Обучение по природни науки и върхови технологии

https://doi.org/10.53656/nat2023-3-4.01

2023/3-4, стр. 187 - 200

DEVELOPMENT OF OIL FIELDS USING SCIENCE ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Al-Obaidi S.H.
OrcID: 0000-0003-0377-0855
E-mail: drsudad@gmail.com
Department of Petroleum Engineering
Mining University Russia
Chang W.J.
OrcID: 0000-0002-5457-2923
E-mail: changwj962@gmail.com
Department of Petroleum Engineering
University of Xidian
Xi’an Shaanxi 710126 China
Hofmann M.
OrcID: 0000-0001-5889-5351
E-mail: hof620929@gmail.com
Department of Petroleum Engineering
Mining University Russia

Резюме: Since artificial intelligence has become increasingly prevalent in the oil industry, it is relevant to this study since it is being used for exploration, development, production, field design, and management planning to improve decision-making, reduce costs, and speed up production. For establishing relationships between complex non-linear datasets, machine learning has proved superior to regression methods in petroleum engineering when it comes to highdimensional data prediction errors, processing power, and memory. In this article, machine learning is compared with conventional statistical models of oil and gas engineering for determining and predicting reservoir pressure values in the development of oil fields. The effectiveness and potential of machine learning to determine reservoir pressure values was analysed. Using non-parametric multivariate model that link well performance over time, a new method is proposed for predicting reservoir pressure using machine learning. According to the proposed method, the predicted reservoir pressure correlates well with values measured by hydrodynamic studies of wells based on the dynamics of indicators describing well performance. Machine learning method based on random forest algorithm tends to provide better prediction reliability for reservoir pressure than linear regression method (absolute deviation: 0.86 ; relative deviation: \(6.8 \%\) ).

Ключови думи: Machine learning; Oil fields; Reservoir pressure; Prediction; Nonparametric

1. Introduction

In many parts of the world, hydrocarbon fields are currently in the final stages of development. For these hydrocarbon fields, operational control of development parameters and a comprehensive study of productive formations are required (Yu et al. 2018; Smirnov & Al-Obaidi 2008; McGlade 2012). The reservoir pressure is one of the most important indicators of development, which is determined primarily by hydrodynamic studies of wells (well testing). Accurate reservoir pressure prediction has a wide range of applications in the oil industry, especially in optimizing continuous field production, quantifying reservoir productivity, adjusting oil production costs, and evaluating workovers (Al-Obaidi, Kamensky & Hofmann 2010; N. Chithra et al. 2013; Song, Fuquan et al. 2023). Well-test methods are mainly used in oilfield businesses to determine the energy state of the reservoir in zones of the well drainage, as prescribed by the guidelines. The main disadvantage is the need to stop the well, in some cases for a very long time, which leads to the so-called shortfalls in oil production. Considering the time difference between studies, comparing reservoir pressures across all wells seems impossible because all wells cannot be shut down simultaneously in field conditions (Tan, J. et al. 2021; Romanov & Zolnikova 2008).

In the conditions of modern oil production, an urgent task is the widespread use of digital technologies to solve various problems of oil and gas production (Giovanni, F. 2018; Haouel & Nemeslaki 2023; Al-Obaidi 2016). Their solution complicates the need to take into account the influence of geological and technological indicators on the development of oil and gas fields. As a matter of fact, even well-studied development targets are characterized by a wide range of reservoir parameters and technological indicators, which significantly complicates the use of digital technologies to address urgent production problems (Su, J., Yao, S., & Liu, H. 2022; Li, G. et al. 2019). Thus, it appears appropriate to investigate how probabilistic analysis and machine learning can contribute to solving these problems.

As artificial intelligence becomes more prevalent in the oil industry, it is used for exploration, development, production, field engineering, and management planning to reduce costs and speed up decision-making. Machine learning has gained a lot of popularity in establishing relationships between complex non-linear datasets. This type of machine learning algorithm has demonstrated its superiority over regression methods in petroleum engineering in terms of high-dimensional data prediction errors, processing power, and memory (Daniel Asante Otchere et al. 2021; Wang, J. et al. 2022; Al-Obaidi, Patkin & Guliaeva 2003). This results in faster decision-making, which invariably saves money, time and equipment. For an improved and more accurate reservoir characterization process, which is robust to anticipated or unexpected changes, the level of accuracy must be high (Fernandes, Corchado & Marreiros 2022; Steven, Bernd & Petros 2020).

The use of machine learning methods is becoming increasingly prevalent in many industries, including oil and gas (Tariq, Z. et al. 2021; You, L. et al. 2018; Al-Obaidi & Khalaf 2019; Le Van & Chon 2017). There is a tremendous amount of digital information being processed by oil companies around the world, and the amount of data is growing each year. The quality of their processing and interpretation is the basis for making effective design and management decisions. In this regard, the adaptation of machine learning methods to the oil and gas industry in order to create automated systems for monitoring the parameters of oil field operation has great potential (Saeed, Masoud & Adel 2023; A. Choubineh et al. 2017; Wang, X.L. 2017).

So, for example, some oil and gas companies use machine learning technologies to identify the causes of failures in the operation of electric centrifugal pumps and also identify several priority areas for themselves using these methods – searching for analogue objects, restoring historical operational data, processing research data in real-time, etc. When generating a large amount of technological information, it seems possible to use methods based on the collection, systematization, processing and interpretation of data presented in the form of digital arrays.

An approach to predicting reservoir pressure is discussed in (Galkin, Ponomareva & Martyushev 2020; Al-Obaidi & Khalaf 2023), which utilizes multilevel probabilistic-statistical models. The use of the developed multidimensional mathematical models makes it possible to determine reservoir pressure in any period of wells operation without shutting them down for testing. It should be noted that the presented models should not be considered as an alternative to hydrodynamic studies. Their use is advisable for express assessment of reservoir pressure or when it is impossible to stop the well for testing due to technological reasons.

It appears that this technique can be applied to other hydrocarbon fields not only in Russia or China but around the world, since it is the most reliable and adapted among the ones known. Moreover, taking into account the experience of its application, we are exploring machine learning methods for determining reservoir pressure values in real-time and for the reproduction of the historical work of the well.

2. Methodology and materials

The following types of problems can be solved using machine learning methods:

1. Regression – prediction of a specific number based on an array of features or characteristics (Palmer 2009; Emeke 2019; S. Tou, 1988);

2. Classification – determination of the category of an object of study by the quantity and quality of its signs or characteristics (Pan, Deng & Lee 2020; Valkó & John Lee 2010; Al-Obaidi & Chang 2023);

3. Clustering – combining objects into groups according to a common feature (Anifowosea, Labadina & Abdulraheem 2015; Sancho et al. 2022;

Patel, Kalpesh & Rohit Patwardhan 2019);

4. Dimensionality reduction – compression of the array of object characteristics to a smaller number of features (Sorek et al. 2017; Galkin et al. 2005; Hofmann, Al-Obaidi & Hussein 2022).

As part of the oil and gas field development analysis, these tasks are ubiquitous; they involve controlling the energy state of the development object, through which formation pressure is measured. Since the described approaches have not been used previously to determine formation pressure in oil fields of the studied region, it is important to investigate their applicability and explore future prospects for their development.

2.1 Initial data for reservoir pressure assessment and forecasting

One of the promising oil fields in the studied territory (object Bb) was chosen as the object of study. The initial data for building models were used from three other oil fields (objects Bb) in the studied territory, which are characterized by a significant life cycle of operation and the volume of field information. These fields are well-studied and have a sufficient number of actual reservoir pressure measurements. Basic information about the development of these fields is given in Table 1.

Table 1. Oil fields information used in building initial models

ParameterField123Number ofwells1124868Number of wells tests349212231Initialreservoirpressure, MPa21,223,422,5Currentreservoirpressure, MPa11,19,57,2

2.2. Machine learning models

Explaining machine learning models is always an important research topic (Elkatatny, Tariq & Mahmoud 2016; Salem, Yakoot & Mahmoud 2022; Al-Obaidi 2016). Simple machine learning models like linear regression and decision trees are easy to understand and explain. For linear regression, the contribution of each variable is determined by the sign and value of its coefficient. Decision trees can be interpreted by visualizing the internal nodes and branches. However, complex non-linear machine learning methods such as support vector regression, random forests, and deep neural networks are difficult to understand, even though they always provide higher fidelity than simpler machine learning methods.

Two methods were used to estimate and predict reservoir pressure: multiple linear regression and “random forest regression”. The random forest machine learning method has been widely used in many areas and is great for solving various kinds of problems (Mehran Rahimi & Mohammad Riahi 2022; Liang Xue et al., 2021; Hofmann, M., Al-Obaidi, S. H. & Hussein, K.F. 2022). This machine learning algorithm was first proposed by American mathematicians Leo Breiman and Adele Cutler and is one of the few universal algorithms. Its versatility lies in the fact that it is suitable for solving problems of classification, regression, clustering, searching for anomalies, etc. Basically, a “random regression forest” is a set of decision trees in which, when solving the regression problem, their answers are averaged, which is suitable for calculating the reservoir pressure parameter.

The random forest model is described by the following characteristics:

1. The number of decision trees – the quality of the result depends on this factor, however, with an increase in the number of trees, the setup time and model operation also increases;

2. Maximum decision tree depth – Increasing this factor will improve the quality of the preparing, however, shallow decision trees are recommended when solving problems with heavy noise (outliers);

3. Maximum number of decision tree nodes (width) – Choosing this parameter must take into account the possibility of preparing the model with a small tree depth;

4. The maximum number of features of one decision tree – With an increase in this factor, the time to build a forest increases and the trees become monotonous; for regression problems, it is \(\mathrm{n} / 3\), where n is the number of trees.

These characteristics are adapted to solve the problems of reproducing and predicting formation pressure values.

3. Results and discussion

3.1. Reservoir pressure prediction using machine learning methods

In the first stage, pre-processing and structuring of field data (fluid flow rate; operating factor; bottom-hole pressure; initial reservoir pressure) is necessary. A computer program “Square” has been created to automate the analysis of field data and build mathematical models, the algorithms of which are based on the methods described above.

To verify the reliability of the developed models, historical measurements of reservoir pressure were reproduced using the probabilistic-statistical model of multiple linear regression and the method of machine learning “random regression forest”.

