Research Article
Development and Performance Evaluation of Manual Operated Coffee Bean Demucilager
Rebira Wirtu*
,
Tolasa Berhanu
Issue:
Volume 11, Issue 1, February 2026
Pages:
1-7
Received:
27 September 2025
Accepted:
6 November 2025
Published:
13 April 2026
DOI:
10.11648/j.ajmie.20261101.11
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Views:
Abstract: Coffee remains Ethiopia's top export commodity and is the most significant crop in the country's economy. The crop's production and quality are extremely low, despite its economic significance. The decline in Ethiopian coffee quality is mostly caused by improper postharvest processing methods, such as harvesting immature cherries, not sorting during grading and processing, improper drying without considering drying time, drying place, layer thickness, and drying material, transportation, storage, over-fermentation, etc. The processing of coffee beans is a critical step that influences the quality of the final product. Effective demucilaging is essential to remove mucilage while preserving the integrity of the beans. This study focuses on the development and performance evaluation of a manually operated coffee bean demucileger, aimed at improving the efficiency of coffee processing while minimizing operational costs. The objective was to design a user-friendly, effective demucilaging device for small-scale coffee production settings. The demucileger was constructed using locally sourced materials, and its performance was evaluated based on parameters such as efficiency, ease of operation, and quality of demucilaged beans. Results indicated that the device successfully removed mucilage with an efficiency rate and washing capacity of 86% and 86.86 kg/hr. while maintaining the integrity of the beans. This manual operated demucileger presents a viable solution for smallholder coffee farmers, potentially enhancing their productivity and product quality in an economically sustainable manner.
Abstract: Coffee remains Ethiopia's top export commodity and is the most significant crop in the country's economy. The crop's production and quality are extremely low, despite its economic significance. The decline in Ethiopian coffee quality is mostly caused by improper postharvest processing methods, such as harvesting immature cherries, not sorting durin...
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Research Article
Advanced Manufacturing for Waste Reduction: Leveraging Industry 4.0 Data Analytics for Environmental Sustainability
Issue:
Volume 11, Issue 1, February 2026
Pages:
8-22
Received:
14 February 2026
Accepted:
2 March 2026
Published:
16 April 2026
DOI:
10.11648/j.ajmie.20261101.12
Downloads:
Views:
Abstract: Industrial waste, leftover materials and chemical residues constitute a major environmental challenge in Bangladesh where the textile industry annually produces some 400,000 tons of fabric waste that is a significant source of pollution. Manufacturing 4.0 technologies are making possible advanced manufacturing systems that can optimize production and reduce waste, using technologies such as those supported on data analytics and the Internet of Things (IoT). The objective of this study is to build machine-learning based predictive analytics framework for minimizing textile production waste, evaluate the developed framework using a practical context in Bangladesh and finally observe the environmental and socio-economic impact caused by the approach. The design employed a mixed-methods case study. Data were collected from a medium-sized textile dye-house in Dhaka from January to March 2025, with IoT tracked measurements on fabric consumption, machine productivity, and wastewater output (n = 1,000 production cycles). Python generates a Random Forest regression model to predict waste, while simulation is carried out through a digital twin to optimize production parameters. The model obtained a mean absolute error of 5.4% and was able to accurately predict the pattern of waste. Application of the optimized parameters resulted in 20% less fabric waste (from 500 to 400 kg/day), 15% less use of water in dyeing (from 10,000 to 8,500 liters/day) and 10% lower CO₂ emission (0.5 tons/day). The greatest waste reduction was observed in the urban area, due to better cutting techniques. These findings highlight the opportunities provided by Industry 4.0 analytics for sustainable manufacturing towards UN SDG 12. Additional investigation is also required on low-cost IoT deployment and policy enablers, to achieve widespread adoption and impactful change sustainably in developing economies.
Abstract: Industrial waste, leftover materials and chemical residues constitute a major environmental challenge in Bangladesh where the textile industry annually produces some 400,000 tons of fabric waste that is a significant source of pollution. Manufacturing 4.0 technologies are making possible advanced manufacturing systems that can optimize production a...
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