Welcome to the Glass Age

117 a flexible design independent of energy source, melting at times with 80% fossil fuel/H 2 and 20% electric boost (at 3 MJ/kg), or conversely 80% boost and 20% combustion (at 2.5 MJ/kg). This should reduce the risks of adopting a new technology. Figure 7.6 shows the concept design of such a horizontal hybrid electric melter. Hybrid electric melting and oxy-gas furnace such as this can break the magic energy barrier undercutting a specific energy consumption of 3 GJ/ton of glass (with 70-80% cullet). Table 7.1 shows that using electric energy directly in the glass melt is much more efficient than hydrogen whether by combustion or via the fuel cell. Direct efficiency is estimated to be 79%, whereas hydrogen reduces efficiency below 30%. Dark factory with smart glass furnaces or Industry 4.0 (UN Goal 9) Since 2020, new technologies such as neural networks have generated opportunities for automation that were impossible before. As consumers we see it first-hand in self-driving vehicles. If automation is pursued for furnaces, forehearths, and perhaps the complete production it becomes possible to switch lighting off and create so called “dark factories”. Without doubt, the term Industry 4.0 created during a Hannover Messe in 2011 has awakened modern industry to the coming revolution. The last decade has seen the glass industry work diligently to optimize systems, but more is required. Realistically, production in 2030 will need far less human intervention than now. Industry 4.0, often referred to as Big Data or the Internet of Things, refers to high levels of automation of individual parts of production and intimate communication between them. For example, if defect levels increase, then the system itself decides how to react. It might increase or reduce the furnace temperature, whichever is appropriate. Such decisions currently depend on human interpretation and experience. We review next the automation already used in the glass industry and investigate new technologies such as artificial intelligence (AI), neural networks, machine learning, deep learning Renewable source Electricity Hydrogen electric Hydrogen combustion Renewable source 100% 100% 100% Electrolyzer 70% 70% Compressor 92% 92% Transportation 92% 98% 98% Transformer/fuel cell 95% 52% Heat losses effect (electrode holders, fluegas) 90% 90% 45% Total 79% 30% 28% Table 7.1. Comparison of electric melting efficiency versus hydrogen route.