The multiple linear regression equation was obtained by the least squares method and has the following form:

\(P_{r(t)}=0.7548 P_{r(t-1)}+0.0131 \tfrac{\left(Q_{f(t)}-Q_{f(t-1)}\right)}{Q_{f(t)}}+0.207 P_{w f(t)}-0.00001 T+1.2851\)

Where \(P_{\mathrm{r}(t)}\)-predicted reservoir pressure; \(P_{\mathrm{r}(t-1)}\)- reservoir pressure preceding the forecast;

\(\tfrac{\left(Q_{f(t)}-Q_{f(t-1)}\right)}{Q_{f(t)}}\)- fluid rate growth rate (hereinafter \(T q\) ) relative to the previous well test;

\(Q_{\mathrm{f}(t)}\)-fluid flow rate per day (in a monthly average); \(P_{\mathrm{wf}(t)}\)– the current bottom-hole pressure; \(T\)– Well operation time.

Using the p-test to assess the significance of the coefficients of the linear regression equation, the following results were obtained (Table 2).

Table 2. Coefficients of the linear regression method and their significance

Parameterp-criterionFreemember0,000*Pr(t-1)0,000*Tq0,005*Pwf(t)0,000*T0,000*

As a result of calculations, the average absolute deviation of the model on the input data was calculated, which amounted to 0.821 MPa, with R21 MPa, with \(\mathrm{R}^{2}=0.757\).

The following parameters were used to build the random regression forest model:

– The number of trees is 200;

– The maximum depth is 5;

– Three features are the maximum number of features in one tree.

After training the “random forest” model, the coefficients of the significance of the factors were calculated. The significance of a factor in a “random forest” is determined by its cumulative importance for each decision tree, i.e., by the measure of the reduction in Gini heterogeneity (Table 3). The average absolute deviation on the input data of the “random forest” model was 0.812 MPa.

Table 3. The factors of the Random Forest method

FactorSignicance coecientPr(t−1)0,815158Tq0,023523Pwf(t)0,132228T0,029091

For the methods described above, the performance of the models was evaluated using a cross-validation approach. In this approach, the sample is divided into equal parts, then each part is sequentially excluded (deferred sample), and a model is built using the remaining data. The error value of the delayed sample is then checked. As a result of this test, the standard deviation for the linear regression model was \(1.071 \pm 0.14 \mathrm{MPa}\), and for the “random forest” model was \(1.018 \pm 0.17 \mathrm{MPa}\). In case no of the models has previously been “trained” on the input data, these values indicate the stability of the models, which means a good chance of getting a reliable result.

To assess the reliability of the linear regression method and the “random forest” method, the dependences of the actual (1553 measurements) and calculated reservoir pressure measurements were plotted (Figs. 1, 2).

Figure 1. Linear regression correlation between actual and calculated reservoir pressure values

Figure 2. Correlation between actual and calculated reservoir pressures using the “random forest” method

By analyzing the presented graphs, it can be concluded that in both cases, the calculated reservoir pressure parameters have a “dense” distribution with actual measurements, which indicates a good convergence of the results in general. The deviations resulting from using linear regression and “random forest” for the entire sample under study are presented in Table 4.

Table 4. Absolute and relative deviations resulting from the application of linear regression and random forest methods

MethodAbsolutedeviationfromtheactualmeasurement(average),MPaRelativedeviationfromtheactualmeasurement(average),%Linearregression0,876,9Randomforest0,866,8

Thus, it can be noted that the methods of linear regression and “random forest” have an equal minimum deviation of the predicted reservoir pressure values from the actual ones, which indicates the effectiveness and prospects of using these methods.

Given the “heterogeneity” of the sample and the large amount of data, it is necessary to compare the results well by well. For this purpose, graphs were constructed for comparing the results of actual and calculated values of reservoir pressure (Fig. 3 – 5). The choice of wells for demonstrating the obtained data was made in such a way as to reflect the most complete picture of the applicability of the methods used.

Figure 3. Calculated and actual reservoir pressure values for well 176

Figure 4. Calculated and actual reservoir pressure values for well 86

Analyzing the presented graphs of comparison of actual and calculated values of reservoir pressure, we can conclude that both methods show good convergence with historical data when solving the problem of reproducing the “falling” dynamics of the studied parameter. However, in some cases, the random forest method shows better convergence. So, for example, in wells 167 and 86, the general reservoir pressure trend is modelled closer to the fact by this method. Particular attention should be paid to calculating the last reservoir pressure measurement since it is most important in predicting this parameter. It is evident from the high degree of convergence of this point that the mathematical model accurately reflects the current energy state of the wells and the development object. As a result, the random forest method also shows better convergence than linear regression. Nevertheless, none of the studied methods could simulate sharp changes in reservoir pressure for well 56 (Fig. 5). In this regard, it is necessary to refine the methodology for monitoring the energy state of the reservoir, taking into account the experience gained.

Figure 5. Calculated and actual reservoir pressure values for well 56

Generally, both methods have shown good results in reproducing the actual values of the reservoir pressure parameter and can be used by experts to evaluate “outliers” in the received data in order to resolve production issues. As well as additional training on the “random forest” model, other machine learning methods should be evaluated for solving the problem, including expanding the set of factors to more accurately model reservoir pressure.

4. Conclusions

In the oil industry, there has been an accumulation of too much information over the years, so machine learning algorithms capable of handling multivariate and complex data are preferred over empirical correlations and linear regression models. The presented study proposes a new method for reservoir pressure prediction using machine learning, based on a non-parametric multivariate model that links well performance over time. Based on the proposed method, reservoir pressures are predicted by taking into account the dynamics of indicators characterizing well operation. The predicted reservoir pressure has a good correlation with well-test values (\(\mathrm{r}=0.909\) for linear regression & \(\mathrm{r}=0.907\) for random forest). In the study, random forest machine learning provided a better reservoir pressure prediction accuracy than linear regression (absolute deviation: 0.86; relative deviation: 6.8%). In addition, the proposed method avoids the tedious procedure of coefficient calibration compared to methods based on parametric transformations.

Based on the calculated value of reservoir pressure, using machine learning, it is possible to determine the mode of development of the reservoir at the moment, design a system for maintaining reservoir pressure in advance or evaluate its effectiveness, and also reasonably make further rational decisions on the development of oil fields.

REFERENCES

A. CHOUBINEH, et al., 2017. Improved predictions of wellhead choke liquid critical-flow rates: modelling based on hybrid neural network training learning based optimization. Fuel. no. 207, pp. 547 – 560. https://doi.org/10.1016/j. fuel.2017.06.131.

A. SANCHO et al., 2022. Cluster analysis of crude oils with k-means based on their physicochemical properties. Computers & Chemical Engineering. no. 157, 107633, ISSN 0098-1354, https://doi.org/10.1016/j.compchemeng.2021.107633.

AL-OBAIDI, S.H. & KHALAF, F., 2019. Development of traditional water flooding to increase oil recovery. International Journal of Scientific & Technology Research. vol. 8, no. 1, pp. 177 – 181.

AL-OBAIDI, S.H. AND KHALAF, F.H., 2023. A New Approach for Enhancing Oil and Gas Recovery of the Hydrocarbon Fields with Low Permeability Reservoirs. Pet Petro Chem Eng J. vol. 7, no. 2, 000343. https://doi.org/10.23880/ ppej-16000343.

AL-OBAIDI, S.H., 2016. Improve the efficiency of the study of complex reservoirs and hydrocarbon deposits – East Baghdad field. International journal of scientific & technology research. vol. 5, no. 8, pp. 129 – 131.

AL-OBAIDI, S.H., CHANG, W., 2023. Evaluation of the Quantitative Criteria of Triassic Carbonate Rocks Reservoirs. J Geology & Geophysics. 12.1067.

AL-OBAIDI, S.H., PATKIN, A.A., GULIAEVA, N.I., 2003. Advance use for the NMR relaxometry to investigate reservoir rocks. JoPET. vol. 2. no. 3, pp. 45 – 48.

AL-OBAIDI, S. H., 2016. Improve the efficiency of the study of complex reservoirs and hydrocarbon deposits – East Baghdad field. International journal of scientific & technology research. vol. 5, no. 8, pp. 129 – 131.

AL-OBAIDI, S. H., KAMENSKY, I. P. & HOFMANN, M., 2021. Changes in the physical properties of hydrocarbon reservoir as a result of an increase in the effective pressure during the development of the field. EngrXiv. February 18. doi:10.31224/osf.io/hy6pa.

ANIFOWOSEA, F., LABADINA, J., ABDULRAHEEM, A., 2015. Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines. Applied Soft Computing. vol. 26, pp. 483 – 496.

DANIEL ASANTE OTCHERE et al., 2021. Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models. Journal of Petroleum Science and Engineering. no. 200, 108182. ISSN 0920-4105. https://doi.org/10.1016/j.petrol.2020.108182.

ELKATATNY, S., TARIQ, Z. & MAHMOUD, M., 2016. Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box). J. Pet. Sci. Eng. no. 146, pp. 1202 – 1210. http://dx.doi.org/10.1016%2Fj.petrol.2016.08.021.

FERNANDES, M., CORCHADO, J.M. & MARREIROS, G., 2022. Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review. Appl Intell. vol. 52, pp. 14246 – 14280. https://doi.org/10.1007/s10489022-03344-3.

GALKIN, V.I., PONOMAREVA, I.N. & MARTYUSHEV, D.A., 2020. Prediction of reservoir pressure and study of its behavior in the development of oil fields based on the construction of multilevel multidimensional probabilistic-statistical models. Georesursy. vol. 23, no. 3, pp. 73 – 82.

GALKIN, A. et al., 2005. Dependences of reservoir oil properties on surface oil. Jo Pet. Eng. Emerg. vol. 5, no. 9, pp. 74 – 7.

GIOVANNI, F., 2018. The Contribute of Digital Technologies for the Oil and Gas Industry. [pdf] University UCBM, Rome, Italy. Available at: http://www. oil-gasportal.com/wp-content/uploads/2018/06/The-contribute-of-digital-technologies-for-the-oil-and-gas-industry_REV_elvy.pdf.

HAOUEL, C., & NEMESLAKI, A., 2023. Digital Transformation in Oil and Gas Industry: Opportunities and Challenges. Periodica Polytechnica Social and Management Sciences. https://doi.org/10.3311/PPso.20830.

HOFMANN, M., AL-OBAIDI, S. H. & HUSSEIN, K.F., 2022. Modeling and monitoring the development of an oil field under conditions of mass hydraulic fracturing. Trends in Sciences. vol. 19, no. 8, p. 3436. https://doi.org/10.48048/ tis.2022.3436

EMEKE, K. B. C. 2019. A novel model developed for forecasting oilfield production using multivariate linear regression method. Journal of Science and Technology Research. vol. 29, no. 2, pp. 579 – 591.

