A Novel All-in-One Real-Time Optimization and Optimal Control Method for Batch Systems: Algorithm Description, Implementation Issues, and Comparison with the Existing Methodologies

Rossi, Francesco; Manenti, Flavio; Buzzi‐Ferraris, Guido

doi:10.1021/ie501376a

Cited by 12 publications

(2 citation statements)

References 21 publications

(24 reference statements)

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“…Similarly, most standard strategies for solving NMPC/DRTO problems may be too computationally intensive, especially when it is vital to utilize large-scale process models. Therefore, the process systems engineering research community keeps working on novel algorithms for NMPC/DRTO (Dones et al, 2010;Dua et al, 2008;Logist et al, 2012;Rossi et al, 2014) as well as innovative numerical solution strategies for this class of problems (Bansal et al, 2003). Unfortunately, no general tuning approach for model predictive control and dynamic real-time optimization has been found yet.…”

Section: C3 Latest Developments In Model Predictive Control and Dymentioning

confidence: 99%

Model predictive control and dynamic real-time optimization of steam cracking units

Rossi

Rovaglio²,

Manenti

2019

Computer Aided Chemical Engineering

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Unlike other parts of this book, this chapter does not deal with mathematical procedures for modeling complex reaction systems, e.g. strategies for construction/reduction of kinetic schemes, approaches to the estimation of their parameters (pre-exponential factors, activation energies, etc.), and so on. Conversely, it shows how to use accurate mathematical models of chemical processes, based on detailed kinetic schemes, for advanced control and optimization purposes. To this end, it describes two advanced model-based optimization/control strategies, called model predictive control and dynamic real-time optimization, and demonstrates the extent to which they can benefit chemical processes. The process unit, utilized to show the potential of model predictive control and dynamic real-time optimization, is a steam cracking furnace.

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Section: C3 Latest Developments In Model Predictive Control and Dymentioning

confidence: 99%

Model predictive control and dynamic real-time optimization of steam cracking units

Rossi

Rovaglio²,

Manenti

2019

Computer Aided Chemical Engineering

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“…Up to now, a simple but intuitive idea of how BSMBO&C operates has been conveyed. The interested reader can find many more details on the framework in its two reference papers (Rossi et al, 2014a;Rossi et al, 2014c). However, some additional explanations on the mathematical structure of the algorithm's objective function must be added.…”

Section: The Bsmboandc Algorithm: a Brief Theoretical Insightmentioning

confidence: 99%

A robust sustainable optimization & control strategy (RSOCS) for (fed-)batch processes towards the low-cost reduction of utilities consumption

Rossi

Manenti

Pirola

et al. 2016

Journal of Cleaner Production

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The need for the development of clean but still profitable processes and the study of low environmental impact and economically convenient management policies for them are two challenges for the years to come. This paper tries to give a first answer to the second of these needs, limited to the area of discontinuous productions. It deals with the development of a robust methodology for the profitable and clean management of (fed-)batch units under uncertainty, which can be referred to as a robust sustainability-oriented model-based optimization & control strategy. This procedure is specifically designed to ensure elevated process performances along with low-cost utilities usage reduction in realtime, simultaneously allowing for the effect of any external perturbation. In this way, conventional offline methods for process sustainable optimization can be easily overcome since the most suitable management policy, aimed at process sustainability, can be dynamically determined and applied in any operating condition. This leads to a significant step forward with respect to the nowadays options in terms of sustainable process management, that drives towards a cleaner and more energy-efficient future. The proposed theoretical framework is validated and tested on a case study based on the well-known fed-batch version of the Williams-Otto process to demonstrate its tangible benefits. The results achieved in this case study are promising and show that the framework is very effective in case of typical process operation while it is partially effective in case of unusual/unlikely critical process disturbances. Future works will go towards the removal of this weakness and further improvement in the algorithm robustness. KeywordsDynamic optimization, non-linear model-predictive control, scenario-based programming, (fed-)batch processes sustainable management, low-cost utilities usage reduction. IntroductionRecently, the social claim for the reduction in the pollution/environmental impact, originating from industrial activities, has paved the way to new research studies in the area of sustainable process design, optimization and real-time operation along with sustainable corporate-scale management. The primary aims of these new research areas consist of: the development of new process configurations and the revamping of the existing ones towards improved sustainability;  the online or offline search for sustainable operating conditions, limited to the single plant or extended to the whole corporate scale. Many authors have been testing problems belonging to these research fields. Their efforts have produced a huge number of contributions concerning sustainable process design, offline optimization and corporate-scale management. For instance, studies concerning the optimization of supply-chain networks subject to additional sustainability constraints can be found in several papers. In detail, Giarola et al. (2014) and Ng and Lam (2013) study the problem limited to the field of bio-refineries while Vance et al. (2013) fo...

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Multi‐scenario robust online optimization and control of fed‐batch systems via dynamic model‐based scenario selection

et al. 2016

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The manuscript proposes a novel robust methodology for the model-based online optimization/optimal control of fed-batch systems, which consists of two different interacting layers executed asynchronously. The first iteratively computes robust control actions online via multi-scenario stochastic optimization while the second iteratively re-estimates the optimal scenario map after every single/every certain number of control action/actions. The novelty of the approach is twofold: (I) the scenario map is optimally computed/updated based on probabilistic information on the process model uncertainty as well as the sensitivity of the controlled system to the uncertain parameters; and (II) the scenario set is dynamically re-estimated, thus accounting for the effect of disturbances and changes in the operating conditions of the target process. The proposed approach is applied to a fed-batch Williams-Otto process and compared to an existing multi-scenario optimization/control algorithm as well as a non-robust optimization/control strategy to draw conclusions about which method is more effective. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3264–3284, 2016

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A Novel All-in-One Real-Time Optimization and Optimal Control Method for Batch Systems: Algorithm Description, Implementation Issues, and Comparison with the Existing Methodologies

Cited by 12 publications

References 21 publications

Model predictive control and dynamic real-time optimization of steam cracking units

Model predictive control and dynamic real-time optimization of steam cracking units

A robust sustainable optimization & control strategy (RSOCS) for (fed-)batch processes towards the low-cost reduction of utilities consumption

Multi‐scenario robust online optimization and control of fed‐batch systems via dynamic model‐based scenario selection

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