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Thermal processes represent a considerable part of the total energy consumption in manufacturing industry, in sectors such as steel, aluminium, cement, ceramic and glass, among others. It can even be the predominant type of energy consumption in some sectors. High thermal energy processes are mostly associated to high thermal losses, (commonly denominated as waste heat), reinforcing the need for waste heat recovery (WHR) strategies. WHR has therefore been identified as a relevant solution to increase energy efficiency in industrial thermal applications, namely in energy intensive consumers. The ceramic sector is a clear example within the manufacturing industry mainly due to the fuel consumption required for the following processes: firing, drying and spray drying. This paper reviews studies on energy efficiency improvement measures including WHR practices applied to the ceramic sector. This focuses on technologies and strategies which have significant potential to promote energy savings and carbon emissions reduction. The measures have been grouped into three main categories: (i) equipment level; (ii) plant level; and (iii) outer plant level. Some examples include: (i) high efficiency burners; (ii) hot air recycling from kilns to other processes and installation of heat exchangers; and (iii) installation of gas turbine for combined heat and power (CHP). It is observed that energy efficiency solutions allow savings up to 50–60% in the case of high efficiency burners; 15% energy savings for hot air recycling solutions and 30% in the when gas turbines are considered for CHP. Limitations to the implementation of some measures have been identified such as the high investment costs associated, for instance, with certain heat exchangers as well as the corrosive nature of certain available exhaust heat.

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Waste heat recovery is key for the improvement of several industrial sectors. It has the potential to be used as an energy source and, therefore, to reduce the current industrial energy consumption. The ceramic industry, in particular, encompasses significant thermal processes (the firing and drying) which generate substantial waste heat. This work studies the potential of waste heat recovery solutions for a ceramic floor tile plant. Direct re-use of hot air streams from kiln cooling zones as kiln combustion air is considered, as well as its use as drying agent and combustion air in dryers. The proposed strategies achieve natural gas savings up to 33% in the plant, reducing fuel consumption by 25-29% in kilns, by 44% in a dryer, and avoiding fuel consumption in the other dryer. Moreover, the payback period of all these strategies has been estimated as 0.12 years, which proves the attractiveness of waste heat recovery.

This paper presents and explores the different Earth Observation approaches and their contribution to the achievement of United Nations Sustainable Development Goals. A review on the Sustainable Development concept and its goals is presented followed by Earth Observation approaches relevant to this field, giving special attention to the contribution of Machine Learning methods and algorithms as well as their potential and capabilities to support the achievement of Sustainable Development Goals. Overall, it is observed that Earth Observation plays a key role in monitoring the Sustainable Development Goals given its cost-effectiveness pertaining to data acquisition on all scales and information richness. Despite the success of Machine Learning upon Earth Observation data analysis, it is observed that performance is heavily dependent on the ability to extract and synthesise characteristics from data. Hence, a deeper and effective analysis of the available data is required to identify the strongest features and, hence, the key factors pertaining to Sustainable Development. Overall, this research provides a deeper understanding on the relation between Sustainable Development, Earth Observation and Machine Learning, and how these can support the Sustainable Development of countries and the means to find their correlations. In pursuing the Sustainable Development Goals, given the relevance and growing amount of data generated through Earth Observation, it is concluded that there is an increased need for new methods and techniques strongly suggesting the use of new Machine Learning techniques.

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