In this study, it is aimed to conduct the thermodynamic and economic analysis of solar thermal power plants using parabolic trough collectors (PTC), linear Fresnel reflectors (LFR) and solar tower (ST) technologies for Cameroon. The analysis is performed for each power plant with the installed capacity of 5 MWe. Initial investment costs for the solar thermal power plants using PTC, LFR and ST technologies are estimated to be 33.49 Million USD, 18.77 Million USD and 36.31 Million USD while levelized costs of electricity (LCOE) are found to be varying from 145.6 USD/MWh to 186.8 USD/MWh, 112.2 USD/MWh to 154.2 USD/MWh and 179.2 USD/MWh to 220.4 USD/MWh, respectively. For the solar thermal power plants using PTC, LFR and ST technologies, payback periods are obtained to be 6.57 years, 6.84 years and 6.02 years, and also, internal rates on the return are calculated to be 21.03%, 20.42% and 22.47%, respectively. Overall energy and exergy efficiency values are found to be 13.39% and 14.37%; 11.90% and 13.74%; 12.13% and 13.64% for the solar thermal power plants using PTC, LFR and ST technologies, respectively. In conclusion, it is seen that LFR technology presents the best performance with the combination of thermodynamic and economic metrics for the deployment of solar thermal power plants in the countries in sub-Saharan Africa like Cameroon.
This study investigates the use of heat recovery steam generators (HRSGs) connected to three types of concentrated solar power (CSP) technologies for solar thermal power plants for the city of Faro-Poli located in northern Cameroon. The HRSGs are designed for the solar thermal power plants using solar tower (ST) technology as HRSG-1 and using parabolic trough collector (PTC) and linear Fresnel reflector (LFR) technologies as HRSG-2. HRSG-1 operates under temperatures between 56.4 and 314.9 °C, whereas HRSG-2 (PTC) and HRSG-2 (LFR) work under temperatures between 57.9 and 264 °C. The exergoeconomic analysis reveals that costs per exergy unit of the solar field system vary from 2.31 to 5.32 $/GJ and that relative cost differences and exergoeconomic factors of HRSG-1 (ST), HRSG-2 (PTC), and HRSG-2 (LFR) are 0.086 and 90.0%, 0.063 and 85.2%, and 0.112 and 72.0%, respectively. The results also show that costs per exergy unit of the HRSGs connected to these three CSP technologies are between 2.41 and 8.41 $/GJ. The avoidable-endogenous exergy destruction values are 158.5, 498.8, and 570.4 kW for HRSG-1 (ST), HRSG-2 (PTC), and HRSG-2 (LFR), respectively. Further, it is seen that HRSG-1 owns the lowest levelized cost rate of product with a value of 0.36 $/h, while the other two HRSG-2 technologies have a value of 1.08 $/h. Finally, the sensitivity analysis shows the cost reduction potential of the HRSGs to make them economically viable. It is concluded that the HRSG-1 (ST) is the most efficient technology considering its impact on the overall exergy efficiency, the levelized cost rate of product, and the cost per exergy unit.
The accuracy of a knowledge extraction algorithm in a large database depends on the quality of the data preprocessing and the methods used. The massive amounts of data that we collect every day are putting storage capacity at a premium. In reality, many databases are characterized by attributes with outliers, redundant, and even more missing values. Missing data and outliers are ubiquitous in our databases, and imputation techniques will help us mitigate their influence. To solve this problem, as well as the problem of data size, this paper proposes a data preprocessing approach based on the k-nearest neighbor (KNN) completion for imputation of missing data and principal component analysis (PCA) for processing redundant data, thus reducing the data size by generating a significant quality sample after imputation of missing and outlier data. A rigorous comparison is made between our approach and two others. The dissolved gas data from Rio Tinto Alcan’s transformer T0001 were imputed by KNN, where k equals 5. For 6 imputed gases, the average percentage error is about 2%, 17.5% after average imputation, and 23.65% after multiple imputations. For data compression, 2 axes were selected based on the elbow rule and the Kaiser threshold.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.