Evaluating the impact of soil and ash accumulation on solar photovoltaic panel: A data envelopment and optical characterization approach

In this paper, we propose an algorithm to calculate cross-efficiency scores which used the equations forming the efficient frontier in data envelopment analysis (DEA). In many standard DEA models, each decision-making unit (DMU) is evaluated by using the advantageous weight for itself. Then, many DMUs are evaluated as efficient, and those efficient DMUs are not ranked by the models. The cross-efficiency evaluation is a method to rank DMUs by using the advantageous weights for all DMUs. Previously, the cross-efficiency scores based on different ideas are calculated by solving multiple linear or nonlinear programming problems. However, it is often hard to solve such a nonlinear programming problem. Therefore, by analysing the efficient frontier, we construct an algorithm to calculate alternative cross-efficiency scores.

Data envelopment analysis (DEA) is a popular technique for measuring the relative efficiency of a set of decision making units (DMUs). Fully ranking DMUs is a traditional and important topic in DEA. In various types of ranking methods, cross efficiency method receives much attention from researchers because it evaluates DMUs by using self and peer evaluation. However, cross efficiency score is usual nonuniqueness. This paper combines the DEA and analytic hierarchy process (AHP) to fully rank the DMUs that considers all possible cross efficiencies of a DMU with respect to all the other DMUs. We firstly measure the interval cross efficiency of each DMU. Based on the interval cross efficiency, relative efficiency pairwise comparison between each pair of DMUs is used to construct interval multiplicative preference relations (IMPRs). To obtain the consistency ranking order, a method to derive consistent IMPRs is developed. After that, the full ranking order of DMUs from completely consistent IMPRs is derived. It is worth noting that our DEA/AHP approach not only avoids overestimation of DMUs’ efficiency by only self-evaluation, but also eliminates the subjectivity of pairwise comparison between DMUs in AHP. Finally, a real example is offered to illustrate the feasibility and practicality of the proposed procedure.

This study mainly focuses on understanding the properties of dust particle deposition (cement, brick powder, white cement, fly ash, and coal) on a solar PV panel under dry conditions in an indoor environment to record the percentage efficiency reduction profile. For the experimental study, a solar PV panel is manually drifted at three different tilted angles (0?, 15?, and 30?) with respect to five different dust samples taken to replicate dry conditions. To maintain optimal power storage by ensuring maximum ray reflection as the angle of inclination of the solar PV panel changes. It entails long-term postoperative improvement of the solar PV module by increasing feasibility and meeting user needs. From the experiment result, it is observed that percent of power loss of each dust particle is measured accurately such as cement (0.067), brick (0.190), white cement (0.163), fly ash (0.164), and coal (0.177), consolidated for three different tilt angles. Similarly, percent of power efficiency of each dust particle is measured accurately for three different tilt angles such as cement (76.689%), brick (61.822%), white cement (52.792%), fly ash (59.859%), and coal (75.381%), respectively.

Solar photovoltaic (PV) panels that use polycrystalline silicon cells are a promising technique for producing renewable energy, although research on the cells’ efficiency and thermal control is still ongoing. This experimental research aims to investigate a novel way to improve power output and thermal performance by combining solar PV panels with burned fly-ash tiles. Made from burning industrial waste, torched fly ash has special qualities that make it useful for architectural applications. These qualities include better thermal insulation, strengthened structural integrity, and high energy efficiency. Our test setup shows that when solar PV panels are combined with torched fly-ash tiles, power generation rises by 7% and surface temperature decreases by 3% when compared to standard panels. The enhanced PV efficiency is ascribed to the outstanding thermal insulation properties of fly ash tiles and their capacity to control panel temperature. To ensure longevity and safety in building applications, the tiles employed in this study had a water absorption rate of 5.37%, flexural strength of 2.95 N/mm2, and slip resistance at 38 km/h. Furthermore, we find improved structural resilience and lower cooling costs when up to 30% of the sand in floor tiles is replaced with torched fly ash, which makes this method especially appropriate for sustainable buildings. Key performance indicators that show how effective these tiles are in maximizing energy use in buildings include thermal emissivity (0.874), solar reflectance (0.8), and solar absorption (0.256). While supporting more ecofriendly building techniques, this study highlights the advantages of utilizing burned fly ash in solar PV systems: enhanced power generation and thermal comfort. The main results open a greater potential for fly ash use in different building materials. The use of torched fly ash in building materials enhances thermal insulation and structural integrity while lowering cooling costs, making it an ideal choice for eco-friendly construction and highlighting the potential for further research into environmentally responsible, energy-efficient solutions.