LE VAN, S. & CHON, B.H., 2017. Evaluating the critical performances of a \(\mathrm{CO}_{2}-\) Enhanced oil recovery process using artificial neural network models. J Pet Sci Eng. no. 157, pp. 207 – 222. https://doi.org/10.1016/j.petrol.2017.07.034.

LI, G. et al., 2019. Optimized Application of Geology-Engineering Integration Data of Unconventional Oil and Gas Reservoirs. China Petroleum Exploration. vol. 24, no. 1, pp. 147 – 152.

LIANG XUE et al., 2021. A data-driven shale gas production forecasting method based on the multi-objective random forest regression. Journal of Petroleum Science and Engineering. no. 196, p. 107801, ISSN 0920-4105. https://doi.org/10.1016/j.petrol.2020.107801.

MCGLADE CE, 2012. A review of the uncertainties in estimates of global oil resources. Energy vol. 47, no. 1, pp. 262 –270.

MEHRAN RAHIMI & MOHAMMAD ALI RIAHI 2022. Reservoir facies classification based on random forest and geostatistics methods in an offshore oilfield. Journal of Applied Geophysics. no. 201, p. 104640, ISSN 0926-9851, https://doi.org/10.1016/j.jappgeo.2022.104640.

N. CHITHRA CHAKRA et al., 2013. An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs). Journal of Petroleum Science and Engineering. no.106, pp. 18 – 33. ISSN 0920-4105. https://doi.org/10.1016/j.petrol.2013.03.004.

PALMER, P. B., 2009. Regression Analysis for Prediction: Understanding the Process. Cardiopulmonary Physical Therapy Journal. vol. 20, no. 3, pp. 23 – 6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845248/

PAN, Y., DENG, L. & LEE, W.J., 2020. A novel data-driven pressure/rate deconvolution algorithm to enhance production data analysis in unconventional reservoirs. Journal of Petroleum Science and Engineering. no. 192, pp. 107332 – 107332.

PATEL, K. & ROHIT, P., 2019. Machine Learning in Oil & Gas Industry: A Novel Application of Clustering for Oilfield Advanced Process Control. SPE Middle East Oil and Gas Show and Conference. https://doi.org/10.2118/194827-MS.

ROMANOV, A. S. & E. F. ZOLNIKOVA, 2008. Gas/Oil Reservoir Pressure Maintenance by Way of Gas Injection. SPE Russian Oil and Gas Technical Conference and Exhibition. https://doi.org/10.2118/117426-MS.

S.K.W. TOU, 1988. Application of dimensional & regression analysis in oil drill bit data. Computers and Geotechnics. vol. 6, no. 1, pp. 49 – 64. https://doi.org/10.1016/0266-352X(88)90055-9.

SAEED BAHALOO, MASOUD MEHRIZADEH & ADEL NAJAFI-MARGHMALEKI 2023. Review of application of artificial intelligence techniques in petroleum operations, Petroleum Research. vol. 8, no. 2, pp. 167 – 182, https://doi.org/10.1016/j.ptlrs.2022.07.002.

SALEM, A.M., YAKOOT, M.S. & MAHMOUD, O., 2022. Addressing Diverse Petroleum Industry Problems Using Machine Learning Techniques: Literary Methodology-Spotlight on Predicting Well Integrity Failures. ACS Omega. vol. 7, no. 3, pp. 2504 – 2519. https://doi.org/10.1021/acsomega.1c05658.

SMIRNOV, V.I. & AL-OBAIDI, SUDAD, H., 2008. Innovative methods of enhanced oil recovery. Oil & Gas Res. vol. 1, no. 1. http://dx.doi.org/10.4172/24720518.1000e10

SONG, FUQUAN et al., 2023. A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network. Energies. vol. 16, no. 6, p. 2904. https://doi.org/10.3390/en16062904.

SOREK, N. et al., 2017. Dimensionality reduction for production optimization using polynomial approximations. Computation Geosciences. no. 21, pp. 247 – 266. https://doi.org/10.1007/s10596-016-9610-3.

STEVEN, L. BRUNTON, BERND, R. NOACK, P. KOUMOUTSAKOS, 2020. Machine Learning for Fluid Mechanics. Annual Review of Fluid Mechanics. vol. 52, no. 1, pp. 477 – 508.

SU, J., YAO, S., & LIU, H., 2022. Data Governance Facilitate Digital Transformation of Oil and Gas Industry. Frontiers in Earth Science. no. 10, p. 861091. https://doi.org/10.3389/feart.2022.861091.

TAN, J. et al., 2021. Analysis of Factors Influencing Shut in Pressure Cone in Offshore Strong Bottom Water Reservoir. Journal of Geoscience and Environment Protection. vol. 9, no. 4, pp. 166 – 175. https://doi.org/10.4236/gep.2021.94010.

TARIQ, Z. et al. 2021. A systematic review of data science and machine learning applications to the oil and gas industry. J Petrol Explor Prod Technol. no. 11, pp. 4339 – 4374. https://doi.org/10.1007/s13202-021-01302-2.

VALKÓ, P., & W. JOHN LEE, 2010. A better way to forecast production from unconventional gas wells. SPE Annual Technical Conference and Exhibition. https://doi.org/10.2118/134231-MS

WANG, X.L., 2017. Application of artificial intelligence in oil andgas industry. Mod Inf Technol. vol. 3, no. 1, pp. 117 – 119.

WANG, J. et al., 2022. A review on extreme learning machine. Multimed Tools Appl. no. 81, pp. 41611 – 41660. https://doi.org/10.1007/s11042-021-11007-7.

YOU, L. et al., 2018. Reconstruction and prediction of capillary pressure curve based on particle swarm optimization-back propagation neural network method. Petroleum. vol. 4, no. 3, pp. 268 – 280. https://doi.org/10.1016/j.petlm.2018.03.004.

KOTENEV, YU. A. et al., 2018. Energy-efficient technology for recovery of oil reserves with gas injection. IOP Conf. Ser.: Earth Environ. Sci. 194 082019.

2025 година
Книжка 4
Книжка 3
ПРАЗНИК НА ХИМИЯТА 2025

Александра Камушева, Златина Златанова

ФАТАЛНИЯТ 13

Гинчо Гичев, Росица Стефанова

ХИМИЯ НА МЕДОВИНАТА

Габриела Иванова, Галя Аралова-Атанасова

Х ИМ ИЯ НА Б АНКНОТИТЕ И МОНЕТИТЕ

Ивайло Борисов, Мая Ганева

АЛУМИНИЙ – „ЩАСТЛИВИЯТ“ 13-И ЕЛЕМЕНТ

Мария Кирилова, Ралица Ранчова

МЕТАЛЪТ НА ВРЕМЕТО

Християна Христова, Мария Стойнова

СЛАДКА ЛИ Е ФРЕНСКАТА ЛУЧЕНА СУПА?

Женя Петрова, Мими Димова

ПАРИТЕ – ИСТОРИЯ И НЕОБХОДИМОСТ

Мария Александрова, Румяна Стойнева

АЛУМИНИЯТ – ОТ ОТПАДЪК ДО РЕСУРС

Стилян Атанасов, Никола Иванов, Галина Кирова

ТАЙНАТА ХИМИЯ НА ШВЕЙЦАРСКИТЕ БАНКНОТИ

Ивайла Николова, Марияна Георгиева

ХИМИЯТА – ДЕТЕКТИВ ИЛИ ПРЕСТЪПНИК?

Алвина Илин, Валентина Ткачова, Петя Петрова

БЕБЕШКИ ШАМПОАН ОТ ЯДЛИВИ СЪСТАВКИ: ФОРМУЛИРАНЕ НА НОВ КОЗМЕТИЧЕН ПРОДУКТ

Хана Крипендорф, 5, Даниел Кунев, 5, Цветелина Стоянова

БЪЛГАРСКОТО ИМЕ НА ДЪЛГОЛЕТИЕТО

Сияна Краишникова, Анелия Иванова

ХИМИЯ НА МОНЕТИТЕ

Кристина Анкова, Сияна Христова, Ростислава Цанева

ХИМИЯ НА ШОКОЛАДА

Камелия Вунчева, Мария-Сара Мандил, Марияна Георгиева

ХИМИЯТА НА ПАРИТЕ

Биляна Куртева, Ралица Ранчова

АЛУМИНИЯТ В КРИОГЕНИКАТА

Даниел Анков, Ива Петкова, Марияна Георгиева

ПРИЛОЖЕНИЕ НА АЛУМИНИЯ ВЪВ ВАКСИНИТЕ

Станислав Милчев, Петя Вълкова

АЛУМИНИЙ: „КРИЛА НА ЧОВЕЧЕСТВОТО – ЛЮБИМЕЦ 13“

Ростислав Стойков, Пепа Георгиева

ХИМИЯТА В ПЧЕЛНИЯ МЕД

Сиана Каракашева, Симона Тричкова, Майя Найденова-Георгиева

ХИМИЯ НА МЛЕЧНИТЕ ПРОДУКТИ

Пламена Боиклиева, 10 клас, Дафинка Юрчиева

ХИМИЯ В МАСЛИНИТЕ

Симона Гочева, Майя Найденова

ХИМИЯ НА ЛЮТОТО

Марта Пенчева, Васка Сотирова

ХИНАП – ИЗСЛЕДВАНЕ НА СЪДЪРЖАНИЕТО НА ВИТАМИН С

Елица Нейкова, Елисавета Григорова, Майя Найденова

ХИМИЯ НA ПAРИТE

Игликa Кoлeвa, Eмилия Ивaнoвa

ВЛИЯНИЕ НА МАРИНАТИТЕ ВЪРХУ МЕСОТО

Емил Мирчев, Галя Петрова

АНАЛИЗ НА ПРИРОДНИ ВОДИ В ОБЩИНА СЛИВЕН

Никола Урумов, Анелия Иванова

ТРИНАДЕСЕТИЯТ ЕЛЕМЕНТ – СПАСИТЕЛ ИЛИ ТИХ РАЗРУШИТЕЛ?

Виктория Дечкова, Никола Велчев, Нели Иванова

Книжка 2
Книжка 1
MATHEMATICAL MODELLING OF THE TRANSMISSION DYNAMICS OF PNEUMONIA AND MENINGITIS COINFECTION WITH VACCINATION

Deborah O. Daniel, Sefiu A. Onitilo, Omolade B. Benjamin, Ayoola A. Olasunkanmi

2024 година
Книжка 5-6
Книжка 3-4
Книжка 1-2
2023 година
Книжка 5-6
ПОДКАСТ – КОГА, АКО НЕ СЕГА?

