Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies; there is increasing competitiveness among them and increasing companies’ value. Machine learning (ML) techniques become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0 and the extensive integration of paradigms such as big data and high computational power. Implementing a system able to identify faults early to avoid critical situations in the production line and its environment is crucial. Therefore, powerful machine learning algorithms are performed for fault diagnosis, real-time data classification, and predicting the state of functioning of the production line. Random forests proved to be a better classifier with an accuracy of 97%, compared to the SVM model’s accuracy which is 94.18%. However, the K-NN model’s accuracy is about 93.83%. An accuracy of 80.25% is achieved by the logistic regression model. About 83.73% is obtained by the decision tree’s model. The excellent experimental results reached on the random forest model demonstrated the merits of this implementation in the production performance, ensuring predictive maintenance and avoiding wasting energy.

Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies; there is increasing competitiveness among them and increasing companies’ value. Machine learning (ML) techniques become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0 and the extensive integration of paradigms such as big data and high computational power. Implementing a system able to identify faults early to avoid critical situations in the production line and its environment is crucial. Therefore, powerful machine learning algorithms are performed for fault diagnosis, real-time data classification, and predicting the state of functioning of the production line. Random forests proved to be a better classifier with an accuracy of 97%, compared to the SVM model’s accuracy which is 94.18%. However, the K-NN model’s accuracy is about 93.83%. An accuracy of 80.25% is achieved by the logistic regression model. About 83.73% is obtained by the decision tree’s model. The excellent experimental results reached on the random forest model demonstrated the merits of this implementation in the production performance, ensuring predictive maintenance and avoiding wasting energy.

In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.

Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.

The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities

In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.

Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.

The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities

In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.

Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.

The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities

In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.

Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.

The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities

Sebkha soils are defined as problem soils located in arid, semi-arid, and coastal areas. Generally, they are fine soil, composed of silt, sand, and clay, which are cemented by different salts (e.g., halite, gypsum, and calcite). In nature, sebkha saline soils are exposed to different drying and wetting (D-W) cycles. However, these cycles have a significant effect on the mechanical behavior of these soils. This study aims to characterize the chemical, mineralogical, and geotechnical properties of sebkha soil using an experimental approach. We focus on the effects of D-W cycles on the unconfined compressive strength (UCS) and salinity of sebkha soils from Ain M'Lila, Algeria. In addition, these D-W cycles were applied to the samples dried in the open air to achieve the targeted water content (water content values of 7%, 11.4%, and 13%). The results obtained show that the UCS increases with decrease in water content and decreases with an increase in the number of D-W cycles. In addition, these cycles affect the salinity of the sebkha soil. Indeed, a significant decrease in soil salinity was recorded with an increase in the number of D-W cycles. Finally, a relationship was found between the salinity of the soil and UCS. The latter decreases with a decrease in soil salinity; this relationship becomes very significant for low water content values of 7% or less.

Sebkha soils are defined as problem soils located in arid, semi-arid, and coastal areas. Generally, they are fine soil, composed of silt, sand, and clay, which are cemented by different salts (e.g., halite, gypsum, and calcite). In nature, sebkha saline soils are exposed to different drying and wetting (D-W) cycles. However, these cycles have a significant effect on the mechanical behavior of these soils. This study aims to characterize the chemical, mineralogical, and geotechnical properties of sebkha soil using an experimental approach. We focus on the effects of D-W cycles on the unconfined compressive strength (UCS) and salinity of sebkha soils from Ain M'Lila, Algeria. In addition, these D-W cycles were applied to the samples dried in the open air to achieve the targeted water content (water content values of 7%, 11.4%, and 13%). The results obtained show that the UCS increases with decrease in water content and decreases with an increase in the number of D-W cycles. In addition, these cycles affect the salinity of the sebkha soil. Indeed, a significant decrease in soil salinity was recorded with an increase in the number of D-W cycles. Finally, a relationship was found between the salinity of the soil and UCS. The latter decreases with a decrease in soil salinity; this relationship becomes very significant for low water content values of 7% or less.

