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Modelling sugarcane quality in the context of mill scale supply chain logistics.

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dc.contributor.advisorBezuidenhout, Carel Nicolaas.
dc.contributor.advisorOrtmann, Gerald Friedel.
dc.contributor.authorJenkins, Edwin Peter Garland.
dc.date.accessioned2015-10-23T07:37:36Z
dc.date.available2015-10-23T07:37:36Z
dc.date.created2014
dc.date.issued2014
dc.descriptionM. Sc. Agric. Eng. University of KwaZulu-Natal, Pietermaritzburg 2014.en
dc.description.abstractThe length of milling season (LOMS) refers to the length and timing of sugarcane crushing operations at a sugar mill. LOMS is central to the competitiveness and profitability of any sugar mill supply area (MSA). Conflicting interests between supply chain stakeholders can make adjusting the LOMS difficult. The LOMS should take into account weather conditions, cane quality, milling capacity, supply chain capabilities and other interrelated issues, such as agronomics. Previous LOMS models in South Africa were developed over a decade ago and there was scope to improve the calculation of risks by using a stochastic modelling approach. Recently, a stochastic model named LOMZI was developed to evaluate stockpiling options at Umfolozi. In this study, LOMZI was adapted and expanded to allow the LOMS for any MSA in South Africa to be investigated. However, mill area specific applications of the updated model fell outside the scope of this study. As it currently stands, LOMZI simulates a sugarcane supply chain from the point where sugarcane is cut, up to delivery at the mill. During the process of adapting LOMZI, the simulation of sugarcane quality was identified as an important area for improvement in the model and this became the focus of the study. A predictive MSA scale cane quality model was developed, based on recent weather conditions and a mechanistic understanding of sugarcane quality. The quality model was developed to simulate the daily average brix %, pol % and fibre % of sugarcane delivered to the mill. The preceding 11 weeks’ rainfall and temperature values were used to predict cane quality. A total of 98 mill-specific coefficients were calibrated from historic milling data and, for model demonstration purposes, the quality model was applied at two mills, namely Sezela and Umfolozi. Independent verifications yielded R2 values between 0.56 and 0.74. A useful method to estimate the average burn/cut to crush delay for a MSA was also identified. The quality model has been successfully integrated with LOMZI. Future work is envisaged to expand LOMZI and to model the operations of sugar mills and the RV cane payment system.en
dc.identifier.urihttp://hdl.handle.net/10413/12542
dc.language.isoen_ZAen
dc.subjectSugar factories--South Africa.en
dc.subjectSugarcane industry--South Africa.en
dc.subjectSugar--Manufacture and refining--South Africa.en
dc.subjectSugarcane--Harvesting--South Africa.en
dc.subjectSugarcane--Milling--South Africa.en
dc.subjectTheses--Agricultural engineering.en
dc.titleModelling sugarcane quality in the context of mill scale supply chain logistics.en
dc.typeThesisen

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Masters Degrees (Agricultural Engineering)