Христо Чукурлиев

Книжка 3-4
Книжка 2
Книжка 1
2022 година
Книжка 6
METEOROLOGICAL DETERMINANTS OF COVID-19 DISEASE: A LITERATURE REVIEW

Z. Mateeva, E. Batchvarova, Z. Spasova, I. Ivanov, B. Kazakov, S. Matev, A. Simidchiev, A. Kitev

Книжка 5
MATHEMATICAL MODELLING OF THE TRANSMISSION MECHANISM OF PLAMODIUM FALCIPARUM

Onitilo S. A, Usman M. A., Daniel D. O. Odetunde O. S., Ogunwobi Z. O., Hammed F. A., Olubanwo O. O., Ajani A. S., Sanusi A. S., Haruna A. H.

ПОСТАНОВКА ЗА ИЗМЕРВАНЕ СКОРОСТТА НА ЗВУКА ВЪВ ВЪЗДУХ

Станислав Сланев, Хафизе Шабан, Шебнем Шабан, Анета Маринова

Книжка 4
MAGNETIC PROPERTIES

Sofija Blagojević, Lana Vujanović, Andreana Kovačević Ćurić

„TAP, TAP WATER“ QUANTUM TUNNELING DEMONSTRATION

Katarina Borković, Andreana Kovačević Ćurić

Книжка 3
Книжка 2
КОМЕТИТЕ – I ЧАСТ

Пенчо Маркишки

Книжка 1
DISTANCE LEARNING: HOMEMADE COLLOIDAL SILVER

Ana Sofía Covarrubias-Montero, Jorge G. Ibanez

2021 година
Книжка 6
STUDY OF COMPOSITIONS FOR SELECTIVE WATER ISOLATION IN GAS WELLS

Al-Obaidi S.H., Hofmann M., Smirnov V.I., Khalaf F.H., Alwan H.H.

Книжка 5
POTENTIAL APPLICATIONS OF ANTIBACTERIAL COMPOUNDS IN EDIBLE COATING AS FISH PRESERVATIVE

Maulidan Firdaus, Desy Nila Rahmana, Diah Fitri Carolina, Nisrina Rahma Firdausi, Zulfaa Afiifah, Berlian Ayu Rismawati Sugiarto

Книжка 4
Книжка 3
Книжка 2
INVESTIGATION OF 238U, 234U AND 210PO CONTENT IN SELECTED BULGARIAN DRINKING WATER

Bozhidar Slavchev, Elena Geleva, Blagorodka Veleva, Hristo Protohristov, Lyuben Dobrev, Desislava Dimitrova, Vladimir Bashev, Dimitar Tonev

Книжка 1
DEMONSTRATION OF DAMPED ELECTRICAL OSCILLATIONS

Elena Grebenakova, Stojan Manolev

2020 година
Книжка 6
ДОЦ. Д-Р МАРЧЕЛ КОСТОВ КОСТОВ ЖИВОТ И ТВОРЧЕСТВО

Здравка Костова, Елена Георгиева

Книжка 5
Книжка 4
JACOB’S LADDER FOR THE PHYSICS CLASSROOM

Kristijan Shishkoski, Vera Zoroska

КАЛЦИЙ, ФОСФОР И ДРУГИ ФАКТОРИ ЗА КОСТНО ЗДРАВЕ

Радка Томова, Светла Асенова, Павлина Косева

Книжка 3
MATHEMATICAL MODELING OF 2019 NOVEL CORONAVIRUS (2019 – NCOV) PANDEMIC IN NIGERIA

Sefiu A. Onitilo, Mustapha A. Usman, Olutunde S. Odetunde, Fatai A. Hammed, Zacheous O. Ogunwobi, Hammed A. Haruna, Deborah O. Daniel

Книжка 2

Книжка 1
WATER PURIFICATION WITH LASER RADIATION

Lyubomir Lazov, Hristina Deneva, Galina Gencheva

2019 година
Книжка 6
LASER MICRO-PERFORATION AND FIELDS OF APPLICATION

Hristina Deneva, Lyubomir Lazov, Edmunds Teirumnieks

ПРОЦЕСЪТ ДИФУЗИЯ – ОСНОВА НА ДИАЛИЗАТА

Берна Сабит, Джемиле Дервиш, Мая Никова, Йорданка Енева

IN VITRO EVALUATION OF THE ANTIOXIDANT PROPERTIES OF OLIVE LEAF EXTRACTS – CAPSULES VERSUS POWDER

Hugo Saint-James, Gergana Bekova, Zhanina Guberkova, Nadya Hristova-Avakumova, Liliya Atanasova, Svobodan Alexandrov, Trayko Traykov, Vera Hadjimitova

Бележки върху нормативното осигуряване на оценяването в процеса

БЕЛЕЖКИ ВЪРХУ НОРМАТИВНОТО ОСИГУРЯВАНЕ, НА ОЦЕНЯВАНЕТО В ПРОЦЕСА НА ОБУЧЕНИЕТО

ТЕХНОЛОГИЯ

Б. В. Тошев

Книжка 5
ON THE GENETIC TIES BETWEEN EUROPEAN NATIONS

Jordan Tabov, Nevena Sabeva-Koleva, Georgi Gachev

Иван Странски – майсторът на кристалния растеж [Ivan Stranski

ИВАН СТРАНСКИ – МАЙСТОРЪТ, НА КРИСТАЛНИЯ РАСТЕЖ

Книжка 4

CHEMOMETRIC ANALYSIS OF SCHOOL LIFE IN VARNA

Radka Tomova, Petinka Galcheva, Ivajlo Trajkov, Antoaneta Hineva, Stela Grigorova, Rumyana Slavova, Miglena Slavova

ЦИКЛИТЕ НА КРЕБС

Ивелин Кулев

Книжка 3
ПРИНЦИПИТЕ НА КАРИЕРНОТО РАЗВИТИЕ НА МЛАДИЯ УЧЕН

И. Панчева, М. Недялкова, С. Кирилова, П. Петков, В. Симеонов

UTILISATION OF THE STATIC EVANS METHOD TO MEASURE MAGNETIC SUSCEPTIBILITIES OF TRANSITION METAL ACETYLACETONATE COMPLEXES AS PART OF AN UNDERGRADUATE INORGANIC LABORATORY CLASS

Anton Dobzhenetskiy, Callum A. Gater, Alexander T. M. Wilcock, Stuart K. Langley, Rachel M. Brignall, David C. Williamson, Ryan E. Mewis

THE 100

Maria Atanassova, Radoslav Angelov

A TALE OF SEVEN SCIENTISTS

Scerri, E.R. (2016). A Tale of Seven Scientists and a New Philosophy of Science.

Книжка 2
DEVELOPMENT OF A LESSON PLAN ON THE TEACHING OF MODULE “WATER CONDUCTIVITY”

A. Thysiadou, S. Christoforidis, P. Giannakoudakis

AMPEROMETRIC NITRIC OXIDE SENSOR BASED ON MWCNT CHROMIUM(III) OXIDE NANOCOMPOSITE

Arsim Maloku, Epir Qeriqi, Liridon S. Berisha, Ilir Mazreku, Tahir Arbneshi, Kurt Kalcher

THE EFFECT OF AGING TIME ON Mg/Al HYDROTALCITES STRUCTURES

Eddy Heraldy, Triyono, Sri Juari Santosa, Karna Wijaya, Shogo Shimazu

Книжка 1
A CONTENT ANALYSIS OF THE RESULTS FROM THE STATE MATRICULATION EXAMINATION IN MATHEMATICS

Elena Karashtranova, Nikolay Karashtranov, Vladimir Vladimirov

SOME CONCEPTS FROM PROBABILITY AND STATISTICS AND OPPORTUNITIES TO INTEGRATE THEM IN TEACHING NATURAL SCIENCES

Elena Karashtranova, Nikolay Karashtranov, Nadezhda Borisova, Dafina Kostadinova

45. МЕЖДУНАРОДНА ОЛИМПИАДА ПО ХИМИЯ

Донка Ташева, Пенка Василева

2018 година
Книжка 6

ЗДРАВЕ И ОКОЛНА СРЕДА

Кадрие Шукри, Светлана Великова, Едис Мехмед

РОБОТИКА ЗА НАЧИНАЕЩИ ЕНТУСИАСТИ

Даниела Узунова, Борис Велковски, Илко Симеонов, Владислав Шабански, Димитър Колев

DESIGN AND DOCKING STUDIES OF HIS-LEU ANALOGUES AS POTENTIOAL ACE INHIBITORS

Rumen Georgiev, , Tatyana Dzimbova, Atanas Chapkanov

X-RAY DIFFRACTION STUDY OF M 2 Zn(TeО3)2 (M - Na, K) ТELLURIDE

Kenzhebek T. Rustembekov, Mitko Stoev, Aitolkyn A. Toibek

CALIBRATION OF GC/MS METHOD FOR DETERMINATION OF PHTHALATES

N. Dineva, I. Givechev, D. Tanev, D. Danalev

ELECTROSYNTHESIS OF CADMIUM SELENIDE NANOPARTICLES WITH SIMULTANEOUS EXTRACTION INTO P-XYLENE

S. S. Fomanyuk, V. O. Smilyk, G. Y. Kolbasov, I. A. Rusetskyi, T. A. Mirnaya

БИОЛОГИЧЕН АСПЕКТ НА РЕКАНАЛИЗАЦИЯ С ВЕНОЗНА ТРОМБОЛИЗА

Мариела Филипова, Даниела Попова, Стоян Везенков

CHEMISTRY: BULGARIAN JOURNAL OF SCIENCE EDUCATION ПРИРОДНИТЕ НАУКИ В ОБРАЗОВАНИЕТО VOLUME 27 / ГОДИНА XXVII, 2018 ГОДИШНО СЪДЪРЖАНИЕ СТРАНИЦИ / PAGES КНИЖКА 1 / NUMBER 1: 1 – 152 КНИЖКА 2 / NUMBER 2: 153 – 312 КНИЖКА 3 / NUMBER 3: 313 – 472 КНИЖКА 4 / NUMBER 4: 473 – 632 КНИЖКА 5 / NUMBER 5: 633 – 792 КНИЖКА 6 / NUMBER 6: 793 – 952 КНИЖКА 1 / NUMBER 1: 1 – 152 КНИЖКА 2 / NUMBER 2: 153 – 312 КНИЖКА

