Model Predictive Control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems.To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The novelty of this POD model reduction-based MPC is to apply POD's highly efficient linear decomposition and convert detailed space-state model to reduced model with function of only 1 dimensional in time. The stabilization/destabilisation of a tubular reactor with recycle is used as an illustrative
An MILP model developed for life cycle optimisation of electricity supply up to 2060. The model can optimise on costs and a number of life cycle environmental impacts. Optimising on costs alone leads to suboptimal solutions. Optimising on GWP reduces both costs and other environmental impacts. Multi-objective optimisation crucial for identifying sustainable electricity futures.
a b s t r a c tA multi-period mixed-integer linear programming model has been developed to help explore future pathways for electricity supply where costs and carbon reduction are a priority. The model follows a life cycle approach and can optimise on costs and on a number of environmental objectives. To illustrate the application, the model has been optimised on two objectives: whole system costs and global warming potential (GWP) using the UK as an example. Four different scenarios have been considered up to 2060, each assuming different electricity demand and carbon reduction targets. When optimising on system costs, they range from £156.6 bn for the least carbon-constrained scenario with moderate increase in electricity demand to £269.9 bn for the scenario with high demand and requiring 100% decarbonisation of electricity supply by 2035. In optimisation on GWP, negative carbon emissions are achieved in all scenarios, ranging from À0.5 to À1.28 Gt CO 2 eq. over the period, owing to biomass carbon capture and storage. Optimising on the GWP also reduces significantly other environmental impacts at costs comparable to optimised costs. This research shows that meeting carbon targets will require careful planning and consideration of objectives other than costs alone to ensure that optimal rather than suboptimal solutions are found for a more sustainable electricity supply.
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