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12. A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Author

Olatunji, Obafemi O., Akinlabi, Stephen, Madushele, Nkosinathi, Adedeji, Paul A., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Vijayan, S., editor, Subramanian, Nachiappan, editor, and Sankaranarayanasamy, K., editor

Published
2021
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13. Spatial Location of Renewable Energy Plants: How Good Is Good Enough?

Author

Adedeji, Paul A., Akinlabi, Stephen, Madushele, Nkosinathi, Olatunji, Obafemi O., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Vijayan, S., editor, Subramanian, Nachiappan, editor, and Sankaranarayanasamy, K., editor

Published
2021
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14. Preparation, Characterization, Image Segmentation and Particle Size Analysis of Cow Bone Powder for Composite Applications

Author

Ikumapayi, O. M., Akinlabi, Esther Titilayo, Adedeji, Paul A., Akinlabi, S. A., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Vijayan, S., editor, Subramanian, Nachiappan, editor, and Sankaranarayanasamy, K., editor

Published
2021
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19. Image Segmentation and Particle Size Analysis of Vibratory Disc Milled Titanium Alloy Powder

Author

Ogbonna, Okwudili S., Akinlabi, Stephen A., Madushele, Nkosnathi, Adedeji, Paul A., Ekolu, Stephen, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Emamian, Seyed Sattar, editor, Awang, Mokhtar, editor, and Yusof, Farazila, editor

Published
2020
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20. Potential Roles of Artificial Intelligence in the LCI of Renewable Energy Systems

Author

Adedeji, Paul A., Akinlabi, Stephen A., Madushele, Nkosinathi, Olatunji, Obafemi O., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Emamian, Seyed Sattar, editor, Awang, Mokhtar, editor, and Yusof, Farazila, editor

Published
2020
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21. Energy Content Modelling for Municipal Solid Waste Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Author

Adeleke, Oluwatobi, Akinlabi, Stephen A., Adedeji, Paul A., Jen, Tien-Chien, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Emamian, Seyed Sattar, editor, Awang, Mokhtar, editor, and Yusof, Farazila, editor

Published
2020
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27. Forecasting Electricity Generation from a Renewable and Low‐Carbon Matrix Using the Stochastic Volatility Process.

Author

Reichert, Bianca, Martins, Tailon, Souza, Adriano Mendonça, and Adedeji, Paul

Subjects
ENERGY industries, STOCHASTIC matrices, ELECTRIC power production, PETROLEUM as fuel, ENERGY consumption
Abstract

The need to replace nonrenewable sources is a global challenge, especially for emerging countries with financial constraints to invest in energy transition. In Brazil, electrical generation is predominantly renewable, since the hydraulic source is responsible for more than half of the domestic electricity supply. Other alternative sources, such as biomass, solar, and wind, complement the renewable portion of the Brazilian electrical matrix. However, periods of drought and climate disturbances threaten the stability of the electrical system and increase dependence on thermal generation. Based on the global movement to intensify the use of renewable sources, which are volatile and influenced by climate variations, the objective of this research is to predict electricity generation using the Heston model. This model, originally applied to predict option prices, assumes nonconstant volatility and has the ability to estimate the average value including the stochastic volatility process. The database is made up of monthly values of the volume of electrical energy generation from biomass, coal, fuel oil, hydraulic, industrial waste, natural gas, nuclear, solar, and wind sources. Compared to the Black‐Scholes model, the chosen method proved to be superior in predicting the generation from coal, hydropower, and solar energy, according to the MAE, MAPE, and RMSE statistics. We conclude that a financial model can be effectively applied to the energy sector and the Heston model can be a useful instrument to assist in the planning and management of the national electrical system. One suggestion for the Brazilian case is to encourage the use of alternative energies and low‐carbon sources, such as solar, wind, and natural gas, providing greater energy security and a more reliable electrical system. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Investigating Work Delegation and Employee Efficiency in an Engineering Maintenance Service Department.

Author

Sabani, Andile, Adedeji, Paul A., Akinlabi, Esther T., and Akinlabi, Stephen A.

Subjects
INFORMATION technology, JOB satisfaction, ENGINEERING firms, SERVICE departments, MINERAL processing
Abstract

Engineering-related maintenance services of organizations are often non-deterministic, and supervisory delegation of work is often inevitable. Employees are expected to be equipped with the necessary capacity for delegated responsibilities. However, there is a need for an investigation of the quality of delivery of delegated responsibilities and the disposition of staff to available multiple efficiency-enhancing tools before a conclusive need for more workforce. This study investigates the performance quality of delegated responsibilities and staff disposition to use technological tools for productivity enhancement. Interview and questionnaire approaches were adopted. 100% sample size was considered for all the Engineering Maintenance Service department employees of one of the leading minerals processing and metallurgical engineering products companies in South Africa. This study investigated the degree to which work is delegated, the quality of performance of delegated work, a correlation between work delegation and quality of service delivery, the need for more personnel, work execution and staff resistance to information technology, and provided machine tools. The need for more effective tools and the preference for manual methods over more efficient methods were also investigated to accommodate staff feedback on work carried out. From the results obtained, 97% and 93.1% of respondents showed that work is delegated and performed satisfactorily, respectively. However, statistical measures show that some staff do not carry out work to the expected satisfaction (p=0.001<<0.05). While 82.8% of the employees attested to the need for an increased workforce in the department, staff's use of information technology for work is less than 80%. Though 96.5% of respondents showed that machine tools provided are always used, 65.5% of staff still preferred the use of manual tools, thus impeding work efficiency. A strong correlation existed between tool efficiency and the use of manual efforts (p=0.007 << 0.05) as well. Hence, periodic staff training is necessary for improved efficiency in delegated duties. Though a larger percentage of the workforce advocates for more staff, improving the current workforce's effectiveness would increase the organization's overall productivity without recruiting more staff. [ABSTRACT FROM AUTHOR]

Published
2024
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