(South Africa), A. Ali, M. Bashir (Pakistan) 266 – 278: j-j Coupled Atomic Terms for Nonequivalent Electrons of (n-1)fx and nd1 Configurations and Correlation with L-S Terms / P. L. Meena (India) 760 – 770: Methyl, тhe Smallest Alkyl Group with Stunning Effects / S. Moulay 771 – 776: The Fourth State of Matter / R. Tsekov

Книжка 5
ИМОБИЛИЗИРАНЕНАФРУКТОЗИЛТРАНСФЕРАЗА ВЪРХУКОМПОЗИТНИФИЛМИОТПОЛИМЛЕЧНА КИСЕЛИНА, КСАНТАН И ХИТОЗАН

Илия Илиев, Тонка Василева, Веселин Биволарски, Ася Виранева, Иван Бодуров, Мария Марудова, Теменужка Йовчева

ELECTRICAL IMPEDANCE SPECTROSCOPY OF GRAPHENE-E7 LIQUID-CRYSTAL NANOCOMPOSITE

Todor Vlakhov, Yordan Marinov, Georgi. Hadjichristov, Alexander Petrov

ON THE POSSIBILITY TO ANALYZE AMBIENT NOISERECORDED BYAMOBILEDEVICETHROUGH THE H/V SPECTRAL RATIO TECHNIQUE

Dragomir Gospodinov, Delko Zlatanski, Boyko Ranguelov, Alexander Kandilarov

RHEOLOGICAL PROPERTIES OF BATTER FOR GLUTEN FREE BREAD

G. Zsivanovits, D. Iserliyska, M. Momchilova, M. Marudova

ПОЛУЧАВАНЕ НА ПОЛИЕЛЕКТРОЛИТНИ КОМПЛЕКСИ ОТ ХИТОЗАН И КАЗЕИН

Антоанета Маринова, Теменужка Йовчева, Ася Виранева, Иван Бодуров, Мария Марудова

CHEMILUMINESCENT AND PHOTOMETRIC DETERMINATION OF THE ANTIOXIDANT ACTIVITY OF COCOON EXTRACTS

Y. Evtimova, V. Mihailova, L. A. Atanasova, N. G. Hristova-Avakumova, M. V. Panayotov, V. A. Hadjimitova

ИЗСЛЕДОВАТЕЛСКИ ПРАКТИКУМ

Ивелина Димитрова, Гошо Гоев, Савина Георгиева, Цвета Цанова, Любомира Иванова, Борислав Георгиев

Книжка 4
PARAMETRIC INTERACTION OF OPTICAL PULSES IN NONLINEAR ISOTROPIC MEDIUM

A. Dakova, V. Slavchev, D. Dakova, L. Kovachev

ДЕЙСТВИЕ НА ГАМА-ЛЪЧИТЕ ВЪРХУ ДЕЗОКСИРИБОНУКЛЕИНОВАТА КИСЕЛИНА

Мирела Вачева, Хари Стефанов, Йоана Гвоздейкова, Йорданка Енева

RADIATION PROTECTION

Natasha Ivanova, Bistra Manusheva

СТАБИЛНОСТ НА ЕМУЛСИИ ОТ ТИПА МАСЛО/ ВОДА С КОНЮГИРАНА ЛИНОЛОВА КИСЕЛИНА

И. Милкова-Томова, Д. Бухалова, К. Николова, Й. Алексиева, И. Минчев, Г. Рунтолев

THE EFFECT OF EXTRA VIRGIN OLIVE OIL ON THE HUMAN BODY AND QUALITY CONTROL BY USING OPTICAL METHODS

Carsten Tottmann, Valentin Hedderich, Poli Radusheva, Krastena Nikolova

ИНФРАЧЕРВЕНА ТЕРМОГРАФИЯ ЗА ДИАГНОСТИКА НА ФОКАЛНА ИНФЕКЦИЯ

Рая Грозданова-Узунова, Тодор Узунов, Пепа Узунова

ЕЛЕКТРИЧНИ СВОЙСТВА НА КОМПОЗИТНИ ФИЛМИ ОТ ПОЛИМЛЕЧНА КИСЕЛИНА

Ася Виранева, Иван Бодуров, Теменужка Йовчева

Книжка 3
ТРИ ИДЕИ ЗА ЕФЕКТИВНО ОБУЧЕНИЕ

Гергана Карафезиева

МАГИЯТА НА ТВОРЧЕСТВОТО КАТО ПЪТ НА ЕСТЕСТВЕНО УЧЕНЕ В УЧЕБНИЯ ПРОЦЕС

Гергана Добрева, Жаклин Жекова, Михаела Чонос

ОБУЧЕНИЕ ПО ПРИРОДНИ НАУКИ ЧРЕЗ МИСЛОВНИ КАРТИ

Виолета Стоянова, Павлина Георгиева

ИГРА НА ДОМИНО В ЧАС ПО ФИЗИКА

Росица Кичукова, Ценка Маринова

ПРОБЛЕМИ ПРИ ОБУЧЕНИЕТО ПО ФИЗИКА ВЪВ ВВМУ „Н. Й. ВАПЦАРОВ“

А. Христова, Г. Вангелов, И. Ташев, М. Димидов

ИЗГРАЖДАНЕ НА СИСТЕМА ОТ УЧЕБНИ ИНТЕРНЕТ РЕСУРСИ ПО ФИЗИКА И ОЦЕНКА НА ДИДАКТИЧЕСКАТА ИМ СТОЙНОСТ

Желязка Райкова, Георги Вулджев, Наталия Монева, Нели Комсалова, Айше Наби

ИНОВАЦИИ В БОРБАТА С ТУМОРНИ ОБРАЗУВАНИЯ – ЛЕЧЕНИЕ ЧРЕЗ БРАХИТЕРАПИЯ

Георги Върбанов, Радостин Михайлов, Деница Симеонова, Йорданка Енева

NATURAL RADIONUCLIDES IN DRINKING WATER

Natasha Ivanova, Bistra Manusheva

Книжка 2

АДАПТИРАНЕ НА ОБРАЗОВАНИЕТО ДНЕС ЗА УТРЕШНИЯ ДЕН

И. Панчева, М. Недялкова, П. Петков, Х. Александров, В. Симеонов

STRUCTURAL ELUCIDATION OF UNKNOWNS: A SPECTROSCOPIC INVESTIGATION WITH AN EMPHASIS ON 1D AND 2D 1H NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY

Vittorio Caprio, Andrew S. McLachlan, Oliver B. Sutcliffe, David C. Williamson, Ryan E. Mewis

j-j Coupled Atomic Terms for Nonequivalent Electrons of (n-1)f

j-jCOUPLEDATOMICTERMSFORNONEQUIVALENT, ELECTRONS OF (n-f X nd CONFIGURATIONS AND, CORRELATION WITH L-S TERMS

INTEGRATED ENGINEERING EDUCATION: THE ROLE OF ANALYSIS OF STUDENTS’ NEEDS

Veselina Kolarski, Dancho Danalev, Senia Terzieva

Книжка 1
ZAGREB CONNECTION INDICES OF TiO2 NANOTUBES

Sohaib Khalid, Johan Kok, Akbar Ali, Mohsin Bashir

SYNTHESIS OF NEW 3-[(CHROMEN-3-YL)ETHYLIDENEAMINO]-PHENYL]-THIAZOLIDIN-4ONES AND THEIR ANTIBACTERIAL ACTIVITY

Ramiz Hoti, Naser Troni, Hamit Ismaili, Malesore Pllana, Musaj Pacarizi, Veprim Thaçi, Gjyle Mulliqi-Osmani

2017 година
Книжка 6
GEOECOLOGICAL ANALYSIS OF INDUSTRIAL CITIES: ON THE EXAMPLE OF AKTOBE AGGLOMERATION

Zharas Berdenov, Erbolat Mendibaev, Talgat Salihov, Kazhmurat Akhmedenov, Gulshat Ataeva

TECHNOGENESIS OF GEOECOLOGICAL SYSTEMS OF NORTHEN KAZAKHSTAN: PROGRESS, DEVELOPMENT AND EVOLUTION

Kulchichan Dzhanaleyeva, Gulnur Mazhitova, Altyn Zhanguzhina, Zharas Berdenov, Tursynkul Bazarbayeva, Emin Atasoy

СПИСАНИЕ ПРОСВѢТА

Списание „Просвета“ е орган на Просветния съюз в България. Списанието е излизало всеки месец без юли и август. Годишният том съдържа 1280 стра- ници. Списанието се издава от комитет, а главен редактор от 1935 до 1943 г. е проф. Петър Мутафчиев, историк византолог и специалист по средновеков-

Книжка 5
47-А НАЦИОНАЛНА КОНФЕРЕНЦИЯ НА УЧИТЕЛИТЕ ПО ХИМИЯ

В последните години тези традиционни за българското учителство конфе- ренции се организират от Българското дружество по химическо образование и история и философия на химията. То е асоцииран член на Съюза на химици- те в България, който пък е член на Европейската асоциация на химическите и

JOURNALS OF INTEREST: A REVIEW (2016)

BULGARIAN JOURNAL OF SCIENCE AND EDUCATION POLICY ISSN 1313-1958 (print) ISSN 1313-9118 (online) http://bjsep.org

INVESTIGATING THE ABILITY OF 8

Marina Stojanovska, Vladimir M. Petruševski

SYNTHESIS OF TiO -M (Cd, Co, Mn)

Candra Purnawan, Sayekti Wahyuningsih, Dwita Nur Aisyah

EFFECT OF DIFFERENT CADMIUM CONCENTRATION ON SOME BIOCHEMICAL PARAMETERS IN ‘ISA BROWN’ HYBRID CHICKEN

Imer Haziri, Adem Rama, Fatgzim Latifi, Dorjana Beqiraj-Kalamishi, Ibrahim Mehmeti, Arben Haziri

PHYTOCHEMICAL AND IN VITRO ANTIOXIDANT STUDIES OF PRIMULA VERIS (L.) GROWING WILD IN KOSOVO

Ibrahim Rudhani, Florentina Raci, Hamide Ibrahimi, Arben Mehmeti, Ariana Kameri, Fatmir Faiku, Majlinda Daci, Sevdije Govori, Arben Haziri

