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Efficient truck flow is essential for the efficient operation of a factory and the distribution of its products. This study demonstrated methods to improve truck loading times and overall efficiency at a major sugar manufacturing facility in the Middle East. The objective was to reduce truck waiting times at loading units and increase capacity. The data were collected through questionnaires, observations, and interviews with stakeholders. A simulation software was employed to analyze truck activity at loading stations at the factory. Multi-criteria decision making (MCDM) tools, AHP and TOPSIS, addressed five primary criteria and nine sub-criteria to assist in identifying, evaluating, and ranking feasible solutions. The study suggested different utilization of the various factory loading platforms at different times of the day. The findings from this study emphasize the importance of simulation-based approaches supplemented with decision-making processes to improve efficiency in sugar manufacturing facilities that may have broader applications in the factories of other industries. The study highlights remarkable improvements in operational efficiency, as seen in Alternative 4 substantial 27.9% enhancement, resulting in cost savings and time efficiency. By implementing these findings, factories can enhance their truck flow management system, reduce waiting times, increase capacity utilization, optimize resource allocation, and improve overall efficiency. [ABSTRACT FROM AUTHOR]

Efficient truck flow is essential for the efficient operation of a factory and the distribution of its products. This study demonstrated methods to improve truck loading times and overall efficiency at a major sugar manufacturing facility in the Middle East. The objective was to reduce truck waiting times at loading units and increase capacity. The data were collected through questionnaires, observations, and interviews with stakeholders. A simulation software was employed to analyze truck activity at loading stations at the factory. Multi-criteria decision making (MCDM) tools, AHP and TOPSIS, addressed five primary criteria and nine sub-criteria to assist in identifying, evaluating, and ranking feasible solutions. The study suggested different utilization of the various factory loading platforms at different times of the day. The findings from this study emphasize the importance of simulation-based approaches supplemented with decision-making processes to improve efficiency in sugar manufacturing facilities that may have broader applications in the factories of other industries. The study highlights remarkable improvements in operational efficiency, as seen in Alternative 4 substantial 27.9% enhancement, resulting in cost savings and time efficiency. By implementing these findings, factories can enhance their truck flow management system, reduce waiting times, increase capacity utilization, optimize resource allocation, and improve overall efficiency.

The cogeneration system in sugar factory uses bagasse with high moisture content as the fuel for the boiler, which results in low boiler efficiency. The system also produces superheated steam, which is extracted from the turbine, and mixed with cooling water to produce saturated steam required by the evaporation system. The potential use superheated steam to reduce bagasse moisture content is ignored in the standard practice of the sugar factory. In this article, an investigation is made into the improvement of the cogeneration system by using superheated steam dryer to reduce the moisture content of bagasse. Mathematical models are developed for the typical system without superheated steam dryer and the improved system with superheated steam dryer. They are then used to compare the performances of both systems. It is found that, under the condition that the required steam flow rate for the evaporation process and the power output are the same, the improved system requires less bagasse consumption, and has larger energy utilization factor. In addition, water that would be lost with flue gases in the typical system is recovered in the improved system. [ABSTRACT FROM AUTHOR]

Copyright of Listy Cukrovarnicke a Reparske is the property of VUC Praha a.s. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Listy Cukrovarnicke a Reparske is the property of VUC Praha a.s. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Process design and operation is concerned with the optimal selection and efficient utilization of resources along time. The operational efficiency of equipment units depends strongly on the maintenance policy employed. This work addresses critical operational issues in the sugar cane industry such as the problem of determining the optimal cyclic cleaning policy in the evaporation section and the corresponding optimum steam consumption profile of both evaporation and crystallization sections. A main feature of this problem is the performance decay with time of each evaporation unit which must be restored by appropriate cleaning operations. in this paper, a detailed mixed integer nonlinear programming (MINLP) performance model which includes the effect of fouling on the overall heat-transfer coefficient is considered. The problem formulation can also handle multiple-unit parallel evaporation lines. Problem solution provides for each production line the optimal cleaning schedule, mass flow to be processed, and vapor bleeds. [Copyright &y& Elsevier]