In this study, variable returns to scale (VRS) data envelopment analysis was integrated into the Taguchi approach to optimize ternary additives for expansive soil enhancement. The ternary additives selected were sawdust ash (SDA), quarry dust (QD) and ordinary Portland cement (OPC). The additives were set as the input variables while multiple responses obtained from the experiments performed with the Taguchi orthogonal array were set as the output variables. Each row in the orthogonal array were defined as a decision making unit (DMU) in the optimization process and output-oriented VRS model was used to obtain the efficiency score for each DMU. Next, benevolent formulation was utilized to obtain the multipliers for the inputs and outputs which were subsequently used to determine the cross efficiency scores for each DMU. The cross-efficiency scores were used to construct the cross-efficiency matrix. Thereafter, the mean cross-efficiency score (MCES) was determined for each DMU. Parameter level that maximizes the MCES was chosen as the optimal level for that parameter. Optimum combination of additives was found at A6 B2 C3. Lastly, confirmatory experiments performed by blending the soil with the optimum combination of additives showed the effectiveness of this method in the enhancement of expansive soil properties.

In view of the growing demand for energy in Iran, the efficient locations and development of renewable energy sources has become a major issue. The importance of prioritizing of the wind turbine site is being emphasized to maximize the productivity, and is a primary and important stage for decision makers in many countries. This research aims to prioritize five cities of Iranshahr, Chabahar, Zabol, Zahedan, and Saravan in Sistan-Baluchistan province located in southeastern part of Iran for wind turbine installation. Data Envelopment Analysis (DEA) methodology was used for this purpose. Five important criteria of wind potential, topography of the region, distance to the power distribution network, land cost, and intensity of natural hazards were considered for this analysis. Also, Anderson-Petersen model was used for ranking efficient Decision Making Units (DMUs). Finally, Simple Additive Weighting (SAW) method was used to validate the results of DEA model. Comparison of the results of DEA and SAW illustrated an excellent fit which indicates high validity of the DEA results. The results showed that the city of Zabol was the best option for wind turbine installation in the province.

This chapter presents work on the use of an artificial intelligence technique to cluster stratified samples of container terminals derived from Data Envelopment Analysis (DEA). This technique is Kohonen’s self-organizing map (SOM; (Kohonen, 1995)). Data envelopment analysis measures the relative efficiency of comparable entities called Decision Making Units (DMUs) essentially performing the same task using similar multiple inputs to produce similar multiple outputs ((Charnes et al., 1978)). The purpose of DEA is to empirically estimate the so-called efficient frontier based on the set of available DMUs. DEA provides the user with information about the efficient and inefficient units, as well as the efficiency scores and reference sets for inefficient units. The results of the DEA analysis, especially the efficiency scores, are used in practical applications as performance indicators. There are many problems associated with applying the DEA in some applications. One problem is that the improvement projection for inefficient units in DEA analysis is concrete relative to its efficiency score. This means, in DEA, relative performance of any DMU can be contrasted only to the efficient DMUs that register unit efficiency score. There is no influence on the performance of efficient DMUs by presence or absence of inefficient DMUs. Therefore, the classical DEA does not actually provide a direct means to rank DMUs based on their relative degrees of efficiency or inefficiency ((Sharma & Yu, 2010)). The second problem is that the DEA models assume that all DMUs are homogenous and identical in their operations ((Seiford, 1994)). Since various applications have heterogeneous DMUs and there is a high request to evaluate these applications under the DEA due to its acceptance as a performance measurement in different kind of business, we have to modify the DEA to work with these applications. If the heterogeneous DMUs are assessed by DEA without any modifications, the DEA yields a biased performance scores and inaccurate analyses. For example, the resources (land, equipment, and labor) of container terminals varies across the world, which requires to be evaluated in term of its common input characterstics. An essential requirement in analyzing these container terminals is to build a fair referencing system for each container terminal to manage and provide a solid plan that improves all inefficient terminals and supports all efficient terminals. This system can not be assessed under the standard DEA due to the non-homogenous nature of these container terminals in terms of their operations, different standards of equipments, infrastructure, and variety in quay length and area size. These factors will yield unfair benchmarking evaluation 5

