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Devrim Murat Yazan, Guido van Capelleveen and Luca Fraccascia
The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the…
Abstract
The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?
This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.
Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.
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Elvitriana, Erman Munir, Delvian and Hesti Wahyuningsih
Purpose – This study aims to obtain the best lipid content in locally isolated microalga that grows in palm oil mill effluent (POME).Design/Methodology/Approach – Microalgae were…
Abstract
Purpose – This study aims to obtain the best lipid content in locally isolated microalga that grows in palm oil mill effluent (POME).
Design/Methodology/Approach – Microalgae were cultured in POME with 25% dilution (LP25), 50% (LP50), and no dilution (LP) in 1,500 ml glass vessel at room temperature using a lighting intensity of 13,000 lux and continuous aeration for 24 hours and 12 hours, respectively. The biomass (in dry weight) of microalgae was analyzed daily by means of spectrophotometry using 624 nm wavelength to determine their growth.
Findings – The results showed that the acclimatized growth of microalgae in POME media adapted faster to the POME concentration. Acclimatized biomass content tends to increase to 1.014 g/L, while the content of non-acclimatized biomass reached only 0.752 g/L. Lipid content resulting from the direct extraction process using the modified Bligh and Dyer method provided the best yield of 47% in the microalgae grown in the LP50 POME medium.
Research Limitations/Implications – Lipid was produced from locally isolated microalgae cultured in POME medium with 25% dilution (LP25), 50% (LP50), and no dilution (LP).
Practical Implications – The lipid produced had the potential for biodiesel energy.
Originality/Value – In this study, microalga was used not only to treat POME liquid wastes but also to produce lipids as biodiesel energy potentials.