot the histograms of all model parameters. figure; % histogram of parameters

for j=1:p;

end

figure;

Figure 8.

20

subplot(1,p,j);

hist(samplResul(j,:),30);

Fault Detection, Diagnosis and Prognosis

degradation and the threshold.

degraTrue=exp(-0.012.\*time);

plot(time,degraTrue,'k'); hold on; plot(time(1:k1),measuData,'\*b'); plot(time(k1:end),degraPI(:,1),'–r'); plot(time(k1:end),degraPI(:,2:3),':r'); plot([0 time(end)],[thres thres],'g');

degraPI=prctile(degraPredi',perceValue)';

xlabel('weeks'); ylabel('Relative C/1 capacity');

Comparison of the predicted degradation with the true degradation.

In fatigue crack growth, the failure criterion is given in terms of the crack size. Therefore, it would be appropriate to use the size of crack as a degradation feature. In this case, the degradation feature monotonically increases, while it was monotonically decreased for the battery example. Assuming that a through-the-thickness center crack exists in an infinite plate under mode-I loading condition, the rate of fatigue crack growth can be expressed using the Paris-Erdogan model as

$$\frac{\text{d}a}{\text{d}N} = \text{C}(\Delta K)^m \tag{16}$$

where a is the half crack size, N is the number of cycles, m and C are model parameters, ΔK ¼ Δσ ffiffiffiffiffi πa p is the range of stress intensity factor, and Δσ is the stress range. It is assumed that time is the number of fatigue loading cycles. For the consistent notation, the crack size and cycles are replaced with ~y ¼ a and t ¼ N in the following explanation. Since the degradation model requires the crack size as a function of time and model parameters, Eq. (16) can be integrated to obtain the following degradation model:

$$\tilde{\mathbf{y}}(t;m,\mathbf{C}) = \left[ t\mathbf{C} \left( \mathbf{1} - \frac{m}{2} \right) \left( \Delta \sigma \sqrt{\pi} \right)^{m} + a\_0^{\mathbf{1} - \frac{m}{2}} \right]^{\frac{2}{2-m}} \tag{17}$$

The system is under fatigue loading with the range of stress being Δσ ¼ 75MPa at each cycle. It is assumed that the health monitoring is performed every 50 cycles to measure the crack size yk until the current time tCUR ¼ 1, 200 cycles, and the initial size of the crack is a0 ¼ 0:01m. Similar to the battery degradation example, the measurement data are simulated by adding random noise to the true crack size. First, the true crack size data are generated at every 50 cycles using Eq. (17) with mtrue ¼ 3:8 and Ctrue ¼ 1:5 � 10�10. The measured crack size data are then generated by adding Gaussian noise ε � N 0; s 2 ð Þ, s ¼ 0:0005m to the true crack sizes. The measured crack size data are used to identify three model parameters, θ ¼ f g m; ln ð Þ C ; s . In the Paris-Erdogan model, the y-intercept C is very small but changes its magnitude by several orders. Therefore, it would be better to identify logarithm of C. For RUL calculation, the critical crack size is determined as 0.043 m.

For the Bayesian method, it is necessary to define the prior distribution and the likelihood function. In the battery example, it was assumed that noise in data follows a normal distribution. However, when the distribution type of measurement noise is unknown, it is possible that the likelihood function might be different from the true noise distribution. The same is true for the prior/initial distribution. Therefore, it would be a good exercise to study the effect of different distribution types by changing the MATLAB codes. In this example, the lognormal distribution is employed for the likelihood function as:

$$f\left(y\_k|\theta\right) = \frac{1}{y\_k \zeta\_k \sqrt{2\pi}} \exp\left[-\frac{1}{2} \left(\frac{\ln y\_k - \eta\_k}{\zeta\_k}\right)^2\right], \quad k = 1, \ldots, N\_{\text{data}}\tag{18}$$

where ζk ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ln 1 þ s=~ykÞ 2 r h � i and ηk ¼ ln ~ykÞ � 0:5 ζk ð Þ2 � are the standard

deviation and mean of a lognormal distribution, respectively. In the above equation, ~ykð Þθ is the model prediction from Eq. (17) at time tk with given model parameters m and C.

