Real Time Optimisation of Industrial Gas Supply Networks

Adamson, Richard; Hobbs, Martin; Silcock, Andy; Montague, G.A.

doi:10.1016/j.ifacol.2015.08.207

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…In Adamson et al (2015) and Xenos et al (2015) polynomial nonlinear regression models of compressor or ASU power were used. Referring to equations 5, 6 and 7, efficiency is multiplied by flow to calculate power, where efficiency is a function of flow and pressure.…”

Section: Data-based Modelling Of Machine Efficiency / Powermentioning

confidence: 99%

“…A key operational aspect of the process considered in this work is that customer demands are unknown and unpredictable, with time horizons of hours, not days. The implementation of an MINLP approach using nonlinear empirical models, reported in Adamson et al (2015) generally yielded excessive solution times rendering the on-line application impractical. In this work the steady-state optimisation problem is formulated as an MILP in order to ensure robust and efficient on-line optimisation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Steady-state optimisation of a multiple cryogenic air separation unit and compressor plant

Adamson

Hobbs²,

Silcock³

et al. 2017

Applied Energy

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Newcastle University ePrints -eprint.ncl.ac.uk Adamson R, Hobbs M, Silcock A, Willis MJ. Steady-state optimisation of a multiple cryogenic air separation unit and compressor plant. Applied Energy 2017, 189, 221-232. AbstractThe development and on-line application of a steady-state optimisation strategy for a multiple cryogenic air separation unit and compressor plant is discussed. Implemented using mixed integer linear programming (MILP), it is demonstrated that the optimiser improves site efficiency at steady state by reduction of power consumption by up to 5% (a significant saving for such an energy intensive process) while meeting customer demand specifications. This is achieved through determination of the production distribution of the air separation units and optimal load distribution of the compression network, while simultaneously ensuring network material balance and network component operating constraints are met. In addition, the work demonstrates achievable benefits of demand side load management during peak power pricing periods, using liquid oxygen as an effective energy storage device. A key constituent of the optimisation strategy is linear modelling to predict individual unit power consumption. Piece-wise linear data-based models of compressor and air separation unit power are shown to provide accurate models which improve existing on-site power prediction by up to 80% for compressors and 60% for the air separation units.

show abstract

Section: Data-based Modelling Of Machine Efficiency / Powermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Steady-state optimisation of a multiple cryogenic air separation unit and compressor plant

Adamson

Hobbs²,

Silcock³

et al. 2017

Applied Energy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using nonlinear regression models defines a MINLP, e.g. see Adamson et al (2015), Xenos et al (2015) and Puranik et al (2016), however, it was shown in Adamson et al (2017) that, through the use of piece-wise linear models to accurately estimate machine power consumption, the problem can be re-cast as a computationally efficient MILP (see section 4.0).…”

Section: Site-wide Optimisation To Meet Demand Specificationsmentioning

confidence: 99%