In this paper we present an application of Model Predictive Control (MPC) to a commercial building cooling system. The MPC models are a combination of white, grey and black box models derived from the experimental dat...
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In this paper we present an application of Model Predictive Control (MPC) to a commercial building cooling system. The MPC models are a combination of white, grey and black box models derived from the experimental dataset and ensure energy and mass conservation is the system. Individual equipment models are validated against the actual measurements to ensure they accurately represent the physical system. A comparison of MPC performance to that of a business-as-usual operation scenario indicates up to 25% savings in operating costs with MPC. Copyright (C) 2022 The Authors.
Incorporating uncertainty into process scheduling by explicitly considering future forecasts is crucial for achieving optimal and feasible solutions in practice. However, the complexity of two-stage stochastic schedul...
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Incorporating uncertainty into process scheduling by explicitly considering future forecasts is crucial for achieving optimal and feasible solutions in practice. However, the complexity of two-stage stochastic scheduling formulations increases exponentially with the number of scenarios, making it difficult to solve large-scale problems in real-time using mathematical programming, which is often necessary for production-maintenance scheduling. This is where decomposition strategies on a scenario basis can be advantageous. Recently, the authors developed a method for decomposing two-stage scheduling problems (TSSPs) formulated on a discrete-time basis using the Similarity Index. This paper extends this concept to TSSPs formulated on a continuous-time basis by fuzzifying discrete decisions among slots instead of time periods, and incorporating the progressive-hedging algorithm to manage non-anticipativity in continuous decisions. The proposed algorithm is tested in a case study of a multiproduct plant consisting of a single processing unit from the literature. Results show that the proposed decomposition algorithm solves the problem faster than the monolithic formulation.
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