Also, the prior distribution of each parameter is assumed as a normal distribution as f mð Þ� N 4; 0:22 � �, fð Þ� lnC N �22:33; 0:52 � �, and f sðÞ� N 5 � 10�4; 1 � 10�4 � �2 � �. Therefore, the joint prior distribution can be obtained from the independence

assumption as

$$f\_{\;\;\theta}(\theta) = f(m) \times f(\ln \mathcal{C}) \times f(\mathfrak{s}) \tag{19}$$

Initially, a fatigue crack grows slowly and then grows rapidly just before becoming unstable. The crack growth model in Eq. (17) is only valid when the crack growth is stable. Normally the threshold ythreshold is set before the crack becomes unstable. When the crack becomes unstable, Eq. (17) yield a complex number. Therefore, the last line of the model definition code identifies if a prediction yields a complex number and converts it to a real number by ignoring the

Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB

In addition to the problem definition part, the posterior distribution part also needs to be modified because instead of a uniform distribution, a normal distribution is used for the prior distribution. Also, lognormal distributions are used instead of normal distributions for the likelihood function. The posterior distribution part

zeta=sqrt(log(1+(s./degraModel(k)).^2)); eta=log(degraModel(k))-0.5\*zeta.^2;

Similar to the battery example, the MATLAB code can be used to plot the trace

of MCMC sampling and the histogram of model parameters and RUL. Using a similar code provided in Section 3.4, the degradation trend could also be plotted. For example, Figure 5 shows the predicted degradation with the true degradation. Even if the median (1553 cycles) has a relatively large error with the true RUL

An important information that was not discussed before is the correlation between model parameters. It is well known that the Paris-Erdogan model parameters, m and C, are strongly correlated [20]. Therefore, it would be beneficial to plot the MCMC samples in the parameter space. The following MATLAB script plots the

[,post(i,j)]=BMappl(para,ParamName,time(1:k1),measuData,prioDisPar);

plot(samplResul(1,:),samplResul(2,:),'.','color',[0.5 0.5 0.5]); hold on

Figure 9 shows the MCMC samples in the parameter space along with the exact value of the parameters (star marker). The figure also shows the contour of the joint posterior PDF based on the grid method. It can be observed that the MCMC samples represent the joint posterior PDF well, and the joint PDF covers the true parameter values. However, due to a strong correlation between the two Paris-Erdogan model parameters, the joint PDF shows a narrow but long tail. Any combination of model parameters along the correlation line may yield a similar

(1709 cycles), the 95% confidence interval covers the true RUL.

[m, C]=meshgrid((3.4:0.009:4.3)',(-24:0.025:-21.5));

prior=prod(normpdf(param,prioDisPar(:,1),prioDisPar(:,2)));

likel=lognpdf(measuData(k),eta,zeta).\*likel;

complex part.

likel=1;

end

4.3 Results

in lines 71–74 needs to be modified as follows:

DOI: http://dx.doi.org/10.5772/intechopen.82781

for k=1:length(measuData)

MCMC samples of the parameters.

for i=1:101; for j=1:101

contour(m,C,real(post));

end; end

damage growth trend.

23

clear; clc; load('Crack at 1200.mat');

plot(3.8,log(1.5E-10),'pk','markersize',14);

para=[m(i,j); C(i,j); 0.0005];

poste=likel.\*prior;

The posterior distribution can be obtained by multiplying the prior distribution in Eq. (19) with the likelihood function in Eq. (18).

#### 4.2 Modifying the code

For the crack growth example, the code in Appendix needs to be changed as follows. First, the problem definition part in lines 2–15 is replaced with the following code:

%===== PROBLEM DEFINITION 1 (Required Parameters)================== WorkName='Crack'; %work results are saved by WorkName TimeUnit='cycles'; % time unit name time=(0:50:3600)'; % time including both at measurement and at prediction measuData=[1.03 1.00 0.96 1.14 1.13 1.10 1.15 1.15 1.19 1.19 1.14 ... 1.14 1.20 1.22 1.37 1.21 1.25 1.25 1.36 1.30 1.32 1.48 ... 1.52 1.47 1.59]'\*0.01; thres=0.043; % threshold - critical value ParamName=['m'; 'C'; 's']; % model parameters' name to be estimated prioDisPar=[4 0.2; -22.33 .5; 5E-4 1E-4]; % parameter prior distributions Ns=10000; % num. of samples for MCMC simulation burnIn=0.2; % burn-in fraction signiLevel=2.5; % significance level for C.I. and P.I. %===============================================================

A total of Ndata ¼ 25 measurement data are provided up to tCUR ¼ 1, 200 cycles, and the degradation is predicted until tend ¼ 3, 600 cycles. Since the prior distributions are assumed a normal distribution, the first column of prioDisPar is the mean, and the second column is the standard deviation.