Sebkha soils are defined as problem soils located in arid, semi-arid, and coastal areas. Generally, they are fine soil, composed of silt, sand, and clay, which are cemented by different salts (e.g., halite, gypsum, and calcite). In nature, sebkha saline soils are exposed to different drying and wetting (D-W) cycles. However, these cycles have a significant effect on the mechanical behavior of these soils. This study aims to characterize the chemical, mineralogical, and geotechnical properties of sebkha soil using an experimental approach. We focus on the effects of D-W cycles on the unconfined compressive strength (UCS) and salinity of sebkha soils from Ain M'Lila, Algeria. In addition, these D-W cycles were applied to the samples dried in the open air to achieve the targeted water content (water content values of 7%, 11.4%, and 13%). The results obtained show that the UCS increases with decrease in water content and decreases with an increase in the number of D-W cycles. In addition, these cycles affect the salinity of the sebkha soil. Indeed, a significant decrease in soil salinity was recorded with an increase in the number of D-W cycles. Finally, a relationship was found between the salinity of the soil and UCS. The latter decreases with a decrease in soil salinity; this relationship becomes very significant for low water content values of 7% or less.

Antibiotic-resistant bacteria can leave hospitals and therefore contaminate the environment and, most likely, humans and animals, through different routes, among which wastewater discharge is of great importance. This study aims to assess the possible role of hospital sewage as reservoir and dissemination pathway of carbapenem-resistant Gram-negative bacilli (GNB). Carbapenem-resistant GNB were selectively isolated from wastewater collected from a public hospital in Batna, Algeria. Species identification was carried out using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry, and antibiotic susceptibility was evaluated by the disc diffusion method. β-Lactamase production was investigated phenotypically using the double-disk synergy assay and the modified CarbaNP test, then the molecular mechanisms of β-lactam-resistance were studied by PCR and sequencing. Ten Enterobacteriaceae and 14 glucose-nonfermenting GNB isolates were obtained. All Enterobacteriaceae isolates were positive for OXA-48 and TEM-1D β-lactamases, where seven of them coproduced an extended-spectrum β-lactamase. VIM-2 carbapenemase was detected in six glucose-nonfermenting GNB isolates. However, three Pseudomonas aeruginosa, one Comamonas jiangduensis and one Acinetobacter baumannii isolates were positive for VIM-4 variant. In addition, NDM-1 enzyme was detected in four A. baumannii isolates. Our findings highlight the potential impact of hospital wastewater in the spread of drug resistance mechanisms outside of hospitals.

Antibiotic-resistant bacteria can leave hospitals and therefore contaminate the environment and, most likely, humans and animals, through different routes, among which wastewater discharge is of great importance. This study aims to assess the possible role of hospital sewage as reservoir and dissemination pathway of carbapenem-resistant Gram-negative bacilli (GNB). Carbapenem-resistant GNB were selectively isolated from wastewater collected from a public hospital in Batna, Algeria. Species identification was carried out using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry, and antibiotic susceptibility was evaluated by the disc diffusion method. β-Lactamase production was investigated phenotypically using the double-disk synergy assay and the modified CarbaNP test, then the molecular mechanisms of β-lactam-resistance were studied by PCR and sequencing. Ten Enterobacteriaceae and 14 glucose-nonfermenting GNB isolates were obtained. All Enterobacteriaceae isolates were positive for OXA-48 and TEM-1D β-lactamases, where seven of them coproduced an extended-spectrum β-lactamase. VIM-2 carbapenemase was detected in six glucose-nonfermenting GNB isolates. However, three Pseudomonas aeruginosa, one Comamonas jiangduensis and one Acinetobacter baumannii isolates were positive for VIM-4 variant. In addition, NDM-1 enzyme was detected in four A. baumannii isolates. Our findings highlight the potential impact of hospital wastewater in the spread of drug resistance mechanisms outside of hospitals.

