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The activity of Co3O4 prepared by different methods was examined for decomposition of N2O in the presence of 2% oxygen concentration. This N2O gas is also a part of NOx gases and it is also nominated as a greenhouse gas that is a big threat for future environment due to very stable compound and having long life many and more than CO2. It is very difficult to make unstable or decompose N2O compound especially at low temperature. Catalytical decomposition is ultimate way to convert N2O into environmental friendly gases. In the present study, Co3O4 was prepared by different methods and used as a catalyst for this purpose. In the preparation of Co3O4, three different methods were used namely sol–gel method (Co3O4‐S), calcination method (Co3O4‐C), and the co‐precipitation method (Co3O4‐P). The prepared materials were well characterized by TEM, TGA, XRD, and BET surface techniques. These three different preparation methods of Co3O4 were performed to optimize for N2O decomposition. The experimental results showed that the catalytic activities of Co3O4 strongly depended on the Na/Co molar ratio and pH of an alkaline solution during Co3O4 preparation other than reaction temperature and time parameters. The optimized catalyst 1% NaOH + 1 M NH4OH/Co3O4‐P was observed highly active at optimum pH value 9.8 and Na/Co molar ratio 0.0120. The highest activity of this optimized catalyst for N2O decomposition was found 98% at 400°C while 95%, 89%, 78%, and 68% were recorded at 350°C, 300°C, 250°C and 200°C, respectively. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13129, 2019

The activity of Co3O4 prepared by different methods was examined for decomposition of N2O in the presence of 2% oxygen concentration. This N2O gas is also a part of NOx gases and it is also nominated as a greenhouse gas that is a big threat for future environment due to very stable compound and having long life many and more than CO2. It is very difficult to make unstable or decompose N2O compound especially at low temperature. Catalytical decomposition is ultimate way to convert N2O into environmental friendly gases. In the present study, Co3O4 was prepared by different methods and used as a catalyst for this purpose. In the preparation of Co3O4, three different methods were used namely sol–gel method (Co3O4‐S), calcination method (Co3O4‐C), and the co‐precipitation method (Co3O4‐P). The prepared materials were well characterized by TEM, TGA, XRD, and BET surface techniques. These three different preparation methods of Co3O4 were performed to optimize for N2O decomposition. The experimental results showed that the catalytic activities of Co3O4 strongly depended on the Na/Co molar ratio and pH of an alkaline solution during Co3O4 preparation other than reaction temperature and time parameters. The optimized catalyst 1% NaOH + 1 M NH4OH/Co3O4‐P was observed highly active at optimum pH value 9.8 and Na/Co molar ratio 0.0120. The highest activity of this optimized catalyst for N2O decomposition was found 98% at 400°C while 95%, 89%, 78%, and 68% were recorded at 350°C, 300°C, 250°C and 200°C, respectively. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13129, 2019

Many studies have examined the impact of climatic variability on agricultural productivity, although an understanding of these effects on farmland values and their relationship to farmers’ decisions to adapt and modify their land-use practices remains nascent in developing nations. We estimated the impacts of the deviation in our study year's (2012) temperature and precipitation patterns from medium-term (1980–2011) climatic patterns on farmland values in Pakistan. This was accomplished by employing a modified form of a Ricardian regression model. We also examined farmers’ perceptions of climate change during this period, as well as their perceptions of climate change impacts on farm productivity, in addition to past and anticipated farm adaptation strategies. Our results indicate that positive temperature deviation from the medium-term mean – indicative of climatic change – affects farmland values in Pakistan. Deviation in annual cumulative precipitation conversely appears to have no significant impact. Estimates of the marginal impact of temperature deviation suggested a slight but negative linear relationship with farmland values. The location of farms in areas where farmers can avail financial or extension services conversely had a positive impact on farmland values, as did the availability of irrigation facilities. Our analysis of farmers’ perceptions of climate change and their consequent adaptation behavior indicated a relatively high degree of awareness of climatic variability that influenced a number of proactive and future anticipated farm adaptation strategies. Examples included increased use of irrigation and farm enterprise diversification, as well as land-use change, including shifting from agriculture into alternative land uses. National policy in Pakistan underscores the importance of maintaining a productive rural agricultural sector. Our findings consequently highlight the importance of appropriate adaptation strategies to maintain both farm productivity and farmland values in much of Pakistan. The implications of increased extension and financial services to enhance farmers’ potential for climate change adaptation are discussed.