ПЕДАГОГИЧЕСКА ПОЕМА

Преди година-две заедно с директора на Националното издателство „Аз- буки“ д-р Надя Кантарева-Барух посетихме няколко училища в Родопите. В едно от тях ни посрещнаха в голямата учителска стая. По стените ѝ имаше големи портрети на видни педагози, а под тях – художествено написани умни мисли, които те по някакъв повод са казали. На централно място бе портретът на Антон Семьонович Макаренко (1888 – 1939). Попитах учителките кой е Макаренко – те посрещнаха въпроса ми с мълчание. А някога, в г

Книжка 4
„СИМВОЛНИЯТ КАПИТАЛ“ НА БЪЛГАРСКОТО УЧИЛИЩЕ

Николай Цанков, Веска Гювийска

KINETICS OF PHOTO-ELECTRO-ASSISTED DEGRADATION OF REMAZOL RED 5B

Fitria Rahmawati, Tri Martini, Nina Iswati

ALLELOPATHIC AND IN VITRO ANTICANCER ACTIVITY OF STEVIA AND CHIA

Asya Dragoeva, Vanya Koleva, Zheni Stoyanova, Eli Zayova, Selime Ali

NOVEL HETEROARYLAMINO-CHROMEN-2-ONES AND THEIR ANTIBACTERIAL ACTIVITY

Ramiz Hoti, Naser Troni, Hamit Ismaili, Gjyle Mulliqi-Osmani, Veprim Thaçi

Книжка 3
Quantum Connement of Mobile Na+ Ions in Sodium Silicate Glassy

QUANTUM CONFINEMENT OF MOBILE Na + IONS, IN SODIUM SILICATE GLASSY NANOPARTICLES

OPTIMIZATION OF ENGINE OIL FORMULATION USING RESPONSE SURFACE METHODOLOGY AND GENETIC ALGORITHM: A COMPARATIVE STUDY

Behnaz Azmoon, Abolfazl Semnani, Ramin Jaberzadeh Ansari, Hamid Shakoori Langeroodi, Mahboube Shirani, Shima Ghanavati Nasab

EVALUATION OF ANTIBACTERIAL ACTIVITY OF DIFFERENT SOLVENT EXTRACTS OF TEUCRIUM CHAMAEDRYS (L.) GROWING WILD IN KOSOVO

Arben Haziri, Fatmir Faiku, Roze Berisha, Ibrahim Mehmeti, Sevdije Govori, Imer Haziri

Книжка 2
COMPUTER SIMULATORS: APPLICATION FOR GRADUATES’ADAPTATION AT OIL AND GAS REFINERIES

Irena O. Dolganova, Igor M. Dolganov, Kseniya A. Vasyuchka

SYNTHESIS OF NEW [(3-NITRO-2-OXO-2H-CHROMEN4-YLAMINO)-PHENYL]-PHENYL-TRIAZOLIDIN-4-ONES AND THEIR ANTIBACTERIAL ACTIVITY

Ramiz Hoti, Hamit Ismaili, Idriz Vehapi, Naser Troni, Gjyle Mulliqi-Osmani, Veprim Thaçi

STABILITY OF RJ-5 FUEL

Lemi Türker, Serhat Variş

A STUDY OF BEGLIKTASH MEGALITHIC COMPLEX

Diana Kjurkchieva, Evgeni Stoykov, Sabin Ivanov, Borislav Borisov, Hristo Hristov, Pencho Kyurkchiev, Dimitar Vladev, Irina Ivanova

Книжка 1
2016 година
Книжка 6
THE EFFECT OF KOH AND KCL ADDITION TO THE DESTILATION OF ETHANOL-WATER MIXTURE

Khoirina Dwi Nugrahaningtyas, Fitria Rahmawati, Avrina Kumalasari

Книжка 5

ОЦЕНЯВАНЕ ЛИЧНОСТТА НА УЧЕНИКА

Министерството на народното просвещение е направило допълне- ния към Правилника за гимназиите (ДВ, бр. 242 от 30 октомври 1941 г.), според които в бъдеще ще се оценяват следните прояви на учениците: (1) трудолюбие; (2) ред, точност и изпълнителност; (3) благовъзпитаност; (4) народностни прояви. Трудолюбието ще се оценява с бележките „образцово“, „добро“, „незадо- волително“. С „образцово“ ще се оценяват учениците, които с любов и по- стоянство извършват всяка възложена им ил

Книжка 4
VOLTAMMERIC SENSOR FOR NITROPHENOLS BASED ON SCREEN-PRINTED ELECTRODE MODIFIED WITH REDUCED GRAPHENE OXIDE

Arsim Maloku, Liridon S. Berisha, Granit Jashari, Eduard Andoni, Tahir Arbneshi

Книжка 3
ИЗСЛЕДВАНЕ НА ПРОФЕСИОНАЛНО-ПЕДАГОГИЧЕСКАТА РЕФЛЕКСИЯ НА УЧИТЕЛЯ ПО БИОЛОГИЯ (ЧАСТ ВТОРА)

Надежда Райчева, Иса Хаджиали, Наташа Цанова, Виктория Нечева

EXISTING NATURE OF SCIENCE TEACHING OF A THAI IN-SERVICE BIOLOGY TEACHER

Wimol Sumranwanich, Sitthipon Art-in, Panee Maneechom, Chokchai Yuenyong

NUTRIENT COMPOSITION OF CUCURBITA MELO GROWING IN KOSOVO

Fatmir Faiku, Arben Haziri, Fatbardh Gashi, Naser Troni

НАГРАДИТЕ „ЗЛАТНА ДЕТЕЛИНА“ ЗА 2016 Г.

На 8 март 2016 г. в голямата зала на Националния политехнически музей в София фондация „Вигория“ връчи годишните си награди – почетен плакет „Златна детелина“. Тази награда се дава за цялостна професионална и творче- ска изява на личности с особени заслуги към обществото в трите направления на фондация „Вигория“ – образование, екология, култура. Наградата цели да се даде израз на признателност за високи постижения на личности, които на професионално равнище и на доброволни начала са рабо

Книжка 2
СТО ГОДИНИ ОТ РОЖДЕНИЕТО НА ПРОФЕСОР ХРИСТО ИВАНОВ (1916 – 2004)

СТО ГОДИНИ ОТ РОЖДЕНИЕТО, НА ПРОФЕСОР ХРИСТО ИВАНОВ, (96 – 00

CONTEXT-BASED CHEMISTRY LAB WORK WITH THE USE OF COMPUTER-ASSISTED LEARNING SYSTEM

N. Y. Stozhko, A. V. Tchernysheva, E.M. Podshivalova, B.I. Bortnik

Книжка 1
ПО ПЪТЯ

Б. В. Тошев

INTERDISCIPLINARY PROJECT FOR ENHANCING STUDENTS’ INTEREST IN CHEMISTRY

Stela Georgieva, Petar Todorov , Zlatina Genova, Petia Peneva

2015 година
Книжка 6
COMPLEX SYSTEMS FOR DRUG TRANSPORT ACROSS CELL MEMBRANES

Nikoleta Ivanova, Yana Tsoneva, Nina Ilkova, Anela Ivanova

SURFACE FUNCTIONALIZATION OF SILICA SOL-GEL MICROPARTICLES WITH EUROPIUM COMPLEXES

Nina Danchova , Gulay Ahmed , Michael Bredol , Stoyan Gutzov

INTERFACIAL REORGANIZATION OF MOLECULAR ASSEMBLIES USED AS DRUG DELIVERY SYSTEMS

I. Panaiotov, Tz. Ivanova, K. Balashev, N. Grozev, I. Minkov, K. Mircheva

KINETICS OF THE OSMOTIC PROCESS AND THE POLARIZATION EFFECT

Boryan P. Radoev, Ivan L. Minkov, Emil D. Manev

WETTING BEHAVIOR OF A NATURAL AND A SYNTHETIC THERAPEUTIC PULMONARY SURFACTANTS

Lidia Alexandrova, Michail Nedyalkov, Dimo Platikanov

Книжка 5
TEACHER’S ACCEPTANCE OF STUDENTS WITH DISABILITY

Daniela Dimitrova-Radojchikj, Natasha Chichevska-Jovanova

IRANIAN UNIVERSITY STUDENTS’ PERCEPTION OF CHEMISTRY LABORATORY ENVIRONMENTS

Zahra Eskandari, Nabi.A Ebrahimi Young Researchers & Elite Club, Arsanjan Branch,

APPLICATION OF LASER INDUCED BREAKDOWN SPECTROSCOPY AS NONDESDUCTRIVE AND SAFE ANALYSIS METHOD FOR COMPOSITE SOLID PROPELLANTS

Amir Hossein Farhadian, Masoud Kavosh Tehrani, Mohammad Hossein Keshavarz, Seyyed Mohamad Reza Darbany, Mehran Karimi, Amir Hossein Rezayi Optics & Laser Science and Technology Research Center,

THE EFFECT OF DIOCTYLPHTHALATE ON INITIAL PROPERTIES AND FIELD PERFORMANCE OF SOME SEMISYNTHETIC ENGINE OILS

Azadeh Ghasemizadeh, Abolfazl Semnani, Hamid Shakoori Langeroodi, Alireza Nezamzade Ejhieh

QUALITY ASSESSMENT OF RIVER’S WATER OF LUMBARDHI PEJA (KOSOVO)

Fatmir Faiku, Arben Haziri, Fatbardh Gashi, Naser Troni

Книжка 4
БЛАГОДАРЯ ВИ!

