749 research outputs found
Safe real-time optimization using multi-fidelity guassian processes
This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes. This work's novelty lies in integrating derivative-free optimization schemes and multi-fidelity Gaussian processes within a Bayesian optimization framework. The proposed scheme uses two Gaussian processes for the stochastic system, one emulates the (known) process model, and another, the true system through measurements. In this way, low fidelity samples can be obtained via a model, while high fidelity samples are obtained through measurements of the system. This framework captures the system's behavior in a non-parametric fashion while driving exploration through acquisition functions. The benefit of using a Gaussian process to represent the system is the ability to perform uncertainty quantification in real-time and allow for chance constraints to be satisfied with high confidence. This results in a practical approach that is illustrated in numerical case studies, including a semi-batch photobioreactor optimization problem
Model Adaptation for Real-Time Optimization in Energy Systems
Real-time optimization (RTO) is widely used in industry to operate processes close to their maximum performance. The models used for RTO need to be adapted using real-time data to ensure feasibility of the model optimal inputs and convergence to the real plant optimal point. Heat and power systems are suitable for being optimized in real-time because of their fast dynamics and the benefits achievable by reacting to changes in power prices and steam demand. This work proposes a modifier adaptation strategy that exploits the structure of certain problems to make the adaptation faster and more reliable, which is proven to be particularly useful for heat and power systems. The adaptation is performed in the equations that predict efficiencies or performance of unit operations. By identifying the variables that modify each performance factor, the number of data sets needed for gradient correction is reduced. This makes the proposed strategy suitable for real-time optimization of processes with a large number of inputs. Two alternatives are proposed to implement the approach: gradient calculation by finite differences and quadratic regression using current and past data. The features and behavior of this approach are shown through two case studies: (i) a simple model with three processes, and (ii) a heat and power system of a sugar and ethanol plant. A comparison with other existent approaches shows a better performance in terms of operating cost and sensitivity to noise.Fil: Serralunga, Fernán José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina;Fil: Mussati, Miguel Ceferino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina;Fil: Aguirre, Pio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina
Economic MPC with Modifier Adaptation using Transient Measurements
Producción CientíficaThis paper presents a method to estimate process dynamic gradients along the transient that combined with the idea of Modifier Adaptation (MA) improves the economic cost fuction of the examples presented. The gradient estimation method, called TMA, aims to reduce the large convergence time required to traditional MA in processes of slow dynamics. TMA is used with an economic predictive control with MA (eMPC+TMA) and was applied in two case studies: a simulation of the Williams-Otto reactor and a hybrid laboratory plant based on the Van de Vusse reactor. The results show that eMPC+TMA could reach the plant real steady-state optimum despite process-model mismatch, due to the inclusion of the effect of process dynamics in the TMA algorithm. Despite the estimation errors, the proposed methodology improved the profit of the experimental case study, with respect to the use of an eMPC with no modifiers, by about 20% for the unconstrained case, and by 130% in the constrained case.Junta de Castilla y León (CLU 2017-09 and UIC 233)FEDER - AEI (PGC2018-099312-B-C31
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
Over the last ten years, we have seen a significant increase in industrial
data, tremendous improvement in computational power, and major theoretical
advances in machine learning. This opens up an opportunity to use modern
machine learning tools on large-scale nonlinear monitoring and control
problems. This article provides a survey of recent results with applications in
the process industry.Comment: IFAC World Congress 202
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