Biotransformation of fatty acids from renewable wastewater as feedstock to value-added chemicals is a fascinating commercial opportunity. α,ω-dicarboxylic acids (DCAs) are building blocks in many industries, such as polymers, cosmetic intermediates, and pharmaceuticals, and can be obtained by chemical synthesis under extreme conditions. However, biological synthesis can replace the traditional chemical synthesis using cytochrome P450 enzymes to oxidize fatty acids to DCAs. Saccharomyces cerevisiae BY(2R)/pYeDP60-CYP52A17SS (BCM), a transgenic strain expressing the galactose-inducible CYP52A17SS cytochrome P450 enzyme, was able to grow in a coconut milk factory wastewater (CCW) medium and produced 12-hydroxydodecanoic acid (HDDA) and 1,12-dodecanedioic acid (DDA). The supplementation of CCW with 10 g/L yeast extract and 20 g/L peptone (YPCCW) markedly increased the yeast growth rate and the yields of 12-HDDA and 1,12-DDA, with the highest levels of approximately 60 and 38 µg/L, respectively, obtained at 30 °C and pH 5. The incubation temperature and medium pH strongly influenced the yeast growth and 1,12-DDA yield, with the highest 1,12-DDA formation at 30 °C and pH 5–5.5. Hence, the S. cerevisiae BCM strain can potentially be used for producing value-added products from CCW.
Capacity planning, which is integral to resource selection and allocation, is an important task in manufacturing system design. As budget is limited, investment in resources must be done carefully and appropriately in order to achieve desired capacity and system efficiency. One of the most expensive and valuable resources in the manufacturing system is machinery. Therefore, selecting an appropriate machine is of high significance. Among many types of manufacturing systems, intermittent manufacturing system is complex, yet flexible. The intermittent manufacturing system enables manufacturers to produce multiple products using multiple processes and multiple machines. The system comprises a network of multiple types of multi-process machines. A machine selection problem in the context of the intermittent manufacturing system is a delicate process, as it must correspond appropriately with production complexities. To alleviate this problem, we develop a decision support system for solving the machine selection problem in an intermittent manufacturing system design. We study several manufacturing systems producing multiple types of products with various multi-process machines to lay out the framework. This decision support system consists of four main parts, namely; data entry, data conversion, optimization model and report. To solve the machine selection problem, an optimization technique is used to identify the optimal number of machines and the allocation of production process of each machine. The objective of this optimization problem is to minimize total production costs (machine cost and operating cost) while maintaining production capacity and other production constraints. The decision support system is developed for and validated with the manufacturing company.
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