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- W4293202034 abstract "Process waste from pharmaceutical plants is a potential source of emerging contaminants that cannot be effectively destroyed by conventional wastewater treatment technology. In the case of tableting plants, strict product quality standards also preclude the possibility of recycling mechanically defective tablets. Such waste must be handled carefully to ensure that traces of active pharmaceutical ingredients do not end up in ecosystems. In addition to the environmental hazards they pose, such streams also represent lost revenue for pharmaceutical firms. Optimization of process parameters and product formulation can thus achieve concurrent environmental and economic gains. Artificial intelligence and machine learning tools can be used for such optimization measures. In this work, rough set-based machine learning is used to generate a rule-based model for predicting mechanical properties of pharmaceutical tablets as functions of product formulation. The resulting rule-based model provides useful insights on how levels of additives can be optimized to give a product that performs well in the tableting process." @default.
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- W4293202034 date "2022-01-01" @default.
- W4293202034 modified "2023-09-27" @default.
- W4293202034 title "Rough Set Approach to Pharmaceutical Process Waste Reduction" @default.
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- W4293202034 doi "https://doi.org/10.1007/978-981-19-1434-8_5" @default.
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