A supervisory control system, based on controlled automata concepts, is developed for the automated manufacturing system of the Atelier Inter6tablissement de Productique (AIP, Dauphin&-Savoie, France). The approach adopted offers important advantages over other existing iipproaches: mainly (i) the resulting controlled behaviours are guaranteed not to contradict the behavioural specifications itnd to be nonblocking, (ii) the controlled behaviours are maximally permissive within the behavioural specifications. The implementation considers modular supervision under partial observation.Automaton models examples of the manufacturing system are provided together with two supervisors. 'Off-the-shelf industrial control systems were used in the implzmentation.

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Rationale behind multi-objective design of low-carbon hydrogen supply chains.• Competition of several solutions for low-carbon hydrogen production.• Competition and interplay between hydrogen, biomass and CO 2 networks.• Impact of CO 2 value chain features on optimal design of hydrogen supply chains.• Impact of biomass availability on optimal design of hydrogen supply chains.

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Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Indonesia, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.