This research aims to experimentally improve the overall efficiency of solar photovoltaic (PV) panels by coating them with hydrophobic SiO2 nanomaterial. Also, an accurate mathematical model was used to estimate the parameters of the PV panel, which is a non-linear optimization problem. Based on the experimental data and using the particle swarm optimization (PSO) algorithm, the optimal five parameters of a single diode model of a PV panel were determined in this study. This experimental work was conducted and carried out in the Renewable Energy Laboratory of Assiut University, Egypt. A comparative analysis was completed for three identical solar PV panels; the first panel was coated with hydrophobic SiO2 nanomaterial, so it was considered to be a self-cleaning panel; the second panel was uncoated and cleaned manually on a daily basis; and the third panel was kept dusty all the time through the experimental investigation, and was used as a reference. Experimentally, the output power of the PV panels was monitored for each panel in this study. Also, the anti-static and anti-reflection effects of coating solar PV panels with hydrophobic SiO2 nanomaterial were investigated experimentally. According to the obtained experimental results, it was found that the use of SiO2 coating for PV panels results in the better performance of the PV panels. The overall efficiency of the coated panel increased by 15% and 5%, compared to the dusty panel and the uncoated panel which was manually cleaned daily, respectively.

The effects of ash and commercial fertilizers on the foliar nutrient concentrations and stand growth of Scots pine were studied in four field experiments established on former cultivated peat soils. The aims were to compare ash types (wood, peat and coal ash), study the effects of ash treatment (pelletization), compare ash fertilization with commercial fertilizers, and to study the interaction between ash fertilization and weed control. Foliar samples were collected 1–3 years and 7–8 years after fertilization. In the unfertilized plots, the foliar nitrogen and phosphorus concentrations were fairly high, while those of potassium were low in all the experiments. The boron levels were low in three out of the four experiments. Application of either loose or pelletized wood ash, as well as of commercial fertilizers, increased foliar potassium and boron concentrations, and thus successfully remedied the existing nutrient imbalances and deficiencies. Since phosphorus deficiencies are rarely encountered on field afforestation sites, poor-quality wood ash with low phosphorus concentration could be used. Peat ash containing phosphorus, but only small amounts of potassium and boron, was not found to be very suitable for soil amelioration in connection with field afforestation. Coal ash, containing only small amounts of potassium, was a good source of boron for pine even when used in small amounts, and thus it can be used in cases where boron deficiencies alone are encountered. Wood ash significantly increased the height growth of Scots pines in two of the experiments, but peat ash and coal ash had no statistically significant effect. Wood ash increased the number of healthy seedlings. Vegetation control decreased seedling mortality by 24%, increased the growth of pine and decreased the proportion of trees damaged by elk and by deciduous trees.

