2018
DOI: 10.1016/j.compchemeng.2017.11.021
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An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations

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Cited by 44 publications
(33 citation statements)
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“…models are versatile and have found useful applications in control theory and, recently, DR studies. For example, HW models developed for an air separation process were employed for DR scheduling34,37 and for scheduling process operations for reduced emissions 38. The two model types discussed above describe the dynamic evolution of the variables associated with the process.…”
mentioning
confidence: 99%
See 1 more Smart Citation
“…models are versatile and have found useful applications in control theory and, recently, DR studies. For example, HW models developed for an air separation process were employed for DR scheduling34,37 and for scheduling process operations for reduced emissions 38. The two model types discussed above describe the dynamic evolution of the variables associated with the process.…”
mentioning
confidence: 99%
“…Motivated by this, HW and SS models of the form discussed in Section 2.2 were identified for this plant. In simplifying process models for scheduling calculations, it is important to consider the scheduling relevant-variables and constraints that impact dynamic feasibility 34,37. Thus, dynamic models are developed only for plant power consumption and cell temperature, as the latter limits the agility and DR capabilities of the plant under variable operation 42.…”
mentioning
confidence: 99%
“…Recent work uses, e.g., artificial neural networks to set up a surrogate model enabling multi-scale optimization exemplified with a membrane process [91] or thin film growth processes [92]. Other solutions proposed to deal with multiscale problems are model reduction methods or surrogate modeling approaches to reduce computational effort for complex nonlinear systems to allow for efficient control and scheduling [93][94][95][96]. Some aspects concerning the abovementioned challenges are expected to be treated using machine learning methods in chemical engineering in the future [97].…”
Section: Future Challengesmentioning
confidence: 99%
“…Approaches to remedy these separations and the complexity of the fully integrated problem include utilizing decomposition strategies, [26][27][28][29][30] time scale-bridging models, 31,32 and/or surrogate, or low order, linear models of closed-loop process dynamics. [33][34][35] The discussion of online, moving horizon, and closedloop implementation of these solution methods has become more prevalent in this area of research. 34,[36][37][38][39] Also some preliminary steps are necessary before this ambitious integration goal can be undertaken.…”
Section: Something Borrowed-adding Feedback To Schedulingmentioning
confidence: 99%
“…Tighter integration of these layers may be a valid long‐term goal, but one challenge is that the time scales remain fairly separate for the chemical production‐scheduling problem and the automatic control problem of executing the schedule. Approaches to remedy these separations and the complexity of the fully integrated problem include utilizing decomposition strategies, time scale‐bridging models, and/or surrogate, or low order, linear models of closed‐loop process dynamics . The discussion of online, moving horizon, and closed‐loop implementation of these solution methods has become more prevalent in this area of research …”
Section: Something Borrowed—adding Feedback To Schedulingmentioning
confidence: 99%