A Dynamic Model of a Mercury Chlorine Cell

Masding, P W; Browning, Natalie

doi:10.1002/9780470999479.ch20

Cited by 3 publications

(2 citation statements)

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“…), and has fast primary dynamics: the production rate can be modulated (increased or decreased) nearly instantly by manipulating current density. Several authors have considered demand response operation of chlor-alkali plants, using steady state or empirical models. − Otashu and Baldea identified the need to incorporate accurate models of the electrochemical and thermal (nonlinear) dynamics at the demand response scheduling level, particularly to ensure the cell temperature remains within the membrane’s recommended operating range during transitions in the production rate. Otashu and Baldea developed a first-principle-based process model for optimal production scheduling in short-term electricity markets and demonstrated significant cost savings compared with operation at a constant load.…”

Section: Chlor-alkali Processmentioning

confidence: 99%

Stochastic Scheduling and Control Using Data-Driven Nonlinear Dynamic Models: Application to Demand Response Operation of a Chlor-Alkali Plant

Simkoff

Bâldea

2020

Ind. Eng. Chem. Res.

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Modern chemical manufacturers are increasingly interested in improving the agility of their operations. This is particularly true in regions with deregulated electricity markets where participation in demand response programs or taking advantage of real-time prices can be profitable. Processes that consume electricity as a primary feedstock and can shift operating points quickly, such as the chlor-alkali process, are prime candidates for such participation; however, process dynamics that evolve over a longer time-scale typically need to be accounted for to ensure safe operation and an on-spec product. This calls for an integrated scheduling and control approach. The resulting optimization problems are computationally challenging due to multiple time-scales and the relatively long horizons involved (on the order of days). We present a methodology for efficient representation of nonlinear process dynamics using novel parameterizations of Hammerstein−Wiener models. We carry out an extensive study concerning real-time electricity market participation of a chlor-alkali process, focusing on the optimal allocation of bids in the day ahead and real-time markets under electricity price and product demand uncertainty.

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Section: Chlor-alkali Processmentioning

confidence: 99%

Stochastic Scheduling and Control Using Data-Driven Nonlinear Dynamic Models: Application to Demand Response Operation of a Chlor-Alkali Plant

Simkoff

Bâldea

2020

Ind. Eng. Chem. Res.

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“…Here we consider a k-factor of 0.10, with a linear increase of 2.08 10 −3 per equivalent membrane lifetime month m l , as given in (1). More specific models are available in literature, defining the k-factor based on various process parameters [41].…”

Section: Chlor-alkali Electrolysis Modelmentioning

confidence: 99%

A Two-Stage Stochastic Optimisation Methodology for the Operation of a Chlor-Alkali Electrolyser under Variable DAM and FCR Market Prices

et al. 2020

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The increased penetration of renewable energy sources in the electrical grid raises the need for more power system flexibility. One of the high potential groups to provide such flexibility is the industry. Incentives to do so are provided by variable pricing and remuneration of supplied ancillary services. The operational flexibility of a chlor-alkali electrolysis process shows opportunities in the current energy and ancillary services markets. A co-optimisation of operating the chlor-alkali process under an hourly variable priced electricity sourcing strategy and the delivery of Frequency Containment Reserve (FCR) is the core of this work. A short term price prediction for the Day-Ahead Market (DAM) and FCR market as input for a deterministic optimisation shows good results under standard DAM price patterns, but leaves room for improvement in case of price fluctuations, e.g., as caused by Renewable Energy Sources (RES). A two-stage stochastic optimisation is considered to cope with the uncertainties introduced by the exogenous parameters. An improvement of the stochastic solution over the deterministic Expected Value (EV) solution is shown.

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