A discrete-time scheduling model for power-intensive processes taking fatigue of equipment into consideration

Obermeier, Andreas; Windmeier, Christoph; Esche, Erik; Repke, Jens‐Uwe

doi:10.1016/j.ces.2018.10.036

Cited by 20 publications

(27 citation statements)

References 24 publications

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“…An initial emphasis is on air separation. They show a clear emphasis on scheduling [6][7][8][9][10][11][12][13][14] aspects [15][16][17], investigate model reduction [18,12], and elaborate design towards flexibility [19][20][21]. Increasingly, electrochemical processes, e.g., chlor-alkali process [2,[22][23][24][25][26][27][28][29][30][31] and water electrolysis [32][33][34][35][36][37][38][39][40][41][42], come into focus.…”

Section: Demand Response -Fluctuation Of Steady-state Processes Reacmentioning

confidence: 99%

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

Esche

Repke

2020

Chemie Ingenieur Technik

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Participating in electricity markets through demand response causes new requirements for optimizing process control of chemical plants. The last ten years have brought great advances in the formulation and solution of economic nonlinear model predictive control and state estimation to support operation of processes under dynamic constraints. However, gaps remain regarding the availabilities of suitable plant models capable of describing processes active in demand response as well as of robust schemes for state estimation and economic nonlinear model predictive control in commercial tools.

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Section: Demand Response -Fluctuation Of Steady-state Processes Reacmentioning

confidence: 99%

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

Esche

Repke

2020

Chemie Ingenieur Technik

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“…42 For this purpose, we impose constraints on the change in production rates, as widely done in scheduling practice to ensure that load changes can be tracked without violating product requirements. 6,8,39 Revisiting the results presented in our previous work concerning the process dynamics, 35 Note that changing the control system to a more advanced one, such as nonlinear model predictive control, could involve substantially loosened ramping limits 43,44 and thereby increase potential savings, which should be thoroughly investigated in the future. Moreover, we acknowledge that numerous works argue to construct dynamic surrogate models 13,45,46 for explicitly capturing the process dynamics and demonstrate their successful application to the scheduling of ASUs.…”

Section: Scheduling Modelmentioning

confidence: 88%

“…The power consumption of the process P t (LIN t , LOX t ) is calculated using an ANN as nonlinear surrogate model resulting in the surface given in Figure 2. Note that this surface is nonconvex, moti- to an advancement compared to the vast majority of previous literature that either use simplified linear characteristics, [4][5][6]36 which potentially introduces significant approximation errors, or involve the identification of piecewise-linearized surrogate models, 8,[37][38][39] which potentially requires large numbers of disjunctions for being accurate.…”

Section: Scheduling Modelmentioning

confidence: 99%

Nonlinear scheduling with time‐variable electricity prices using sensitivity‐based truncations of wavelet transforms

2020

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We propose an algorithm for scheduling subject to time‐variable electricity prices using nonlinear process models that enables long planning horizons with fine discretizations. The algorithm relies on a reduced‐space formulation and enhances our previous work (Schäfer et al., Comput Chem Eng, 2020;132:106598) by a sensitivity‐based refinement procedure. We therein expose the coefficients of the wavelet transform of the time series of independent process variables to the optimizer. The problem size is reduced by truncating the transform and iteratively adjusted using Lagrangian multipliers. We apply the algorithm to the scheduling of a multi‐product air separation unit. The nonlinear power consumption characteristic is replaced by an artificial neural network trained on data from a rigorous model. We demonstrate that the proposed algorithm reduces the number of optimization variables by more than one order of magnitude, whilst furnishing feasible schedules with insignificant losses in objective values compared to solutions considering the full dimensionality.

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“…Zudem werden Anfahrvorgänge und Anlagenstopps berücksichtigt [9,14]. Des Weiteren betrachten Obermeier et al den Aspekt der Lebensdauer kritischer Anlagenkomponenten [6,18]. Ein aktuelles Beispiel für DSM bei fluktuierender Energieverfügbarkeit am Beispiel einer LZA findet sich in Kelley et al [19].…”

Section: Simulation Und Optimierung Des Flexiblen Betriebs Von Luftzeunclassified

Flexibler Betrieb von Luftzerlegungsanlagen

Klein

Fritsch

Haider

et al. 2020

Chemie Ingenieur Technik

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Vor dem Hintergrund der Energiewende wird die aktuelle Forschung zum energieoptimalen, lastflexiblen Betrieb von Luftzerlegungsanlagen beschrieben. Zum einen wird ein realitätsgetreuer, druckgetriebener Ansatz zur dynamischen Simulation vorgestellt. Auf dessen Basis wird ein detailliertes, transientes Simulationsmodell einer realen Luftzerlegungsanlage, ein digitaler Zwilling, erstellt. Exemplarisch werden Simulationen extremer Betriebsszenarien, wie das Anfahren oder Abschalten des Hauptwärmeübertragers, der Trennkolonnen und der gesamten Luftzerlegungsanlage, beschrieben. Zum anderen wird die Lebensdauer des Hauptwärmeübertragers aufgrund der hohen thermischen Wechselbelastung mithilfe eines Prüfstands untersucht. Die aufgenommenen Daten dienen zur Ableitung und Validierung dreidimensionaler Simulationsmodelle.

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A discrete-time scheduling model for power-intensive processes taking fatigue of equipment into consideration

Cited by 20 publications

References 24 publications

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

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

Nonlinear scheduling with time‐variable electricity prices using sensitivity‐based truncations of wavelet transforms

Flexibler Betrieb von Luftzerlegungsanlagen

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