Many studies have examined the impact of climatic variability on agricultural productivity, although an understanding of these effects on farmland values and their relationship to farmers’ decisions to adapt and modify their land-use practices remains nascent in developing nations. We estimated the impacts of the deviation in our study year's (2012) temperature and precipitation patterns from medium-term (1980–2011) climatic patterns on farmland values in Pakistan. This was accomplished by employing a modified form of a Ricardian regression model. We also examined farmers’ perceptions of climate change during this period, as well as their perceptions of climate change impacts on farm productivity, in addition to past and anticipated farm adaptation strategies. Our results indicate that positive temperature deviation from the medium-term mean – indicative of climatic change – affects farmland values in Pakistan. Deviation in annual cumulative precipitation conversely appears to have no significant impact. Estimates of the marginal impact of temperature deviation suggested a slight but negative linear relationship with farmland values. The location of farms in areas where farmers can avail financial or extension services conversely had a positive impact on farmland values, as did the availability of irrigation facilities. Our analysis of farmers’ perceptions of climate change and their consequent adaptation behavior indicated a relatively high degree of awareness of climatic variability that influenced a number of proactive and future anticipated farm adaptation strategies. Examples included increased use of irrigation and farm enterprise diversification, as well as land-use change, including shifting from agriculture into alternative land uses. National policy in Pakistan underscores the importance of maintaining a productive rural agricultural sector. Our findings consequently highlight the importance of appropriate adaptation strategies to maintain both farm productivity and farmland values in much of Pakistan. The implications of increased extension and financial services to enhance farmers’ potential for climate change adaptation are discussed.

Abstract Distribution networks are undergoing radical changes due to the high level of penetration of dispersed generation and storage systems. This trend is strongly modifying the structure as well as the management of distribution networks, which are progressively approaching the new concept of microgrids (MGs). Also, the level of penetration of storage systems for plug-in electric vehicles (PEVs) is increasing significantly due to the significant potential that PEVs have for reducing both emission levels and transportation costs. The inclusion of these vehicles in MGs leads to a series of challenges in grid operation, especially ensuring the provision of services that can improve the operation of distribution networks. This paper deals with MGs, including renewable generation plants and aggregators of PEV fleets connected to the grid through power electronic devices. A multi-objective optimization model is presented for obtaining optimal, coordinated operation of MGs. A multi-objective model was solved using two different methods, i.e., the exponential weighted criterion method and a compromise programming method. Both of these methods appeared to be particularly suitable when computational time is an important issue, as it is in the case of optimal control. The effectiveness of the multi-objective approach was demonstrated with numerical applications to a low-voltage microgrid; other multi-objective model-solving algorithms also were assessed in order to compare their programming complexity and the computational efforts required.

Abstract Distribution networks are undergoing radical changes due to the high level of penetration of dispersed generation and storage systems. This trend is strongly modifying the structure as well as the management of distribution networks, which are progressively approaching the new concept of microgrids (MGs). Also, the level of penetration of storage systems for plug-in electric vehicles (PEVs) is increasing significantly due to the significant potential that PEVs have for reducing both emission levels and transportation costs. The inclusion of these vehicles in MGs leads to a series of challenges in grid operation, especially ensuring the provision of services that can improve the operation of distribution networks. This paper deals with MGs, including renewable generation plants and aggregators of PEV fleets connected to the grid through power electronic devices. A multi-objective optimization model is presented for obtaining optimal, coordinated operation of MGs. A multi-objective model was solved using two different methods, i.e., the exponential weighted criterion method and a compromise programming method. Both of these methods appeared to be particularly suitable when computational time is an important issue, as it is in the case of optimal control. The effectiveness of the multi-objective approach was demonstrated with numerical applications to a low-voltage microgrid; other multi-objective model-solving algorithms also were assessed in order to compare their programming complexity and the computational efforts required.

Monitoring and maintenance of photovoltaic (PV) modules are critical for a reliable and efficient operation. Hotspots in PV modules due to various defects and operational conditions may challenge the reliability, and in turn, the entire system. From the monitoring standpoint, hotspots should be detected and categorized for subsequent maintenance. In this paper, hotspots are detected, evaluated, and categorized uniquely by using a machine learning technique on thermal images of PV modules. To achieve so, the texture and histogram of gradient (HOG) features of thermal images of PV modules are used for classification. The categorized hotspots are detected by training the machine learning algorithm, i.e., a Naive Bayes (nBayes) classifier. Experimental results are performed on a 42.24-kWp PV system, which demonstrates that a mean recognition rate of around 94.1% is achieved for the set of 375 samples.

Monitoring and maintenance of photovoltaic (PV) modules are critical for a reliable and efficient operation. Hotspots in PV modules due to various defects and operational conditions may challenge the reliability, and in turn, the entire system. From the monitoring standpoint, hotspots should be detected and categorized for subsequent maintenance. In this paper, hotspots are detected, evaluated, and categorized uniquely by using a machine learning technique on thermal images of PV modules. To achieve so, the texture and histogram of gradient (HOG) features of thermal images of PV modules are used for classification. The categorized hotspots are detected by training the machine learning algorithm, i.e., a Naive Bayes (nBayes) classifier. Experimental results are performed on a 42.24-kWp PV system, which demonstrates that a mean recognition rate of around 94.1% is achieved for the set of 375 samples.