Александър Панайотов

ТЕМАТА ВЪГЛЕХИДРАТИ В ПРОГРАМИТЕ ПО ХИМИЯ И БИОЛОГИЯ

Радка Томова, Елена Бояджиева, Миглена Славова , Мариан Николов

BILINGUAL COURSE IN BIOTECHNOLOGY: INTERDISCIPLINARY MODEL

V. Kolarski, D. Marinkova, R. Raykova, D. Danalev, S. Terzieva

ХИМИЧНИЯТ ОПИТ – НАУКА И ЗАБАВА

Елица Чорбаджийска, Величка Димитрова, Магдалена Шекерлийска, Галина Бальова, Методийка Ангелова

ЕКОЛОГИЯТА В БЪЛГАРИЯ

Здравка Костова

Книжка 3
SYNTHESIS OF FLUORINATED HYDROXYCINNAMOYL DERIVATIVES OF ANTI-INFLUENZA DRUGS AND THEIR BIOLOGICAL ACTIVITY

Boyka Stoykova, Maya Chochkova, Galya Ivanova, Luchia Mukova, Nadya Nikolova, Lubomira Nikolaeva-Glomb, Pavel Vojtíšek, Tsenka Milkova, Martin Štícha, David Havlíček

SYNTHESIS AND ANTIVIRAL ACTIVITY OF SOME AMINO ACIDS DERIVATIVES OF INFLUENZA VIRUS DRUGS

Radoslav Chayrov, Vesela Veselinova, Vasilka Markova, Luchia Mukova, Angel Galabov, Ivanka Stankova

NEW DERIVATIVES OF OSELTAMIVIR WITH BILE ACIDS

Kiril Chuchkov, Silvia Nakova, Lucia Mukova, Angel Galabov, Ivanka Stankova

MONOHYDROXY FLAVONES. PART III: THE MULLIKEN ANALYSIS

Maria Vakarelska-Popovska, Zhivko Velkov

LEU-ARG ANALOGUES: SYNTHESIS, IR CHARACTERIZATION AND DOCKING STUDIES

Tatyana Dzimbova, Atanas Chapkanov, Tamara Pajpanova

MODIFIED QUECHERS METHOD FOR DETERMINATION OF METHOMYL, ALDICARB, CARBOFURAN AND PROPOXUR IN LIVER

I. Stoykova, T. Yankovska-Stefenova, L.Yotova, D. Danalev Bulgarian Food Safety Agency, Sofi a, Bulgaria

LACTOBACILLUS PLANTARUM AC 11S AS A BIOCATALYST IN MICROBIAL ELECYTOLYSIS CELL

Elitsa Chorbadzhiyska, Yolina Hubenova, Sophia Yankova, Dragomir Yankov, Mario Mitov

STUDYING THE PROCESS OF DEPOSITION OF ANTIMONY WITH CALCIUM CARBONATE

K. B. Omarov, Z. B. Absat, S. K. Aldabergenova, A. B. Siyazova, N. J. Rakhimzhanova, Z. B. Sagindykova

Книжка 2
TEACHING CHEMISTRY AT TECHNICAL UNIVERSITY

Lilyana Nacheva-Skopalik, Milena Koleva

ФОРМИРАЩО ОЦЕНЯВАНЕ PEER INSTRUCTION С ПОМОЩТА НА PLICКERS ТЕХНОЛОГИЯТА

Ивелина Коцева, Мая Гайдарова, Галина Ненчева

VAPOR PRESSURES OF 1-BUTANOL OVER WIDE RANGE OF THEMPERATURES

Javid Safarov, Bahruz Ahmadov, Saleh Mirzayev, Astan Shahverdiyev, Egon Hassel

Книжка 1
РУМЕН ЛЮБОМИРОВ ДОЙЧЕВ (1938 – 1999)

Огнян Димитров, Здравка Костова

NAMING OF CHEMICAL ELEMENTS

Maria Atanassova

НАЙДЕН НАЙДЕНОВ, 1929 – 2014 СПОМЕН ЗА ПРИЯТЕЛЯ

ИНЖ. НАЙДЕН ХРИСТОВ НАЙДЕНОВ, СЕКРЕТАР, НА СЪЮЗА НА ХИМИЦИТЕ В БЪЛГАРИЯ (2.10.1929 – 25.10.2014)

2014 година
Книжка 6
145 ГОДИНИ БЪЛГАРСКА АКАДЕМИЯ НА НАУКИТЕ

145 ANNIVERSARY OF THE BULGARIAN ACADEMY OF SCIENCES

ПАРНО НАЛЯГАНЕ НА РАЗТВОРИ

Б. В. Тошев Българско дружество за химическо образование и история и философия на химията

LUBRICATION PROPERTIES OF DIFFERENT PENTAERYTHRITOL-OLEIC ACID REACTION PRODUCTS

Abolfazl Semnani, Hamid Shakoori Langeroodi, Mahboube Shirani

THE ORIGINS OF SECONDARY AND TERTIARY GENERAL EDUCATION IN RUSSIA: HISTORICAL VIEWS FROM THE 21ST CENTURY

V. Romanenko, G. Nikitina Academy of Information Technologies in Education, Russia

ALLELOPATHIC AND CYTOTOXIC ACTIVITY OF ORIGANUM VULGARE SSP. VULGARE GROWING WILD IN BULGARIA

Asya Pencheva Dragoeva, Vanya Petrova Koleva, Zheni Dimitrova Nanova, Mariya Zhivkova Kaschieva, Irina Rumenova Yotova

Книжка 5
GENDER ISSUES OF UKRAINIAN HIGHER EDUCATION

Н.H.Petruchenia, M.I.Vorovka

МНОГОВАРИАЦИОННА СТАТИСТИЧЕСКА ОЦЕНКА НА DREEM – БЪЛГАРИЯ: ВЪЗПРИЕМАНЕ НА ОБРАЗОВАТЕЛНАТА СРЕДА ОТ СТУДЕНТИТЕ В МЕДИЦИНСКИЯ УНИВЕРСИТЕТ – СОФИЯ

Радка Томова, Павлина Гатева, Радка Хаджиолова, Зафер Сабит, Миглена Славова, Гергана Чергарова, Васил Симеонов

MUSSEL BIOADHESIVES: A TOP LESSON FROM NATURE

Saâd Moulay Université Saâd Dahlab de Blida, Algeria

Книжка 4
ЕЛЕКТРОННО ПОМАГАЛO „ОТ АТОМА ДО КОСМОСА“ ЗА УЧЕНИЦИ ОТ Х КЛАС

Силвия Боянова Професионална гимназия „Акад. Сергей П. Корольов“ – Дупница

ЕСЕТО КАТО ИНТЕГРАТИВЕН КОНСТРУКТ – НОРМАТИВЕН, ПРОЦЕСУАЛЕН И ОЦЕНЪЧНО-РЕЗУЛТАТИВЕН АСПЕКТ

Надежда Райчева, Иван Капурдов, Наташа Цанова, Иса Хаджиали, Снежана Томова

44

Донка Ташева, Пенка Василева

ДОЦ. Д.П.Н. АЛЕКСАНДЪР АТАНАСОВ ПАНАЙОТОВ

Наташа Цанова, Иса Хаджиали, Надежда Райчева

COMPUTER ASSISTED LEARNING SYSTEM FOR STUDYING ANALYTICAL CHEMISTRY

N. Y. Stozhko, A. V. Tchernysheva, L.I. Mironova

С РАКЕТНА ГРАНАТА КЪМ МЕСЕЦА: БОРБА С ЕДНА ЛЕДЕНА ЕПОХА В ГОДИНАТА 3000 СЛЕД ХРИСТА. 3.

С РАКЕТНА ГРАНАТА КЪМ МЕСЕЦА:, БОРБА С ЕДНА ЛЕДЕНА ЕПОХА, В ГОДИНАТА 000 СЛЕД ХРИСТА. .

Книжка 3
KNOWLEDGE OF AND ATTITUDES TOWARDS WATER IN 5

Antoaneta Angelacheva, Kalina Kamarska

ВИСША МАТЕМАТИКА ЗА УЧИТЕЛИ, УЧЕНИЦИ И СТУДЕНТИ: ДИФЕРЕНЦИАЛНО СМЯТАНЕ

Б. В. Тошев Българско дружество за химическо образование и история и философия на химията

ВАСИЛ ХРИСТОВ БОЗАРОВ

Пенка Бозарова, Здравка Костова

БИБЛИОГРАФИЯ НА СТАТИИ ЗА МИСКОНЦЕПЦИИТЕ В ОБУЧЕНИЕТО ПО ПРИРОДНИ НАУКИ ВЪВ ВСИЧКИ ОБРАЗОВАТЕЛНИ НИВА

Б. В. Тошев Българско дружество за химическо образование и история и философия на химията

Книжка 2
SCIENTIX – OБЩНОСТ ЗА НАУЧНО ОБРАЗОВАНИЕ В ЕВРОПА

Свежина Димитрова Народна астрономическа обсерватория и планетариум „Николай Коперник“ – Варна

BOTYU ATANASSOV BOTEV

Zdravka Kostova, Margarita Topashka-Ancheva

CHRONOLOGY OF CHEMICAL ELEMENTS DISCOVERIES

Maria Atanassova, Radoslav Angelov

Книжка 1
ОБРАЗОВАНИЕ ЗА ПРИРОДОНАУЧНА ГРАМОТНОСТ

Адриана Тафрова-Григорова

A COMMENTARY ON THE GENERATION OF AUDIENCE-ORIENTED EDUCATIONAL PARADIGMS IN NUCLEAR PHYSICS

Baldomero Herrera-González Universidad Autónoma del Estado de México, Mexico

2013 година
Книжка 6
DIFFERENTIAL TEACHING IN SCHOOL SCIENCE EDUCATION: CONCEPTUAL PRINCIPLES

G. Yuzbasheva Kherson Academy of Continuing Education, Ukraine

АНАЛИЗ НА ПОСТИЖЕНИЯТА НА УЧЕНИЦИТЕ ОТ ШЕСТИ КЛАС ВЪРХУ РАЗДЕЛ „ВЕЩЕСТВА И ТЕХНИТЕ СВОЙСТВА“ ПО „ЧОВЕКЪТ И ПРИРОДАТА“

Иваничка Буровска, Стефан Цаковски Регионален инспекторат по образованието – Ловеч

HISTORY AND PHILOSOPHY OF SCIENCE: SOME RECENT PERIODICALS (2013)

Chemistry: Bulgarian Journal of Science Education

45. НАЦИОНАЛНА КОНФЕРЕНЦИЯ НА УЧИТЕЛИТЕ ПО ХИМИЯ

„Образователни стандарти и природонаучна грамотност“ – това е темата на състоялата се от 25 до 27 октомври 2013 г. в Габрово 45. Национална конфе- ренция на учителите по химия с международно участие, която по традиция се проведе комбинирано с Годишната конференция на Българското дружество за химическо образование и история и философия на химията. Изборът на темата е предизвикан от факта, че развиването на природонаучна грамотност е обща тенденция на реформите на учебните програми и главна

Книжка 5

ЗА ХИМИЯТА НА БИРАТА

Ивелин Кулев

МЕТЕОРИТЪТ ОТ БЕЛОГРАДЧИК

Б. В. Тошев Българско дружество за химическо образование и история и философия на химията

