Cassava starch processing at small scale in North Vietnam
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Permanent link to cite or share this item: https://hdl.handle.net/10568/43247
In Northern Vietnam, small-scale cassava starch processing is conducted in densely populated craft villages, where processors face difficulties to expand their activities. Three different processing systems were studied among a cluster of three communes in the Red River Delta, producing up to 430 t of starch (at 55% dry matter) per day. The first system, type A, is a cylindrical rasper and a manual sieve, the second, type B, is a cylindrical rasper and stirring-filtering machine and the third, type C, used equipment for both the rasping and filtering stages. Moisture, starch, crude fibers and ash content analysis were carried out on samples collected from the A-B-C manufacturing processes to establish the mass balance of starch. Production capacity, water consumption, electrical requirements and capital-labor costs per tonne of starch (12% moisture) were also reported. A-B-C manufacturing processes enabled 75% recovery of the starch present in fresh roots. No significant change was observed in the composition of starch. Upgrading from system A to B and subsequently to C resulted in an increase in the extraction capacities (up to 0.9 t of peeled roots per hour), the extraction efficiencies during the extraction stage (up to 93%), and an increase in the water consumption and electrical power (up to 21 m3 and 55 kWh per tonne of starch, respectively). The highest amount of total solids carried in the waste-water was obtained with type C (up to 17% of the dry weight of fresh roots, compared to 10% and 13% for type A and B, respectively). This may lead to a higher chemical oxygen demand (COD) and biological oxygen demand (BOD) in waste-water, which can result in more polluted waste-water than compared with the type A and B technologies. Upgrading the rasping-extraction technologies also resulted in higher profits and reduction of labor per tonne of starch (up to 18 US$ and 26 man-hours respectively). The diagnosis proposed in this study can be applied in different contexts to recommend technological options by considering space, energy and capital-labor availabilities.
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