Antibiotic-resistant bacteria can leave hospitals and therefore contaminate the environment and, most likely, humans and animals, through different routes, among which wastewater discharge is of great importance. This study aims to assess the possible role of hospital sewage as reservoir and dissemination pathway of carbapenem-resistant Gram-negative bacilli (GNB). Carbapenem-resistant GNB were selectively isolated from wastewater collected from a public hospital in Batna, Algeria. Species identification was carried out using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry, and antibiotic susceptibility was evaluated by the disc diffusion method. β-Lactamase production was investigated phenotypically using the double-disk synergy assay and the modified CarbaNP test, then the molecular mechanisms of β-lactam-resistance were studied by PCR and sequencing. Ten Enterobacteriaceae and 14 glucose-nonfermenting GNB isolates were obtained. All Enterobacteriaceae isolates were positive for OXA-48 and TEM-1D β-lactamases, where seven of them coproduced an extended-spectrum β-lactamase. VIM-2 carbapenemase was detected in six glucose-nonfermenting GNB isolates. However, three Pseudomonas aeruginosa, one Comamonas jiangduensis and one Acinetobacter baumannii isolates were positive for VIM-4 variant. In addition, NDM-1 enzyme was detected in four A. baumannii isolates. Our findings highlight the potential impact of hospital wastewater in the spread of drug resistance mechanisms outside of hospitals.

Antibiotic-resistant bacteria can leave hospitals and therefore contaminate the environment and, most likely, humans and animals, through different routes, among which wastewater discharge is of great importance. This study aims to assess the possible role of hospital sewage as reservoir and dissemination pathway of carbapenem-resistant Gram-negative bacilli (GNB). Carbapenem-resistant GNB were selectively isolated from wastewater collected from a public hospital in Batna, Algeria. Species identification was carried out using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry, and antibiotic susceptibility was evaluated by the disc diffusion method. β-Lactamase production was investigated phenotypically using the double-disk synergy assay and the modified CarbaNP test, then the molecular mechanisms of β-lactam-resistance were studied by PCR and sequencing. Ten Enterobacteriaceae and 14 glucose-nonfermenting GNB isolates were obtained. All Enterobacteriaceae isolates were positive for OXA-48 and TEM-1D β-lactamases, where seven of them coproduced an extended-spectrum β-lactamase. VIM-2 carbapenemase was detected in six glucose-nonfermenting GNB isolates. However, three Pseudomonas aeruginosa, one Comamonas jiangduensis and one Acinetobacter baumannii isolates were positive for VIM-4 variant. In addition, NDM-1 enzyme was detected in four A. baumannii isolates. Our findings highlight the potential impact of hospital wastewater in the spread of drug resistance mechanisms outside of hospitals.

Antibiotic-resistant bacteria can leave hospitals and therefore contaminate the environment and, most likely, humans and animals, through different routes, among which wastewater discharge is of great importance. This study aims to assess the possible role of hospital sewage as reservoir and dissemination pathway of carbapenem-resistant Gram-negative bacilli (GNB). Carbapenem-resistant GNB were selectively isolated from wastewater collected from a public hospital in Batna, Algeria. Species identification was carried out using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry, and antibiotic susceptibility was evaluated by the disc diffusion method. β-Lactamase production was investigated phenotypically using the double-disk synergy assay and the modified CarbaNP test, then the molecular mechanisms of β-lactam-resistance were studied by PCR and sequencing. Ten Enterobacteriaceae and 14 glucose-nonfermenting GNB isolates were obtained. All Enterobacteriaceae isolates were positive for OXA-48 and TEM-1D β-lactamases, where seven of them coproduced an extended-spectrum β-lactamase. VIM-2 carbapenemase was detected in six glucose-nonfermenting GNB isolates. However, three Pseudomonas aeruginosa, one Comamonas jiangduensis and one Acinetobacter baumannii isolates were positive for VIM-4 variant. In addition, NDM-1 enzyme was detected in four A. baumannii isolates. Our findings highlight the potential impact of hospital wastewater in the spread of drug resistance mechanisms outside of hospitals.

Antibiotic-resistant bacteria can leave hospitals and therefore contaminate the environment and, most likely, humans and animals, through different routes, among which wastewater discharge is of great importance. This study aims to assess the possible role of hospital sewage as reservoir and dissemination pathway of carbapenem-resistant Gram-negative bacilli (GNB). Carbapenem-resistant GNB were selectively isolated from wastewater collected from a public hospital in Batna, Algeria. Species identification was carried out using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry, and antibiotic susceptibility was evaluated by the disc diffusion method. β-Lactamase production was investigated phenotypically using the double-disk synergy assay and the modified CarbaNP test, then the molecular mechanisms of β-lactam-resistance were studied by PCR and sequencing. Ten Enterobacteriaceae and 14 glucose-nonfermenting GNB isolates were obtained. All Enterobacteriaceae isolates were positive for OXA-48 and TEM-1D β-lactamases, where seven of them coproduced an extended-spectrum β-lactamase. VIM-2 carbapenemase was detected in six glucose-nonfermenting GNB isolates. However, three Pseudomonas aeruginosa, one Comamonas jiangduensis and one Acinetobacter baumannii isolates were positive for VIM-4 variant. In addition, NDM-1 enzyme was detected in four A. baumannii isolates. Our findings highlight the potential impact of hospital wastewater in the spread of drug resistance mechanisms outside of hospitals.

