Arwa Al Mayasi

Bio: Arwa Al Mayasi is an academic researcher. The author has contributed to research in topics: Photovoltaic system. The author has an hindex of 1, co-authored 1 publications receiving 15 citations.

Effect of Soiling on Solar Photovoltaic Performance under Desert Climatic Conditions

Abstract: The solar irradiation at the gulf Arabia is considered one of the highest in the world. However, this region is classified as a desert with high dust accumulation. Thus, the objective of this study is to analyze the effect of soiling and the photovoltaic (PV) tilt angle on the performance of 2.0 MWp of car park PV plant in Oman. Experimental measurements were taken and a model was developed for simulation. The power generation by the cleaned PV system was measured as 1460 kW around noon. After one week of operation, the power production (at the same irradiance level) reduced to 1390 kW due to soiling. It further reduced to 1196 kW and 904 kW after three and five weeks of operation, respectively. The results also show that a soiling-percentage of 7.5% reduced the monthly electricity generation (307 MWh) by 5.6% and a soiling-percentage of 12.5% reduced the generation by 10.8%. Furthermore, the increase in tilt is not recommended due to the duo-pitch canopy effect of the car park where the panels with 180° azimuth generate lower electricity than the panels with 0° azimuth. In addition, the part of the car park with 180° azimuth caused shading to the other part for high tilt angles.

Measured Performance of a 1.72 kW Rooftop Grid Connected Photovoltaic System in Ireland

Abstract: This paper presents results obtained from monitoring a 1.72 kWp photovoltaic system installed on a flat roof of a 12 m high building in Dublin, Ireland (latitude 53.4°N and longitude 6.3°E). The system was monitored between November 2008 and October 2009 and all the electricity generated was fed into the low voltage supply to the building. Monthly average daily and annual performance parameters of the PV system evaluated include: final yield, reference yield, array yield, system losses, array capture losses, cell temperature losses, PV module efficiency, system efficiency, inverter efficiency, performance ratio and capacity factor. The maximum solar radiation, ambient temperature and PV module temperature recorded were 1241 W/m2 in March, 29.5 °C and 46.9 °C in June respectively. The annual total energy generated was 885.1 kW h/kWp while the annual average daily final yield, reference yield and array yield were 2.41 kW h/kWp/day, 2.85 kW h/kWp/day and 2.62 kW h/kWp/day respectively. The annual average daily PV module efficiency, system efficiency and inverter efficiency were 14.9%, 12.6% and 89.2% respectively while the annual average daily performance ratio and capacity factor were 81.5% and 10.1% respectively. The annual average daily system losses, capture losses and cell temperature losses were 0.23 h/day, 0.22 h/day and 0.00 h/day respectively. Comparison of this system with other systems in different locations showed that the system had the highest annual average daily PV module efficiency, system efficiency and performance ratio of 14.9%, 12.6% and 81.5% respectively. The PV system’s annual average daily final yield of 2.4 kW h/kWp/day is higher than those reported in Germany, Poland and Northern Ireland. It is comparable to results from some parts of Spain but it is lower than the reported yields in most parts of Italy and Spain. Despite low insolation levels, high average wind speeds and low ambient temperature improve Ireland’s suitability.

Relating consumers' information and willingness to buy electric vehicles: Does personality matter?

Abstract: Being an energy-efficient mode of transportation, electric vehicles (EVs) adoption is a multifaceted mechanism driven by a bunch of factors. However, studies focusing on assessing the influence of personality traits on consumers' information about EVs (CINEVs) and willingness to buy (WTB) EVs are scarce. This study investigates the relationship between CINEVs and WTBEVs by accommodating the moderating role of the Big Five personality traits. Results are based on a sample of 624 respondents in the seven largest Indian cities by employing a comprehensive questionnaire survey. Structural equation modeling is used to test the formulated hypotheses. The results highlight that CINEVs is directly related to WTBEVs. We further add to the existing pool of knowledge by providing empirical evidence that openness, conscientiousness, extraversion, and agreeableness positively moderate the relationship between CINEVs and WTBEVs, whereas neuroticism negatively moderates this relationship. The results uncovered an interesting role of personality traits in propagating EV development.

Universal Model to Predict Expected Direction of Products Quality Improvement

Abstract: Improving the quality of products remains a challenge. This is due to the turbulent environment and the dynamics of changing customer requirements. Hence, the key action is to predict beneficial changes in products, which will allow one to achieve customer satisfaction and reduce the waste of resources. Therefore, the purpose of this article was to develop a universal model to predict the expected direction of quality improvement. Initially, the purpose of the research was determined by using the SMART(-ER) method. Then, during the brainstorming method (BM), the product criteria and range states of these criteria were determined. Next, a survey with the Likert scale was used to obtain customers’ expectations, i.e., assessing the importance of criteria and customers’ satisfaction with ranges of product criteria states. Based on customer assessments, quality product levels were calculated using the Weighted Sum Model (WSM). Then, the initial customer satisfaction from the product quality level was identified according to the relative state’s scale. Based on this, the direction of product quality improvement was anticipated using the Naïve Bayesian Classifier (NBC). A test of the model was carried out for photovoltaic panels (PV) of a key EU producer. However, the proposed model is universal, because it can be used by any entity to predict the direction of improvement of any kind of product. The originality of this model allows the prediction of the destination of product improvement according to customers’ assessments for weights of criteria and satisfaction from ranges of quality-criterion states.

Parametric study and optimization of microwave assisted biodiesel synthesis from Argemone Mexicana oil using response surface methodology

Abstract: Biodiesel production from nonedible feedstock is a viable alternative to fulfill the energy need and to reduce the emission from constraint fuel. Argemone Mexicana is a weed that grows in the arid zone and contains a high amount of non-edible oil makes it an ideal feedstock for biodiesel synthesis. The motive of the present study is to explore the transesterification of Argemone Mexicana oil using a microwave heat source and a Parametric study, following by optimization of biodiesel yield using response surface methodology employing central composite design. The quadratic model with a high value of R2 99, R2adj 97.60 revealed good agreement with the actual AOME yield. The optimized operating parameters such as temperature, catalyst amount, time, and methanol to oil molar ratio were found to be 58.8 °C, 1.03 wt%, 3.56 min, and 9.54:01 respectively. The predicted yield of 99.28 wt% was in close agreement with an actual yield of 99.03%. The study reveals that catalyst amount, temperature, time, and methanol to oil molar ratio have a significant effect on microwave-assisted transesterification of Argemone oil. Argemone oil methyl esters (AOME) exhibit property close to ASTM standards and it has the potential as an alternative to diesel.