Empirical modelling of the solar spectral influence on photovoltaic devices for improved performance forecasting

Photovoltaic performance modelling is essential for the successful development of PV systems. Accurate modelling can inform system design and financing prior to construction, help with fault detection during operation, and improve the grid penetration of PV energy. Whereas the models to account for the effects of broadband irradiance, temperature, and so forth on PV performance are well established, those for the influence of the solar spectrum, known as spectral correction functions (SCFs), suffer a range of limitations. Existing models are typically based on proxy variables used to represent the solar spectrum, which are restricted in the amount of information they contain on the prevailing spectral irradiance conditions. Furthermore, validation of these models is restricted to climates that are not representative of the UK, where a broader range of spectral irradiance conditions is experienced due to its high northern latitude and frequent overcast or partially overcast skies. Some studies have explored the possibility of characterising measured spectra with parameters such as the average photon energy to develop SCFs. However, these studies are limited in terms of their validation scope, such as duration of field data and types of PV module, and extension to a predictive model. In this project, two new SCFs are developed and validated in two distinct climate regions for multiple PV technologies. The first is based on the average photon energy alone (f(APE)), while the second is based on both the average photon energy and the depth of the 650--670nm water absorption band (f(APE,e)). Using data from Go (Golden, Colorado, USA), the former is shown to cut the prediction error for aSi modules by around 40% relative to a single-variable air mass SCF and a double-variable air mass and clearness index SCF. The latter, f(APE,e), addresses issues raised in the literature regarding the reliability of APE as a spectral characterisation index. Using the same data, f(APE,e) is shown to cut the prediction error by up to 60% with respect to a comparable multivariable proxy SCF based on the air mass and atmospheric precipitable water content. These results are also validated at a new test site built at the University of Nottingham as part of this project. Although the overall errors are greater due to site-specific system characteristics, the relative improvements achieved by the APE-based models with respect to the proxy-based models are maintained in both climate regions. The proposed spectral correction approaches can be integrated into wider PV performance models to improve their performance forecasting accuracy

Examination of the factors which influence farmers’ intentions towards the implementation of nutrient management planning

