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Traditionally, the design of new chemical products for specific applications is done by using a combination of design heuristics, experimental studies and expert judgements. In addition to the conventional methods, chemical products can also be designed by using computer-aided molecular design (CAMD) techniques. Based on CAMD, optimal chemical products can be designed by identifying the molecule with the best properties that correspond with the target functionalities of the product. In general, the optimality of product property (termed as property superiority) is the only factor considered while designing optimal products by using CAMD techniques. However, it is noted that property prediction models are developed with certain accuracy and uncertainties. As the accuracy of property prediction models (termed as property robustness) can affect the effectiveness of CAMD techniques in predicting the product property, the effects of property prediction uncertainty have to be considered while applying CAMD techniques. This paper presents a systematic fuzzy optimisation based molecular design methodology. The methodology is developed for the design of optimum molecules used in chemical processes by considering and optimising both property superiority and robustness. Property superiority is quantified by property optimality. Meanwhile, property robustness is expressed by the standard deviation of the property prediction model, which is a measure of average variation between the experimental data and estimated values of product property using property prediction model. Fuzzy optimisation approach is extended in this work to address and trade off property superiority and robustness simultaneously. Molecular design technique is adapted in this work to identify the optimal molecular structure which satisfies multiple product specification. To illustrate the proposed method, a case study is presented where optimal solution is selected based on how much the solution satisfied the criteria of property superiority and robustness. [ABSTRACT FROM AUTHOR]

Biomass is a sustainable source of energy which can be utilised to produce value-added products such as biochemical products and biomaterials. In order to produce a sustainable supply of such value-added products, an integrated biorefinery is required. An integrated biorefinery is a processing facility that integrates multiple biomass conversion pathways to produce value-added products. To date, various biomass conversion pathways are available to convert biomass into a wide range of products. Due to the large number of available pathways, various systematic screening tools have been developed to address the process design aspect of an integrated biorefinery. Process design however, is often inter-linked with product design as it is important to identify the optimal molecule (based on desired product properties) prior to designing its optimal production routes. In cases where the desired product properties cannot be met by a single component chemical product, a mixture of chemicals would be required. In this respect, product and process design decisions would be a challenging task for an integrated biorefinery. In this work, a novel two-stage optimisation approach is developed to identify the optimal conversion pathways in an integrated biorefinery to convert biomass into the optimal mixtures in terms of target product properties. In the first stage, the optimal mixture is designed via computer-aided molecular design (CAMD) technique. CAMD technique is a reverse engineering approach which predicts the molecules with optimal properties using property prediction models. Different classes of property models such as group contribution (GC) models and quantitative structure property relationship (QSPR) are adapted in this work. The main component of the mixture is first determined from the target product properties. This is followed by the identifying of additive components to form an optimal mixture with the main component based on the desired product properties. Once the optimal mixture is determined, the second stage identifies the optimal conversion pathways via superstructural mathematical optimisation approach. With such approach, the optimal conversion pathways can be determined based on different optimisation objectives (e.g. highest product yield, lowest environmental impact etc.). To illustrate the proposed methodology, a case study on the design of fuel additives as a mixture of different molecules from palm-based biomass is presented. With the developed methodology, optimal fuel additives are designed based on optimal target properties. Once the optimal fuel additives are designed, the optimal conversion pathways in terms of highest product yield and economic performance that convert biomass into the optimal fuel additives are identified. [ABSTRACT FROM AUTHOR]