Next, the model definition part in lines 65–67 is replaced with the following code:

```
%===== PROBLEM DEFINITION 2 (model equation)=======================
a0=0.01; dsig=75; coef=1-m/2;
degraModel=(t.*exp(C).*coef.*(dsig*sqrt(pi)).^m + a0.^coef).^(1./coef);
loca=imag(degraModel)�=0; degraModel(loca)=real(degraModel(loca));
%===============================================================
```
Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB DOI: http://dx.doi.org/10.5772/intechopen.82781

Initially, a fatigue crack grows slowly and then grows rapidly just before becoming unstable. The crack growth model in Eq. (17) is only valid when the crack growth is stable. Normally the threshold ythreshold is set before the crack becomes unstable. When the crack becomes unstable, Eq. (17) yield a complex number. Therefore, the last line of the model definition code identifies if a prediction yields a complex number and converts it to a real number by ignoring the complex part.

In addition to the problem definition part, the posterior distribution part also needs to be modified because instead of a uniform distribution, a normal distribution is used for the prior distribution. Also, lognormal distributions are used instead of normal distributions for the likelihood function. The posterior distribution part in lines 71–74 needs to be modified as follows:

```
prior=prod(normpdf(param,prioDisPar(:,1),prioDisPar(:,2)));
likel=1;
for k=1:length(measuData)
 zeta=sqrt(log(1+(s./degraModel(k)).^2)); eta=log(degraModel(k))-0.5*zeta.^2;
 likel=lognpdf(measuData(k),eta,zeta).*likel;
end
poste=likel.*prior;
```


**4. Injury analysis for the side-facing seated occupant**

The results in Section 3 show that a side facing seated occupant could have high risk of head and chest injuries even at a moderate severity frontal crash. Our other previous study [84] concluded that as a frontal crash severity increased above 40 kph or 25 mph delta velocity, the estimated injury risks for the body regions of head, chest, abdomen, pelvis, and knee-thigh-hip of the side-facing seated occupant increased significantly.

In real-world vehicle crashes, a lateral-facing seated occupant could also be exposed to various impacts other than a frontal crash, such as the oblique, side, and rear vehicle crashes. In this study, we investigated a mid-size male occupant on a 2nd row side-facing seat in a minivan subject to various crash pulses from the current US regulatory vehicle crash tests. The objective was to better understand the body injury risks and restraint protection effectiveness for such a side-facing seated occupant.

The general approach went through the three phases: 1) selected the vehicle crash test cases, collected the test data, and performed the vehicle crash test simulations; 2) performed the occupant simulations and injury analysis; 3) developed new restraint concepts for the occupant protection (the methods and results from this phase will be summarized in Section 5).

### **4.1 Methods**

In this study, the case vehicle was a US minivan with redesigned seating arrangement for a conceptual automated vehicle.

Eight US regulatory vehicle crash tests for the minivan were considered, as listed in **Table 3**, including the US NCAP rigid barrier frontal crash (FC-RB), the IIHS 40% offset deformable barrier frontal crash (FC-ODB), the IIHS small overlap rigid barrier frontal crash (FC-SOB), the US NCAP moving deformable barrier near side crash (NS-DB), the IIHS moving deformable high barrier near side crash (NS-IDB), the US NCAP moving deformable barrier far side crash (FS-DB), the IIHS moving deformable high barrier far side crash (FS-IDB), and the NHTSA rear crash sled test (RC-RL).

The vehicle side crash tests listed in **Table 3** were simulated. The vehicle FE model was originally obtained from the public resource hosted by George Washington University National Crash Analysis Center (NCAC). Further updates were made on the side door structures of the minivan model. Correlation of the maximum side door

#### **Table 3.**

*The US regulatory vehicle crash tests for the minivan investigated in this study.*

structure deformation against the measured data were achieved. The updated vehicle FE model consisted of 572,555 elements, 604,821 nodes and 694 parts. **Figure 8** shows the vehicle crash simulation model setup for the four side crash tests, in which coupled vehicle crash and occupant simulations were performed.