Energy efficiency and ratio performance are two key parameters for the analysis of the performance of photovoltaic (PV) modules. The present paper focusses on the assessment of the efficiency of four different photovoltaic module technologies based on energy efficiency and ratio performance. These PV modules were installed at the Applied Research Unit in Renewable Energy (URAER) in Algeria and were used to provide experimental data to help local and international economical actors with performance enhancement and optimal choice of different technologies subject to arid outdoor conditions. The modules studied in this paper are: two thin-film modules of copper indium selenide (CIS), hetero-junction with intrinsic thin-layer silicon (HIT) and two crystalline silicon modules (polycrystalline (poly-Si), monocrystalline (mono-Si)). These technologies were initially characterized using a DC regulator based on their measured I-V characteristics under the same outdoor climate conditions as the location where the monitoring of the electrical energy produced from each PV module was carried out. The DC regulator allows for extracting the maximum electrical power. At the same time, the measurements of the solar radiation and temperature were obtained from a pyranometer type Kipp & ZonenTM CMP21 and a Pt-100 temperature sensor (Kipp & Zonen, Delft, Netherlands). These measurements were performed from July 2020 to June 2021. In this work, the monthly average performance parameters such as energy efficiency are given and analyzed. The average efficiency of the modules over 12 months was evaluated at 4.74%, 7.65%, 9.13% and 10.27% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The calculated percentage deviations in the efficiency of the modules were 8.49%, 18.88%, 19.74% and 23.57% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The low variation in the efficiency of the HIT module can be attributed to the better operation of this module under arid outdoor conditions, which makes it a promising module for adaptation to the region concerned.

Energy efficiency and ratio performance are two key parameters for the analysis of the performance of photovoltaic (PV) modules. The present paper focusses on the assessment of the efficiency of four different photovoltaic module technologies based on energy efficiency and ratio performance. These PV modules were installed at the Applied Research Unit in Renewable Energy (URAER) in Algeria and were used to provide experimental data to help local and international economical actors with performance enhancement and optimal choice of different technologies subject to arid outdoor conditions. The modules studied in this paper are: two thin-film modules of copper indium selenide (CIS), hetero-junction with intrinsic thin-layer silicon (HIT) and two crystalline silicon modules (polycrystalline (poly-Si), monocrystalline (mono-Si)). These technologies were initially characterized using a DC regulator based on their measured I-V characteristics under the same outdoor climate conditions as the location where the monitoring of the electrical energy produced from each PV module was carried out. The DC regulator allows for extracting the maximum electrical power. At the same time, the measurements of the solar radiation and temperature were obtained from a pyranometer type Kipp & ZonenTM CMP21 and a Pt-100 temperature sensor (Kipp & Zonen, Delft, Netherlands). These measurements were performed from July 2020 to June 2021. In this work, the monthly average performance parameters such as energy efficiency are given and analyzed. The average efficiency of the modules over 12 months was evaluated at 4.74%, 7.65%, 9.13% and 10.27% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The calculated percentage deviations in the efficiency of the modules were 8.49%, 18.88%, 19.74% and 23.57% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The low variation in the efficiency of the HIT module can be attributed to the better operation of this module under arid outdoor conditions, which makes it a promising module for adaptation to the region concerned.

