The demand for new novel flavour and fragrance (F&F) molecules has boosted the need for a systematic approach to designing fragrance molecules. However, the F&F-related industry still relies heavily on experimental approaches or on existing databases without considering the consequences resulting from changes in concentration, which could omit potential fragrances. Computer-aided molecular design (CAMD) has great potential to identify novel molecular structures to be used as fragrances. Using CAMD for this purpose requires models to predict the olfaction properties of molecules. A rough set-based machine learning (RSML) approach is used to develop an interpretable predictive model for odour characteristics in this work. New rule-based models are generated from RSML based on the dilution and a number of different topological indices which identify the structure-odour relationship of fragrance molecules. The most prominent rules are selected and formulated as constraints in a CAMD optimisation model. The combination of several rules was able to increase the coverage of different classes of molecules. To model the performance indicators that vary over a range of properties, a disjunctive programming model is also incorporated into the CAMD framework. A case study demonstrates the utilisation of this methodology to design fragrance additives in dishwashing liquid. The results illustrate the capability of the novel RSML and CAMD framework to identify potential fragrance molecules that can be used in consumer products.
The valorisation of biomass by synthesising a multi-biomass corridor can be an optimistic pathway to solving the growing waste management problem. However, the supply chain problem usually involves a massive number of variables, including the connectivity of the sink source and the selection of a technology pathway. In this work, a “Decomposition Approach” was utilised, wherein a P-graph was incorporated with a conventional mathematical model to reduce the number of variables. Although this type of approach is well established with respect to solving biomass supply chain problems, no previous works have comprehensively considered the effect of the maximum allowable travel distance (MATD) on a supply chain model. A case study in Peninsular Malaysia involving oil palm, paddy, and coconut biomass was conducted using the proposed approach. Moreover, a multiple linear regression (MLR) tool for formulating the cost-correlated function based on the best technology pathway obtained from a P-Graph was incorporated. As a result, the net profit of the biomass corridor was estimated to be USD 0.87 billion, with 1.45 × 107 tonnes per year of biomass being sent to 39 processing hubs over a 20-year lifespan. Furthermore, a sensitivity analysis was also conducted to investigate the impact of several cost-related parameters on the net profit.
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