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The combination of global warming along with increasing energy demand necessitates the importance of improving processes pertaining to the production and consumption of energy in combined heat and power plants. This thesis brings to light transient factors currently burdening process performance for circulating fluidized bed boilers (CFBs) combusting refuse derived fuels (RDFs) and district heating networks (DHN). These two domains are not completely disconnected from one another, which is the case for Northern European countries. Heat can be generated from a central location to be distributed through a network of customers to meet a heating demand. Results show that first-principle modelling techniques have the capacity to capture transients factors associated within the aforementioned entwined energy systems.

On the production side, obtaining real-time information pertaining to the lower heating value of refuse derived fuel affords the ability to implement feed-forward model predictive control. Therefore, feed-forward model predictive control has the potential to minimize combustion temperature swings by making the necessary controls moves before changes in the fuel’s composition are actualized by the process. On the consumption side, attaining a deeper understanding of district heating network dynamics, e.g. heat propagation, network losses, distribution delays, and end-user requirements, introduces the possibility to analyse network performance and reduce peak load production. The perspective of quick network performance can be achieved by an automated approach to building and simulating district heating networks. Nonconventional end-user heating configurations, e.g. homes utilizing district heating and a heat pump, has the potential of illustrating how heating consumption patterns may change over time. Peak load reduction is achievable in district heating networks when it is possible to reduce network supply temperature. This can be achieved by predicting end-user heating requirements and using this information for feed-forward model predictive control.

The overall observations made in this thesis demonstrates that process improvements are obtainable for transient energy systems. Despite the presented work focusing on only one type of energy production and one type of consumption, the approach described unlocks a flexibility that eliminates the need for unambiguous modelling and simulations by allowing for the reusability of model components. The exportability of these models further distinguishes them, as they can be used to test new control approaches within an energy system as real-time predictions within each energy sub-system become more accessible.