Energy efficiency and ratio performance are two key parameters for the analysis of the performance of photovoltaic (PV) modules. The present paper focusses on the assessment of the efficiency of four different photovoltaic module technologies based on energy efficiency and ratio performance. These PV modules were installed at the Applied Research Unit in Renewable Energy (URAER) in Algeria and were used to provide experimental data to help local and international economical actors with performance enhancement and optimal choice of different technologies subject to arid outdoor conditions. The modules studied in this paper are: two thin-film modules of copper indium selenide (CIS), hetero-junction with intrinsic thin-layer silicon (HIT) and two crystalline silicon modules (polycrystalline (poly-Si), monocrystalline (mono-Si)). These technologies were initially characterized using a DC regulator based on their measured I-V characteristics under the same outdoor climate conditions as the location where the monitoring of the electrical energy produced from each PV module was carried out. The DC regulator allows for extracting the maximum electrical power. At the same time, the measurements of the solar radiation and temperature were obtained from a pyranometer type Kipp & ZonenTM CMP21 and a Pt-100 temperature sensor (Kipp & Zonen, Delft, Netherlands). These measurements were performed from July 2020 to June 2021. In this work, the monthly average performance parameters such as energy efficiency are given and analyzed. The average efficiency of the modules over 12 months was evaluated at 4.74%, 7.65%, 9.13% and 10.27% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The calculated percentage deviations in the efficiency of the modules were 8.49%, 18.88%, 19.74% and 23.57% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The low variation in the efficiency of the HIT module can be attributed to the better operation of this module under arid outdoor conditions, which makes it a promising module for adaptation to the region concerned.

Energy efficiency and ratio performance are two key parameters for the analysis of the performance of photovoltaic (PV) modules. The present paper focusses on the assessment of the efficiency of four different photovoltaic module technologies based on energy efficiency and ratio performance. These PV modules were installed at the Applied Research Unit in Renewable Energy (URAER) in Algeria and were used to provide experimental data to help local and international economical actors with performance enhancement and optimal choice of different technologies subject to arid outdoor conditions. The modules studied in this paper are: two thin-film modules of copper indium selenide (CIS), hetero-junction with intrinsic thin-layer silicon (HIT) and two crystalline silicon modules (polycrystalline (poly-Si), monocrystalline (mono-Si)). These technologies were initially characterized using a DC regulator based on their measured I-V characteristics under the same outdoor climate conditions as the location where the monitoring of the electrical energy produced from each PV module was carried out. The DC regulator allows for extracting the maximum electrical power. At the same time, the measurements of the solar radiation and temperature were obtained from a pyranometer type Kipp & ZonenTM CMP21 and a Pt-100 temperature sensor (Kipp & Zonen, Delft, Netherlands). These measurements were performed from July 2020 to June 2021. In this work, the monthly average performance parameters such as energy efficiency are given and analyzed. The average efficiency of the modules over 12 months was evaluated at 4.74%, 7.65%, 9.13% and 10.27% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The calculated percentage deviations in the efficiency of the modules were 8.49%, 18.88%, 19.74% and 23.57% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The low variation in the efficiency of the HIT module can be attributed to the better operation of this module under arid outdoor conditions, which makes it a promising module for adaptation to the region concerned.

Energy efficiency and ratio performance are two key parameters for the analysis of the performance of photovoltaic (PV) modules. The present paper focusses on the assessment of the efficiency of four different photovoltaic module technologies based on energy efficiency and ratio performance. These PV modules were installed at the Applied Research Unit in Renewable Energy (URAER) in Algeria and were used to provide experimental data to help local and international economical actors with performance enhancement and optimal choice of different technologies subject to arid outdoor conditions. The modules studied in this paper are: two thin-film modules of copper indium selenide (CIS), hetero-junction with intrinsic thin-layer silicon (HIT) and two crystalline silicon modules (polycrystalline (poly-Si), monocrystalline (mono-Si)). These technologies were initially characterized using a DC regulator based on their measured I-V characteristics under the same outdoor climate conditions as the location where the monitoring of the electrical energy produced from each PV module was carried out. The DC regulator allows for extracting the maximum electrical power. At the same time, the measurements of the solar radiation and temperature were obtained from a pyranometer type Kipp & ZonenTM CMP21 and a Pt-100 temperature sensor (Kipp & Zonen, Delft, Netherlands). These measurements were performed from July 2020 to June 2021. In this work, the monthly average performance parameters such as energy efficiency are given and analyzed. The average efficiency of the modules over 12 months was evaluated at 4.74%, 7.65%, 9.13% and 10.27% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The calculated percentage deviations in the efficiency of the modules were 8.49%, 18.88%, 19.74% and 23.57% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The low variation in the efficiency of the HIT module can be attributed to the better operation of this module under arid outdoor conditions, which makes it a promising module for adaptation to the region concerned.