For simulations of the vehicle frontal, oblique and rear crash tests listed in **Table 3**, a simplified vehicle FE model with 148,437 elements, 153,155 nodes and 47 parts was developed from the original NCAC model. The vehicle crash pulses from the frontal and oblique tests were collected and applied to the vehicle model, as shown in **Figure 9**.

For the occupant simulations, the GHBMC M50-OS v1.8.4 model (updated internally) was used to represent a 50th%ile male occupant. **Figure 10** shows the occupant model setup with the interior seating configuration of the minivan. The occupant restrained with a 3-point seatbelt was placed on a concept bench seat (case seat) on the frontal right side in the middle of the vehicle, surrounded by a 1st row right hand side (RHS) seat on his right, a 2nd row rear-facing seat on his front, and the rear-seat on his left.

The case seat model consisted of the cushion, seat pan and seatback. The material models of the cushion foam and the cover fabric were carried over from the validated mechanical properties of a passenger seat. The 1st row RHS seat was represented by a validated production passenger seat FE model as a surrogate.

The occupant seating under gravity was simulated initially. Each simulation run through 150 msec of termination time. The final occupant seating position and posture were then defined along with the seat cushion and seatback geometry profiles from the seating simulation at the time when the occupant achieved his equilibrium seating position.

For each of all the US regulatory vehicle crash tests listed in **Table 3**, the vehicle and occupant simulations were conducted. From the occupant simulation results, we analyzed the occupant kinematic and kinetic response, as well as the injury measures and risks estimated from the injury risk functions summarized in **Table 2**.


**8. Acknowledgments**

The authors are grateful to Dr. Goeuriot D. and Dr. Liébault J. (E.N.S.M.), to Prof. Kortov V.S. (Ural State Technical University) for the supply of some samples. Fruitful discussions with Dr. Bernardini J. (Im2np) and Prof. Fakhfakh Z. (LaMaCoP) were highly appreciated. The first author gratefully acknowledges financial support from the Ministry of Higher Education and Scientific Research of Tunisia and the French Institute of Cooperation.

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**25**

V.P. Ilyina

*Russia*

*Academy of Science*

**Evaluation of Dielectric Properties from**

**Electrical Porcelain Production**

*Establishment the Karelian Centre of Science of the Russian Academy of Sciences Institute of Geology of the Russian*

**the Cakes of Feldspathic Raw Material for**

Feldspars are anhydrous alumosilicates containing alkaline (Na+, К+) and alkaline-earth (Са2+) cations. The basic types of feldspars used in ceramic production are: potassic feldspar (microcline) К2О•А12O3•6SiO2, sodic feldspar (albite) Na2O•Al2O3•6SiO2 and calcium feldspar (anorthite) СаО•А12О3•2SiO2. The dielectrical properties of electrical porcelain depend on the mineralogical composition, alkali ratio and total alkali content of feldspathic

To examine new sources of raw materials for electrical porcelain production, we have evaluated nonconventional types feldspathic rocks of Karelian. Feldspathic rocks such as alkaline and nepheline syenites from the Elisenvaara and Yeletozero deposits, aplite-like granite from the Louhi area and volcanics such as Kostomuksha halleflinta and Roza-Lambi quartz porphyry. On a chemical compound they represent Alumosilikate K, Na, Ca, less often Ва. Form isomorphic numbers, including Plagioclase [3]. In world practice, the above rocks are

Nonconventional types of feldspathic raw materials were evaluated on cakes because the dielectrical and other properties of natural feldspars and their cakes are much the same, while the chemical bonds of natural feldspars persist after their melting and are inherited by

Cakes (material in vitreous state, as in ceramics) were prepared from finely ground (particle size 0.063 mm) powders of deferrized feldspathic rocks by caking them in crucibles at 1350°

We have assessed the electrical properties (dielectrical permeability -ε, dielectrical loss angle tangent -tgδ and electrical resistance -lgρ), the thermal coefficient of linear expansion (ТCLE) and рН of nonconventional types of feldspathic raw materials such as potassic halleflinta, quartz porphyry, syenite and granite-aplite and compared them with those of pegmatite, a common paw material for ceramics production. The compositions and physico-

a common source of mineral products which has some advantages over pegmatite.

the glass phase of the resulting ceramics [1,2].

technical properties of feldspathic rocks are shown in Table 1.

**1. Introduction**

raw material [1].

С for 3 hr.