Identifying Optimal Conceptual Design of a Briquetting Machine

Philip E. Vincent *

Department of Industrial Engineering, Southern Illinois University Edwardsville, United States.

O. M Olabanji

Department of Mechanical Engineering, Federal University of Technology Akure, Nigeria.

P.K Farayibi

Department of Industrial and Production Engineering, Federal University of Technology Akure, Nigeria.

Jegede J. Oluwaseun

Department of Construction Project Management, University of Bolton, UK.

*Author to whom correspondence should be addressed.


Abstract

Briquetting is a mechanical compaction process for increasing the density of bulky materials. Briquette is an example of biomass which is a renewable source of energy. As the world turn to renewable energy due to global warming, depletion of fossil fuel reserves and deforestation. The demand for briquetting machines is on the increase. This high demand and large market for briquetting machines has result in the need for briquetting machines with extended capabilities and design customization. This calls for an elaborate conceptual design phase. This research adopts a fuzzified Multi Criteria Decision Making (MCDM) model, (COPRAS) to identify the optimal conceptual design from four conceptual designs of briquette making machine, operating based on different principles. This was achieved by considering eight (8) design features and their sub features as the criterion to analyze, evaluate and measure the four conceptual designs. The result shows that in applying the COPRAS-F model, several decision makers choice can be factored into the process of choosing an optimal design, from a group of designs and still generate a valid result.

Keywords: Fuzzy COPRAS, multicriteria decision making, briquetting machine design, triangular fuzzy number, design concept selection


How to Cite

Vincent , Philip E., O. M Olabanji, P.K Farayibi, and Jegede J. Oluwaseun. 2024. “Identifying Optimal Conceptual Design of a Briquetting Machine”. Current Journal of Applied Science and Technology 43 (3):45-61. https://doi.org/10.9734/cjast/2024/v43i34359.

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