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Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning-based data analysis is necessary to find the relationship between biochar production parameters and feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11–14 wt%, volatile matter of 60–62 wt% and carbon content of 42–45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C–475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield. [ABSTRACT FROM AUTHOR]

Household products such as dishwasher products and multipurpose cleaners may contain specific chemical ingredients to meet the consumer needs. However, some of the ingredients may result in skin and respiratory irritation. Thus, a systematic methodology to estimate the extent of hazard and risk for consumers’ exposure to the products is needed. In this work, an index-based methodology is presented to estimate the severity of the hazards and risks of the ingredients at during the early stage of product design. Higher score was assigned to the higher potential of hazard and risk, and vice versa. The hazard potential was determined based on hazard classification by the Global Harmonised System (GHS). Risk assessment was performed by considering the Margin of Exposure (MOE) and Risk Characterization Ratio (RCR). To demonstrate the proposed methodology, the dermal and inhalation hazards as well as risks from ingredients used in formulation of liquid detergent were evaluated.

The growing importance of the membrane-based air separation processes results in an increasing demand for suitable polymeric membrane structures. This has spurred the interest in designing polymer structures for O2/N2 separation by employing a systematic approach. In this work, a computer-aided molecular design (CAMD)-based framework was developed to identify promising structures of polymers that can be used for air separation. To incorporate constraints in CAMD, the rough set-based machine learning (RSML) method was implemented to establish predictive models for the physical and transport properties of polymer owing to its interpretability. The deterministic rules generated from RSML would be interpreted scientifically reflecting the structure–property relationship to ensure that the molecules generated were feasible according to a scientific point of view. The most prominent rules selected were then integrated as constraints in CAMD. The relevant properties in this framework comprised of glass transition temperature (Tg), molar volume (Vm), cohesive energy (Ecoh), O2 permeability and O2/N2 selectivity. The solutions from CAMD optimisation were demonstrated in case studies. Results indicated the capability of a novel approach in identifying potential polymeric membrane candidates for air separation application that meet the permeability and selectivity requirements. [ABSTRACT FROM AUTHOR]

In this work, a novel machine learning based methodology was developed to predict fragrance from the molecular structure and the effect of the subjects attributes on odour perception. As fragrance is linked to the molecular structure and interactions, topological indices are used to develop a predictive model. Rough set‐based machine learning is used to generate rule‐based models that link the topology of fragrant molecules and dilution to their respective odour characteristics. The results show that the generated models are effective in determining the odour characteristic of molecules. [ABSTRACT FROM AUTHOR]

The global drive to achieve the climate change mitigation targets set during the Paris Agreement and COP26 necessitates sustainable electricity generation from renewable energy sources. However, renewable energy systems face economic challenges due to unattractive feed‐in‐tariffs (FiT). This paper presents a mathematical programming model to optimise the participation and electricity generation by biomass power plants in a FiT programme. A disjunctive fuzzy optimisation approach is adopted in this work due to the presence of several biomass power plants, thus resulting in a multi‐objective optimisation. The newly developed mixed‐integer linear programming (MILP) model maximises the overall satisfaction extent of biomass power plants in power generation. The optimisation model ensures the satisfaction of all biomass power plants' technical and economic constraints. A base case study of seven power plants utilising various palm biomass is used to demonstrate the disjunctive fuzzy optimisation approach for the MILP model. This is followed by a scenario investigating the impact of demand variation. The optimisation of the case studies favours the utilisation of empty fruit bunch (EFB), palm oil mill effluent (POME), and palm mesocarp fibre (PMF) for sustainable electricity generation. Palm kernel shell (PKS) is the least preferred palm biomass due to its lowest net availability of useful energy from biomass combustion. This work provides a decision tool for the selection of biomass type during electricity generation. The MILP model in this work may be readily extended for any number of power plants, handling a variety of biomass feedstocks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Process Systems Engineering (PSE) tools have been instrumental in developing optimal processes and identifying better products based on different functions. Over the years, the global process industry is continually improving in product development and process performances. Another typical challenge encountered in the management of biomass is to synthesize an optimal supply chain of biomass resources to powerplants that can generate energy from biomass. [Extracted from the article]