We study in this paper the performance of Hospitals in Lebanon. Using the nonparametric method Data Envelopment Analysis (DEA), we are able to measures relative efficiency of Hospitals in Lebanon. DEA is a technique that uses linear programming and it measures the relative efficiency of similar type of organizations termed as Decision Making Units (DMUs). In this study, due to the lack of individual data on hospital level, each DMU refers to a qada in Lebanon where the used data represent the aggregation of input and outputs of different hospitals within the qada. In DEA, the inclusion of more number of inputs and /or outputs results in getting a more number of efficient units. Therefore, selecting the appropriate inputs and outputs is a major factor of DEA results. Therefore, we use here the Principal Component Analysis (PCA) in order to reduce the data structure into certain principal components which are essential for identifying efficient DMUs. It is important to note that we have used the basic BCC-input model for the entire analysis. We considered 24 DMUs for the study, using DEA on original data; we got 17 DMUs out of 24 DMUs as efficient. Then we considered 1 PC for inputs and 1 PC for output with almost 80 percent variances, resulting in 3 DMUs as efficient and 21 as inefficient. Using 1 PC for input and 2 PCs for output with 90 percent variance for both input and output, we got 9 DMUs as efficient and 15 DMUs as inefficient. Finally, we have attempted to identify the efficient units with 2 PCs and for 2 PCs for input and outputs with variance more than 95 percent, resulting in 10 efficient DMUs and 14 inefficient DMUs. In Principal Component analysis, if the variance lies between 80 percent to-90 percent it is judged as a meaningful one. It is concluded that Principal Component Analysis plays an important role in the reduction of input output variables and helps in identifying the efficient DMUs and improves the discriminating power of DEA.

A balanced data envelopment analysis cross-efficiency evaluation approach

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Data Envelopment Analysis (DEA) is a mathematical programming approach for assessing the relative efficiency of decision making units (DMUs). The cross-efficiency evaluation is an extension of DEA that provides a ranking method and eliminates unrealistic DEA weighting schemes on weight restrictions, without requiring a prior information. The cross-efficiency evaluation may have some shortages, e.g. the cross-efficiency scores may not be unique due to the presence of several optima. To rectify this issue, several secondary goals have been proposed in the literature. Some scholars have proposed several cross-efficiency evaluations based on maximising (minimising) the total deviation from their ideal point as an aggressive (benevolent) perspective. In some cases, minimising (maximising) the number of DMUs that achieve their target efficiencies, is more important than maximising (minimising) the total deviation from the ideal point. We propose some alternative models for the cross-efficiency evaluation based on the cardinality of the set of “satisfied DMUs”, i.e. the DMUs that achieve their maximum efficiencies. For aggressive (benevolent) cross-efficiency evaluation, among all the optimal weights for a specific unit, we choose the weights which can maximise its efficiency, and at the same time minimise (maximise) the number of satisfied units. We demonstrate how the proposed method can be implemented and illustrate the method using two examples.

The purpose of this study was to analyze the efficiency of local government expenditure based on Islamic Human Development Index (I-HDI) in Local Government at the Indonesian’s Province using Data Envelopment Analysis (DEA). DEA is a non parametic method that was developed to measure the level of efficiency compared to the Decision Making Unit (DMU). This method can measure the efficiency of a government entity by using multi-input and multi-output. DEA result also can be used to identify wich input or output must be increased by Decision Making Unit (DMU) to reach optimum efficiency. The result from this study showed that the average of efficiency score in local government at Indonesian’s Province is increasing from 2015 until 2018. This study also indicates that North Sumatera, East Java, and Yogyakarta’s government spending were efficient in 4 years. While Bangka Belitung and West Papua achieve the lowest efficiency score, and should optimize input and increase the number of output to improve the efficiency. Morever, the main source of inefficiency is in Life birth rate indicators.

The purpose of this study was to analyze the efficiency of local government expenditure based on Islamic Human Development Index (I-HDI) in Local Government at the Indonesian’s Province using Data Envelopment Analysis (DEA). DEA is a non parametic method that was developed to measure the level of efficiency compared to the Decision Making Unit (DMU). This method can measure the efficiency of a government entity by using multi-input and multi-output. DEA result also can be used to identify wich input or output must be increased by Decision Making Unit (DMU) to reach optimum efficiency. The result from this study showed that the average of efficiency score in local government at Indonesian’s Province is increasing from 2015 until 2018. This study also indicates that North Sumatera, East Java, and Yogyakarta’s government spending were efficient in 4 years. While Bangka Belitung and West Papua achieve the lowest efficiency score, and should optimize input and increase the number of output to improve the efficiency. Morever, the main source of inefficiency is in Life birth rate indicators.