Книжка 4
RECASTING THE DERIVATION OF THE CLAPEYRON EQUATION INTO A CONCEPTUALLY SIMPLER FORM

Srihari Murthy Meenakshi Sundararajan Engineering College, India

CHEMICAL REACTIONS DO NOT ALWAYS MODERATE CHANGES IN CONCENTRATION OF AN ACTIVE COMPONENT

Joan J. Solaz-Portolés, Vicent Sanjosé Universitat de Valènciа, Spain

POLYMETALLIC COMPEXES: CV. SYNTHESIS, SPECTRAL, THERMOGRAVIMETRIC, XRD, MOLECULAR MODELLING AND POTENTIAL ANTIBACTERIAL PROPERTIES OF TETRAMERIC COMPLEXES OF Co(II), Ni(II), Cu(II), Zn(II), Cd(II) AND Hg(II) WITH OCTADENTATE AZODYE LIGANDS

Bipin B. Mahapatra, S. N. Dehury, A. K. Sarangi, S. N. Chaulia G. M. Autonomous College, India Covt. College of Engineering Kalahandi, India DAV Junior College, India

ПРОФЕСОР ЕЛЕНА КИРКОВА НАВЪРШИ 90 ГОДИНИ

CELEBRATING 90TH ANNIVERSARY OF PROFESSOR ELENA KIRKOVA

Книжка 3
SIMULATION OF THE FATTY ACID SYNTHASE COMPLEX MECHANISM OF ACTION

M.E.A. Mohammed, Ali Abeer, Fatima Elsamani, O.M. Elsheikh, Abdulrizak Hodow, O. Khamis Haji

FORMING OF CONTENT OF DIFFERENTIAL TEACHING OF CHEMISTRY IN SCHOOL EDUCATION OF UKRAINE

G. Yuzbasheva Kherson Academy of Continuing Education, Ukraine

ИЗСЛЕДВАНЕ НА РАДИКАЛ-УЛАВЯЩА СПОСОБНОСТ

Станислав Станимиров, Живко Велков

Книжка 2
Книжка 1
COLORFUL EXPERIMENTS FOR STUDENTS: SYNTHESIS OF INDIGO AND DERIVATIVES

Vanessa BIANDA, Jos-Antonio CONSTENLA, Rolf HAUBRICHS, Pierre-Lonard ZAFFALON

OBSERVING CHANGE IN POTASSIUM ABUNDANCE IN A SOIL EROSION EXPERIMENT WITH FIELD INFRARED SPECTROSCOPY

Mila Ivanova Luleva, Harald van der Werff, Freek van der Meer, Victor Jetten

ЦАРСКАТА ПЕЩЕРА

Рафаил ПОПОВ

УЧИЛИЩНИ ЛАБОРАТОРИИ И ОБОРУДВАНЕ SCHOOL LABORATORIES AND EQUIPMENT

Учебни лаборатории Илюстрации от каталог на Franz Hugershoff, Лайциг, притежаван от бъдещия

2012 година
Книжка 6
ADDRESING STUDENTS’ MISCONCEPTIONS CONCERNING CHEMICAL REACTIONS AND SYMBOLIC REPRESENTATIONS

Marina I. Stojanovska, Vladimir M. Petruševski, Bojan T. Šoptrajanov

АНАЛИЗ НА ПОСТИЖЕНИЯТА НА УЧЕНИЦИТЕ ОТ ПЕТИ КЛАС ВЪРХУ РАЗДЕЛ „ВЕЩЕСТВА И ТЕХНИТЕ СВОЙСТВА“ ПО ЧОВЕКЪТ И ПРИРОДАТА

Иваничка Буровска, Стефан Цаковски Регионален инспекторат по образованието – Ловеч

ЕКОТОКСИКОЛОГИЯ

Васил Симеонов

ПРОФ. МЕДОДИЙ ПОПОВ ЗА НАУКАТА И НАУЧНАТА ДЕЙНОСТ (1920 Г.)

Проф. Методий Попов (1881-1954) Госпожици и Господа студенти,

Книжка 5
КОНЦЕПТУАЛНА СХЕМА НА УЧИЛИЩНИЯ КУРС П О ХИМИЯ – МАКР О СКОПСКИ ПОДХОД

Б. В. Тошев Българско дружество за химическо образование и история и философия на химията

ROLE OF ULTRASONIC WAVES TO STUDY MOLECULAR INTERACTIONS IN AQUEOUS SOLUTION OF DICLOFENAC SODIUM

Sunanda S. Aswale, Shashikant R. Aswale, Aparna B. Dhote Lokmanya Tilak Mahavidyalaya, INDIA Nilkanthrao Shinde College, INDIA

SIMULTANEOUS ESTIMATION OF IBUPROFEN AND RANITIDINE HYDROCHLORIDE USING UV SPECTROPHOT O METRIC METHOD

Jadupati Malakar, Amit Kumar Nayak Bengal College of Pharmaceutical Sciences and Research, INDIA

GAPS AND OPPORTUNITIES IN THE USE OF REMOTE SENSING FOR SOIL EROSION ASSESSMENT

Mila Ivanova Luleva, Harald van der Werff, Freek van der Meer, Victor Jetten

РАДИОХИМИЯ И АРХЕОМЕТРИЯ: ПРО Ф. ДХН ИВЕЛИН КУЛЕВ RADIOCHEMISTRY AND ARCHEOMETRY: PROF. IVELIN KULEFF, DSc

Б. В. Тошев Българско дружество за химическо образование и история и философия на химията

Книжка 4
TEACHING THE CONSTITUTION OF MATTER

Małgorzata Nodzyńska, Jan Rajmund Paśko

СЪСИРВАЩА СИСТЕМА НА КРЪВТА

Маша Радославова, Ася Драгоева

CATALITIC VOLCANO

CATALITIC VOLCANO

43-ТА МЕЖДУНАРОДНА ОЛИМПИАДА ПО ХИМИЯ

Донка ТАШЕВА, Пенка ЦАНОВА

ЮБИЛЕЙ: ПРОФ. ДХН БОРИС ГЪЛЪБОВ JUBILEE: PROF. DR. BORIS GALABOV

Б. В. Тошев Българско дружество за химическо образование и история и философия на химията

ПЪРВИЯТ ПРАВИЛНИК ЗА УЧЕБНИЦИТЕ (1897 Г.)

Чл. 1. Съставянето и издаване на учебници се предоставя на частната инициа- тива. Забележка: На учителите – съставители на учебници се запрещава сами да разпродават своите учебници. Чл. 2. Министерството на народното просвещение може да определя премии по конкурс за съставяне на учебници за горните класове на гимназиите и специ- алните училища. Чл. 3. Никой учебник не може да бъде въведен в училищата, ако предварително не е прегледан и одобрен от Министерството на народното просвещение. Чл.

JOHN DEWEY: HOW WE THINK (1910)

John Dewey (1859 – 1952)

ИНФОРМАЦИЯ ЗА СПЕЦИАЛНОСТИТЕ В ОБЛАСТТА НА ПРИРОДНИТЕ НАУКИ В СОФИЙСКИЯ УНИВЕРСИТЕТ „СВ. КЛИМЕНТ ОХРИДСКИ“ БИОЛОГИЧЕСКИ ФАКУЛТЕТ

1. Биология Студентите от специалност Биология придобиват знания и практически умения в областта на биологическите науки, като акцентът е поставен на организмово равнище. Те се подготвят да изследват биологията на организмите на клетъчно- организмово, популационно и екосистемно ниво в научно-функционален и прило- жен аспект, с оглед на провеждане на научно-изследователска, научно-приложна, производствена и педагогическа дейност. Чрез широк набор избираеми и факул- тативни курсове студентите

Книжка 3
УЧИТЕЛИТЕ ПО ПРИРОДНИ НАУКИ – ЗА КОНСТРУКТИВИСТКАТА УЧЕБНА СРЕДА В БЪЛГАРСКОТО УЧИЛИЩЕ

Адриана Тафрова-Григорова, Милена Кирова, Елена Бояджиева

ПОВИШАВАНЕ ИНТЕРЕСА КЪМ ИСТОРИЯТА НА ХИМИЧНИТЕ ЗНАНИЯ И ПРАКТИКИ ПО БЪЛГАРСКИТЕ ЗЕМИ

Людмила Генкова, Свобода Бенева Българско дружество за химическо образование и история и философия на химията

НАЧАЛО НА ПРЕПОДАВАНЕТО НА УЧЕБЕН ПРЕДМЕТ ХИМИЯ В АПРИЛОВОТО УЧИЛИЩЕ В ГАБРОВО

Мария Николова Национална Априловска гимназия – Габрово

ПРИРОДОНАУЧНОТО ОБРАЗОВАНИЕ В БЪЛГАРИЯ – ФОТОАРХИВ

В един дълъг период от време гимназиалните учители по математика, физика, химия и естествена

Книжка 2
„МАГИЯТА НА ХИМИЯТА“ – ВЕЧЕР НА ХИМИЯТА В ЕЗИКОВА ГИМНАЗИЯ „АКАД. Л. СТОЯНОВ“ БЛАГОЕВГРАД

Стефка Михайлова Езикова гимназия „Акад. Людмил Стоянов“ – Благоевград

МЕЖДУНАРОДНАТА ГОДИНА НА ХИМИЯТА 2011 В ПОЩЕНСКИ МАРКИ

Б. В. Тошев Българско дружество за химическо образование и история и философия на химията

ЗА ПРИРОДНИТЕ НАУКИ И ЗА ПРАКТИКУМА ПО ФИЗИКА (Иванов, 1926)

Бурният развой на естествознанието във всичките му клонове през XIX –ия век предизвика дълбоки промени в мирогледа на културния свят, в техниката и в индустрията, в социалните отношения и в държавните интереси. Можем ли днес да си представим един философ, един държавен мъж, един обществен деец, един индустриалец, просто един културен човек, който би могъл да игнорира придобив- ките на природните науки през последния век. Какви ужасни катастрофи, какви социални сътресения би сполетяло съвре

Книжка 1
MURPHY’S LAW IN CHEMISTRY

Milan D. Stojković

42-рa МЕЖДУНАРОДНА ОЛИМПИАДА ПО ХИМИЯ

Донка Ташева, Пенка Цанова

СЕМЕЙНИ УЧЕНИЧЕСКИ ВЕЧЕРИНКИ

Семейството трябва да познава училишето и училишето трябва да познава семейството. Взаимното познанство се налага от обстоятелството, че те, макар и да са два различни по природата си фактори на възпитанието, преследват една и съща проста цел – младото поколение да бъде по-умно, по-нравствено, физически по-здраво и по-щастливо от старото – децата да бъдат по-щастливи от родителите