Guido Buzzi-Ferraris’s research while affiliated with Politecnico di Milano and other places

... In chemical engineering, optimization is a practical choice for designing and operating systems to maximize performance while minimizing operational expenses, environmental impact, and energy consumption [9]. Rossi et al. (2017) presented a study using dynamic optimization and chemical process control to detect runaway reactions where unstable peak temperatures may lead to severe reactor explosions. Their approach ensures uncertainty management with dynamic optimization. ...

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Comparative analysis of trust region filter methods with different mathematical models in surrogate-based optimization

... Dans le domaine "voisin" des centrales solaires thermodynamiques (CSP) utilisant des technologies solairesà concentration et produisant de l'électricité, les stratégies de contrôle et la gestion du stockage sont des sujets d'actualité qui font l'objet de nombreuses publications et qui peuvent nous intéresser dans notre recherche d'un fonctionnement optimal. Ainsi, Manenti et al. [105,106] ont simulé le fonctionnement dynamique de la centrale CSP Archimède en Italie. Ils ont représenté de manière simplifiée le fonctionnement de cette centrale et ont pu obtenir une simulation dynamique suffisante pour analyser les phases de démarrage et d'arrêt ainsi que la possible stratégie de contrôle de la centrale. ...

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Développement d'un outil de simulation et d'optimisation dynamique d'une centrale solaire thermique.

... However, there are developments in the integration under uncertainty, which are of consequence here as noted in [56]. One example is the dynamic identification of the optimal description of model uncertainty from batch to batch in [99]. They emphasize the importance of the choice of uncertain parameters to characterize uncertainty in process models and present a framework for rapid identification of the optimal set of uncertain parameters needed for formulation of stochastic online optimization problems using a combination of approximate statistical analysis and multipoint sensitivity analysis. ...

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Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

... Otherwise, a huge number of experimental points close to 50 bar would have negatively impacted the refit performance, forcing the minimization solver to better predict close to the 50 bar region by neglecting other portions of the investigation domain where observations were less dense. 96,97 As a last consideration, we decided to limit the number of experimental data in the database so as to reduce the computational time and effort. We believe that 159 observations are sufficient to perform a kinetics refit. ...

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Century of Technology Trends in Methanol Synthesis: Any Need for Kinetics Refitting?

... Yet, the association of an offline and an online phase could benefit to the operation 1020 of a solar system and has been implemented previously for other processes. optimization of multi-unit batch processes (Rossi et al., 2017). Distinct objective functions were used in the two steps, with an economic objective present in each of the two layers. ...

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Analysis and Future Perspectives for the Application of Dynamic Real-Time Optimization to Solar Thermal Plants: A Review

... 50 complete scheme Graaf et al. 51 Lim et al. 36 Park et al. 35 CO + 2H 2 ⇆ CH 3 OH Seidel et al. 34 CO 2 + 3H 2 ⇆ CH 3 OH + H 2 O Nestler et al. 52 CO 2 + H 2 ⇆ CO + H 2 O Slotboom et al. 44 Lacerda de Oliveira Campos et al. 43 Bisotti et al. 13 Skrzypek guarantee that influential observations are completely removed and these have not affected the refitting procedure. 57,59,60 In the cross-validation method, the entire dataset is divided into an arbitrary number of subsets (N). The regression procedure is repeated N times using N − 1 subsets for the minimization procedure (training folder), while the last one is then used as a validation. ...

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Impact of Kinetic Models on Methanol Synthesis Reactor Predictions: In Silico Assessment and Comparison with Industrial Data

... The application of statistical techniques to the quantification of model uncertainty is a new paradigm, which has recently emerged due to the growing interest of industry and of the PSE community in stochastic optimization frameworks, robust design strategies and quantitative risk assessment. Specifically, strategies for uncertainty quantification are commonly applied in areas such as robust process/product design (especially within the pharmaceutical sector) , drug delivery and robust optimization/control of industrial processes (Rossi et al., 2016). ...

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Assessment of Accuracy and Computational Efficiency of Different Strategies for Estimation of Probability Distributions Applied to ODE/DAE Systems

... 18,19 The main advantage of the SIP method is that it guarantees rigorous global optimality for certain categories of nonlinear problems. Another approach is the multiscenario-based method, [20][21][22] which generates scenarios within the uncertainty set and iteratively adds more scenarios as model constraints. However, this method cannot theoretically examine and ensure the feasibility of all scenarios over the whole uncertainty set. ...

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Robust dynamic optimization for nonlinear chemical processes under measurable and unmeasurable uncertainties

... In a second optimization, the reactor volume is minimized which is achieved via maximization of the reaction rates. For this purpose, the adiabatic reactor edges are priced with kinetic rate expressions obtained from the kinetic model 2 for CH 3 OH synthesis by Villa et al. (1987) ...

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Thermodynamic Network Flow Approach for Chemical Process Synthesis

... When the goal of the experiment is to distinguish between multiple models that encode competing hypotheses, MBDOE techniques generally select the experimental perturbation and measurements to maximize the predicted variance between the different model simulations. [43][44][45] Kreutz and Timmer 19 briefly describe these approaches and point out that parameter estimation is an integral part of model discrimination. Given the new data, the best approximating model can be selected from an information theoretic perspective. ...

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Model-based design of experiments for cellular processes