Abstract
This paper is a case study review of the timber waste problem and solutions that have been proffered in the Manicaland region of Zimbabwe. The study was conducted through primary data collected during site visits and a review of literature reports on the area. It revealed the absence of up to date quantitative and spatial data on timber waste, with literature reporting at least 70,000 tonnes per annum of sawmill waste. Most of the offcuts and chips are utilized at commercial sawmills for generation of steam for kiln driers, while others are used as firewood by workers and communities. With the growing agricultural hype, most bark is used in tobacco and flower seedbeds, while shavings are used for animal and poultry bedding. Sawdust and shavings represent the most underutilized waste fractions, with heaps scattered all over the region, marring its aesthetic appeal and posing various ecological threats. This waste accumulation, characteristic of timber producing developing nations, defies global best practices in the timber industry which have seen disposed waste reduce to ~1% after uptake by downstream industries like engineered wood products, pulp, paper and cogeneration. Efforts have been made to valorise the waste by making briquettes and compatible stoves, however, they have not been properly supported and promoted for a significant domestic uptake. Recommendations include use of briquettes for CHP at centralized or recommended sites, support of domestic cooking initiatives and consideration of biogas and biofuel potential alternatives, with the support of comparative cost benefit and feasibility studies.
Original language | English |
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Pages (from-to) | 419-429 |
Number of pages | 11 |
Journal | Procedia Manufacturing |
Volume | 35 |
DOIs | |
Publication status | Published - 2019 |
Event | 2nd International Conference on Sustainable Materials Processing and Manufacturing, SMPM 2019 - Sun City, South Africa Duration: Mar 8 2019 → Mar 10 2019 |
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
- Artificial Intelligence