Nutrients such as Nitrogen (N), Phosphorous (P) and Potassium (K) and other micronutrients such as such as magnesium, manganese and cobalt, are essential for the continued growth of global agricultural production. These nutrients are typically applied to agricultural fields in the form of synthetic fertiliser and/or manure. However, if not used efficiently, the risk of loss to water courses and the atmosphere can increase. Inefficient use has led to global deteriorations in water quality, algal blooms, fish kills and contributed to greenhouse gas emissions. Poor management of nutrients is one important reason contributing to the inefficient use of nutrients on farms. Key issues include the over application of the wrong nutrient source to fields that do not require it, using the wrong rate at the wrong time. Under application of nutrients is also an issue as this has been associated with the underperformance of crops and reductions in soil fertility levels. Farmers are advised to adopt certain nutrient management practices that have been proven to ensure that nutrients are targeted appropriately which has been associated with improvements in nutrient use efficiency, production and a reduction in the risk of nutrient losses to the environment. One such practice is called nutrient management planning. This is a process which involves the collection of site-specific information (e.g. stocking rate, soil fertility levels of crop type) which is then used to devise a nutrient management plan. A nutrient management plan is a document that is developed by farmers typically in conjunction with an agricultural advisor. This plan makes recommendations of how best to target nutrients in line with crop demand. However, despite widespread pressure and considerable promotion of the advantages of nutrient management planning, uptake of nutrient management planning by farmers remains limited globally. Policy makers are keen to understand what motivates farmers to implement nutrient management planning. The overall aim of the research presented in this thesis is to examine and explain the factors which influence farmers’ intentions towards the implementation of nutrient management planning. The two practices under consideration are farmers’ intentions to apply fertiliser on the basis of soil test results (practice one) and to follow a nutrient management plan (NMP) (practice two). A review of the literature demonstrates that there remains a dearth of studies specifically focusing on the uptake of nutrient management planning. Furthermore, among the existing studies, the focus is typically on explaining uptake as a function of farm (e.g. system and farm size) and farmer characteristics (e.g. age and education). A limited number of studies specifically in relation to nutrient management planning focus on the socio-psychological beliefs, including social pressure and perceptions of capability, of farmers. Those studies that do focus on these issues typically remain qualitative in nature and therefore generalising the results remains an issue. To accomplish the aim of this research the well-established socio-psychological Theory of Planned Behaviour (TPB) is used as a basis for understanding farmers’ intentions towards implementing nutrient management planning. A number of additional variables are also chosen based on a review of the literature such as farm system, farm size, farmer age and education as well as use and trust in information sources. The data came from a sample (n=1009) of Irish farmers for the year 2016. A quota controlled system was set in place to ensure that the sample was representative in terms of predominant farm systems and sizes in Ireland. Ireland presents an interesting case study for analysis due to ambitious targets to increase food production, whilst also maintaining and improving water quality, whilst reducing overall greenhouse gas emissions from agriculture. The issues in this Irish case are reflected more widely and therefore results from this study can be generalised. A cross-sectional survey was designed to collect information from farmers regarding their beliefs and intentions towards the implementation of the aforementioned practices and information regarding the additional variables. To analyse the data elicited by the survey a range of econometric techniques are applied. The primary techniques employed include binary logistic regression, principal component analysis, latent class analysis and structural equation modelling. In total three separate analyses are conducted which are presented as three empirical papers. The first analyses farmers’ intentions to apply fertiliser on the basis of soil test results whereas the second and third both focus on farmers intentions to follow a nutrient management plan. Overall, the results from the analyses show that subjective norm (social pressure) and perceived behavioural control (farmers’ perception of ease/difficulty of implementation) to implement these practices are among the most important factors determining their intention to use them. Agricultural extension is also another key factor influencing farmers’ intentions. However, the results from the latent class analysis also show that the variables which influence farmers’ intentions vary between groups in terms of significance, but also magnitude of influence (marginal effect). Finally, results from the structural equation model also highlight that farmers’ place their trust in different sources of information and as trust increases farmers’ perceptions of nutrient management planning are influenced. These results provide policy makers with useful information for increasing the use of nutrient management planning among farmers. The results of this thesis suggest five main strategies to increase farmers’ intentions to adopt nutrient management planning. First, increase social pressure on farmers to use this practice. Second, increase farmers’ level of perceived behavioural control (ability) over implementing nutrient management planning. Thirdly, increase contact between agricultural extension and farmers, in particular combing both one-to-one contact and group based learning environments may be beneficial. Fourthly, information about nutrient management planning should be targeted through the sources of information farmers are more likely to trust. Finally, policy makers must target different groups of farmers with campaigns designed to increase implementation of nutrient management planning because the results show that farmers are likely to respond differently. Future research should be directed at examining the best methods for increasing social pressure and perceptions of control and to encourage and enable farmers to implement nutrient management planning and how these campaigns should be tailored to specific groups of farmers

Dissolution Method Development and Validation for Estimation of Noscapine Tablets

A dissolution method was developed and UV spectrophotometry was developed for the evaluation of the dissolution of tablets containing 15 mg Noscapine .The dissolution medium 0.1 N HCl was found suitable to ensure sink conditions. USP Apparatus 2, 900 mL dissolution medium 45 minutes and 100 RPM were fixed. Dissolution profiles were generated at 10, 15, 20,   30; 45 min. Dissolution samples were analyzed with UV spectrophotometer at 213 nm. The UV method for determination of tablet was developed and validated. The method presented linearity (R2 = 0.999) in the concentration range of 1–9 μg/mL. The recoveries were good, ranging from 97.18% to 101.45%. The intraday and Interday precision results were 0.54% and 0.78% RSD, respectively. The developed dissolution test is adequate for its purpose and can be applied for the quality control of tablets. Keywords: Dissolution test; Noscapine; Tablets; UV Spectrophotometry metho

Empirical modelling of the solar spectral influence on photovoltaic devices for improved performance forecasting

