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A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.

A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.

The strong interaction between electric vehicles and the charging infrastructure presents new challenges towards system-level optimization. Moreover choices between the power level, duration, and frequency of charging events can have direct impact both on energy efficiency, as well as the overall lifetime of vehicles. This paper presents a preliminary study into the optimization scheme for such systems, with the focus on an electric bus application. The results illustrate an energy saving when comparing the EMS and baseline strategy of ~ 1.6%improvement measured in kWh/km, alongside a potential cycle life prolongation factor improvement of the EMS compared to the baseline is 1.5 in average.

The strong interaction between electric vehicles and the charging infrastructure presents new challenges towards system-level optimization. Moreover choices between the power level, duration, and frequency of charging events can have direct impact both on energy efficiency, as well as the overall lifetime of vehicles. This paper presents a preliminary study into the optimization scheme for such systems, with the focus on an electric bus application. The results illustrate an energy saving when comparing the EMS and baseline strategy of ~ 1.6%improvement measured in kWh/km, alongside a potential cycle life prolongation factor improvement of the EMS compared to the baseline is 1.5 in average.

This chapter investigates how the institutional context influences the business models for community energy projects, which often intend to enable community members to gain some level of influence, control and ownership over the energy system. We analyse the viability of these projects, by investigating the concrete opportunities for community-based Virtual Power Plant projects in two countries (the Netherlands and Ireland) and how these are shaped by incumbent institutional arrangements at EU, national and local levels. The analysis is structured using the constitutive elements of a business model can-vas as a heuristic. The results show that in order to survive in the energy services market (which is necessary for economic viability on the longer term) community energy initia-tives are forced to partially abandon their community logic-based business model. We discuss how current institutional arrangements are not conducive to a more inclusive en-ergy system and how this could be improved.

This chapter investigates how the institutional context influences the business models for community energy projects, which often intend to enable community members to gain some level of influence, control and ownership over the energy system. We analyse the viability of these projects, by investigating the concrete opportunities for community-based Virtual Power Plant projects in two countries (the Netherlands and Ireland) and how these are shaped by incumbent institutional arrangements at EU, national and local levels. The analysis is structured using the constitutive elements of a business model can-vas as a heuristic. The results show that in order to survive in the energy services market (which is necessary for economic viability on the longer term) community energy initia-tives are forced to partially abandon their community logic-based business model. We discuss how current institutional arrangements are not conducive to a more inclusive en-ergy system and how this could be improved.