A Framework for Stochastic and Surrogate-Assisted Optimization using Sequential Modular Process Simulators

Penteado, Alberto; Esche, Erik; Weigert, Joris; Repke, Jens‐Uwe

doi:10.1016/b978-0-12-823377-1.50318-9

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…Aspen Plus simulations suffer from nonconvergence issues when improper operating conditions are given, or the recycle stream is set too tight. 30,48 Such problems also occur in our case. The obtained classifier is applied here to screen out the potential nonconverged inputs in the successive iterations, thus improving the percentage of effective data from 81% to 91% (Figure 15).…”

Section: Case Study 2: Surrogate Generation For Gtl a Steady-state Fl...supporting

confidence: 72%

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Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas‐to‐liquids

2022

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We propose a sequential sampling approach to training statistical digital twins. This approach is relevant for real-world engineering problems with expensive data generation. Prerequisite for building surrogates is sufficient data; however, oversampling does not improve regression accuracy. The time for data generation may be reduced by: (a) applying a classifier to improve data quality and avoid evaluation of infeasible inputs, and (b) employing dynamic sampling linked to regression quality. In dynamic sampling, the initial sampling rate is large to generate enough data for surrogate regression in a few iterations; the sampling rate gradually slows down with the improvement of the iteratively refined surrogate. A dynamic process and a steadystate process from the field of carbon capture and utilization are used as case studies: pressure swing adsorption (PSA) and gas-to-liquids (GTL). The computational costs for surrogates generation are reduced by 86% for PSA and 51% for GTL, compared with employing a static sampling rate.

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Section: Case Study 2: Surrogate Generation For Gtl a Steady-state Fl...supporting

confidence: 72%

“…Aspen Plus simulations suffer from nonconvergence issues when improper operating conditions are given, or the recycle stream is set too tight 30,48 . Such problems also occur in our case.…”

Section: Case Studiesmentioning

confidence: 87%

Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas‐to‐liquids

2022

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“…Aspen simulations suffer from non-convergence issues when improper operating conditions are given, or the recycle stream is set too tight. 27,49 Such problems also occur in our case.…”

Section: Case Study 2: Surrogate Generation For Gtl a Steady-state Flowsheet In Aspen Plussupporting

confidence: 72%

Efficient Surrogates Construction of Chemical Processes: Case studies on Pressure Swing Adsorption and Gas-to-Liquids

Hao

Zhang

Lapkin

2021

Preprint

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We propose a workflow for reduction in the time required for data generation during generation of statistical digital twins. This methodology is particularly relevant for real-world engineering problems when data generation is expensive. A prerequisite for building surrogates is sufficient input/output data, whereas over-sampling can hardly improve the regression accuracy. The time for data generation can be reduced via (1) reduction of the average time spent on generating individual data points and (2) reduction in the total number of data points, by reducing the sampling rate with the improvement of surrogate quality. Examples of a dynamic process and a steady-state process from the field of carbon capture and utilization are used as two case studies: pressure swing adsorption (PSA) and Gas-to-Liquids (GTL). With the proposed methodology, the time for surrogate generation can be reduced by 88% for PSA and 60% for GTL, respectively.

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“…Further details on the models are provided in [25] and the Aspen Plus model files are made available in [26]. All process optimizations have been performed via a self-programmed Python interface to Aspen Plus [27] and by applying Differential Evolution as optimization algorithm [28].…”

Section: Methodsmentioning

confidence: 99%

Economic Potential of Bio-Ethylene Production via Oxidative Coupling of Methane in Biogas from Anaerobic Digestion of Industrial Effluents

et al. 2021

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Brazil’s large biofuels industry generates significant amounts of effluents, e.g., vinasse from bioethanol, that can effectively be used as substrate for production of biogas via Anaerobic Digestion (AD). The Oxidative Coupling of Methane (OCM) is the heterogeneous catalytic oxidation of methane into ethylene, which is a main building block for the chemical industry. This work investigates the potential and competitiveness of bio-ethylene production via OCM using biogas produced by biological anaerobiosis of vinasse as a feedstock. The proposed process can add incentive to treat of vinasse via AD and replace fossil ethylene, thus potentially reducing emissions of Greenhouse Gases (GHG). A process model is developed in Aspen Plus v10 software and used to design an economic Biogas-based Oxidative Coupling of Methane (Bio-OCM) process that consumes biogas and oxygen as educts and produces ethylene, ethane, and light off-gases as products. Operating conditions in the reaction section are optimized and a reaction product yield of 16.12% is reached by applying two adiabatic Packed Bed Reactors (PBRs) in series. For the downstream CO2 removal section, a standalone amine-absorption process is simulated and compared to a hybrid membrane-absorption process on an economic basis. For the distillation section, two different configurations with and without Recycle Split Vapor (RSV) are simulated and compared. The bio-ethylene production cost for a Bio-OCM plant to be installed in Brazil is estimated considering a wide range of prices for educts, utility, side products, and equipment within a Monte Carlo simulation. The resulting average production cost of bio-ethylene is 0.53 ±0.73 USD.kgC2H4-1. The production cost is highly sensitive to the sales price assigned to a light off-gas side-product stream containing mostly the un-reacted methane. A sales price close to that of Brazilian pipeline natural gas has been assumed based on the characteristics of this stream. The Monte Carlo simulation shows that a bio-ethylene production cost below or equal to 0.70 USD.kgC2H4-1 is achieved with a 55.2% confidence, whereas market values for fossil ethylene typically lie between 0.70USD.kgC2H4-1–1.50USD.kgC2H4-1. Technical and economic challenges for the industrial implementation of the proposed Bio-OCM process are identified and relevant opportunities for further research and improvement are discussed.

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A Framework for Stochastic and Surrogate-Assisted Optimization using Sequential Modular Process Simulators

Cited by 6 publications

References 6 publications

Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas‐to‐liquids

Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas‐to‐liquids

Efficient Surrogates Construction of Chemical Processes: Case studies on Pressure Swing Adsorption and Gas-to-Liquids

Economic Potential of Bio-Ethylene Production via Oxidative Coupling of Methane in Biogas from Anaerobic Digestion of Industrial Effluents

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