Photovoltaic performance modelling is essential for the successful development of PV systems. Accurate modelling can inform system design and financing prior to construction, help with fault detection during operation, and improve the grid penetration of PV energy. Whereas the models to account for the effects of broadband irradiance, temperature, and so forth on PV performance are well established, those for the influence of the solar spectrum, known as spectral correction functions (SCFs), suffer a range of limitations. Existing models are typically based on proxy variables used to represent the solar spectrum, which are restricted in the amount of information they contain on the prevailing spectral irradiance conditions. Furthermore, validation of these models is restricted to climates that are not representative of the UK, where a broader range of spectral irradiance conditions is experienced due to its high northern latitude and frequent overcast or partially overcast skies. Some studies have explored the possibility of characterising measured spectra with parameters such as the average photon energy to develop SCFs. However, these studies are limited in terms of their validation scope, such as duration of field data and types of PV module, and extension to a predictive model. In this project, two new SCFs are developed and validated in two distinct climate regions for multiple PV technologies. The first is based on the average photon energy alone (f(APE)), while the second is based on both the average photon energy and the depth of the 650--670nm water absorption band (f(APE,e)). Using data from Go (Golden, Colorado, USA), the former is shown to cut the prediction error for aSi modules by around 40% relative to a single-variable air mass SCF and a double-variable air mass and clearness index SCF. The latter, f(APE,e), addresses issues raised in the literature regarding the reliability of APE as a spectral characterisation index. Using the same data, f(APE,e) is shown to cut the prediction error by up to 60% with respect to a comparable multivariable proxy SCF based on the air mass and atmospheric precipitable water content. These results are also validated at a new test site built at the University of Nottingham as part of this project. Although the overall errors are greater due to site-specific system characteristics, the relative improvements achieved by the APE-based models with respect to the proxy-based models are maintained in both climate regions. The proposed spectral correction approaches can be integrated into wider PV performance models to improve their performance forecasting accuracy

Roll of Smoking in the Default of Tuberculosis Treatment in Rajkot District

Background: Smoking is the major cause of Mycobacterial infection, default and relapse of the infection. Aims and Objective: This study was conducted to estimate the effect of smoking in the default patients of tuberculosis treatment. Material and Method: 150 cases (TB treatment Defaulter) and 150 controls (Who have completed whole course of treatment) from the Rajkot district of Gujarat were enrolled and interviewed. Smokers were identified by pre-structured interview and self-report. Risk of treatment default was calculated in smokers. Results: In smokers risk of treatment default was 265% higher (OR = 2.65) in comparison the non-smoker. Risk of treatment default was quite higher in female smokers in comparison the male smokers. Conclusion: Smoking should be prevented to improve the tuberculosis treatment adherence

Direct spectral distribution characterisation using the Average Photon Energy for improved photovoltaic performance modelling

Accurate photovoltaic (PV) performance modelling is crucial for increasing the penetration of PV energy into the grid, analysing returns on investment, and optimising system design prior to investment and construction. Performance models usually correct an output value known at reference conditions for the effects of environmental and system variables at arbitrary conditions. Traditional approaches to correct for the effect of the solar spectrum on performance are based on proxy variables that represent spectral influences, such as absolute air mass (AMa) and clearness index (Kt). A new methodology to account for the spectral influence on PV performance is proposed in this study. The proposed methodology is used to derive a novel spectral correction function based on the average energy of photons contained within the measured solar spectral distribution. The Average Photon Energy (APE) parameter contains information on the combined effects of multiple proxy variables and is not limited by climatic conditions such as cloud cover, as is the case with most traditional models. The APE parameter is shown to be capable of explaining almost 90% of the variability in PV spectral efficiency, compared to around 65% for AMa. The derived APE function is validated and shown to offer an increase of 30% in predictive accuracy for the spectral efficiency compared with the traditional AMa function, and a 17% improvement relative to the AMa-Kt function

A Review on Recent Contribution of Meshfree Methods to Structure and Fracture Mechanics Applications

Meshfree methods are viewed as next generation computational techniques. With evident limitations of conventional grid based methods, like FEM, in dealing with problems of fracture mechanics, large deformation, and simulation of manufacturing processes, meshfree methods have gained much attention by researchers. A number of meshfree methods have been proposed till now for analyzing complex problems in various fields of engineering. Present work attempts to review recent developments and some earlier applications of well-known meshfree methods like EFG and MLPG to various types of structure mechanics and fracture mechanics applications like bending, buckling, free vibration analysis, sensitivity analysis and topology optimization, single and mixed mode crack problems, fatigue crack growth, and dynamic crack analysis and some typical applications like vibration of cracked structures, thermoelastic crack problems, and failure transition in impact problems. Due to complex nature of meshfree shape functions and evaluation of integrals in domain, meshless methods are computationally expensive as compared to conventional mesh based methods. Some improved versions of original meshfree methods and other techniques suggested by researchers to improve computational efficiency of meshfree methods are also reviewed here