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Purpose – The purpose of this research is to study the process conditions that give best yield and expected compositions of liquid smoke products that result during the pyrolisis process relying on predetermined variables.

Design/Methodology/Approach – Pyrolisis process running times are varied, that is, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, and 6 hourly. Condensing temperature maintained remained 25–30 °C. Products identification was applied by using gas chromotography mass spectroscopy.

Findings – Based on the research output, it was concluded that process conditions which give maximum yield were achieved when using double unit condenser (DUC) and time optional four hours, and it provides maximum volume liquid smoke product, and compositions of pyrolisis products. The process also created seven components, namely nepthalene, propanoic acid, 3,7 nanodiena, 2 metilguaiakol, 2-metoksi 4-methyl phenol, 4 ethyl-2 metoksil phenol, oxybanzene. Applying DUC during condensation phase may increase condensing force thereafter obtaining resulted products between 200% and 300% rather than using single unit condenser (SUC).

Research Limitations/Implications – This research was conducted on a fixed batch reactor made of a metal plate with a thickness of 3.0 mm. It carries 200 kg in capacity. In this phase, the moisture of candlenut shells might be kept in 10–12.5% wt. Process temperature applied ranged within 350–500 °C.

Originality/Value – In addition the study increased the theorical of understanding about pyrolisis process and Improving the production of liquid smoke from candlenut shell by pyrolisis process using the method of vapor condensation (Double unit condensor).

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.

Since its introduction by Chari, Kehoe, and McGrattan (2007), Business Cycle Accounting (BCA) exercises have become widespread. Much attention has been devoted to the results of such exercises and to methodological departures from the baseline methodology. Little attention has been paid to identification issues within these classes of models. In this chapter, the authors investigate whether such issues are of concern in the original methodology and in an extension proposed by Šustek (2011) called Monetary Business Cycle Accounting. The authors resort to two types of identification tests in population. One concerns strict identification as theorized by Komunjer and Ng (2011) while the other deals both with strict and weak identification as in Iskrev (2010). Most importantly, the authors explore the extent to which these weak identification problems affect the main economic takeaways and find that the identification deficiencies are not relevant for the standard BCA model. Finally, the authors compute some statistics of interest to practitioners of the BCA methodology.