Tight convex and concave relaxations are of high importance in the field of deterministic global optimization. We present a heuristic to tighten relaxations obtained by the McCormick technique. We use the McCormick su...
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Herein, a framework for deterministic global optimization of process flowsheets is adapted to the design of an organic Rankine cycle for geothermal power generation. A case study using isobutane as working fluid is co...
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Herein, a framework for deterministic global optimization of process flowsheets is adapted to the design of an organic Rankine cycle for geothermal power generation. A case study using isobutane as working fluid is considered for the optimal sizing of components and selection of operating conditions at different ambient temperatures. The framework can provide the global optimum in reasonable calculation times within tight tolerances. In contrast, most local solvers applied are found to be inadequate. The CPU times are substantially smaller compared to a state-of-the-art global solver. For the case considered, recuperation can increase net power output but not necessarily economics.
We propose an iterative, partition-based moving horizon state estimator for large-scale linear systems that consist of interacting subsystems. Every subsystem estimates its own state and disturbance variables, taking ...
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For the first time, a distributed output feedback control scheme is presented which combines distributed model predictive control with distributed moving horizon estimation. More specifically, we combine the iterative...
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