In this review paper, a brief overview of the increasing applicability of Process Systems Engineering (PSE) tools in two research areas, which are the design of ionic liquids and the design of integrated biorefineries, is presented. The development and advances of novel computational tools and optimization approaches in recent years have enabled these applications with practical results. A general introduction to ionic liquids and their various applications is presented followed by the major challenges in the design of optimal ionic liquids. Significant improvements in computational efficiency have made it possible to provide more reliable data for optimal system design, minimize the production cost of ionic liquids, and reduce the environmental impact caused by such solvents. Hence, the development of novel computational tools and optimization tools that contribute to the design of ionic liquids have been reviewed in detail. A detailed review of the recent developments in PSE applications in the field of integrated biorefineries is then presented. Various value-added products could be processed by the integrated biorefinery aided with applications of PSE tools with the aim of enhancing the sustainability performance in terms of economic, environmental, and social impacts. The application of molecular design tools in the design of integrated biorefineries is also highlighted. Major developments in the application of ionic liquids in integrated biorefineries have been emphasized. This paper is concluded by highlighting the major opportunities for further research in these two research areas and the areas for possible integration of these research fields.

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Summary In this work, a data‐driven rough‐set‐based machine learning model has been proposed as a pre‐processing and predictive modelling tool to predict the pyrolysis bio‐oil properties based on pyrolysis temperature and feedstock characteristics. A database consisting of feedstock proximate and ultimate analyses, pyrolysis temperature, bio‐oil's pH value, and bio‐oil's higher heating value was compiled and used to train the rough‐set‐based machine learning model. The resulting rule‐based rough‐set‐based machine learning model demonstrated promising strength, certainty, and coverage factor. Furthermore, the emergent patterns and mechanistic plausibility of the rough‐set‐based machine learning models were analysed. The generated rules illustrated reasonable predictive capability in estimating the higher heating value and pH value of bio‐oil based on the feedstock characterisation and pyrolysis temperature. Rough‐set‐based machine learning model is thus demonstrated to be a simple and straightforward approach for feedstock composition and pyrolysis temperature selection in pyrolysis/co‐pyrolysis bio‐oil production.

Design and screening of fragrances based on experiments or experiences of specialists can overlook potentially better fragrance products. To overcome this issue, a systematic mathematical programming-based approach is developed for...

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