Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process

Morgan, Josh; Chinen, Anderson Soares; Anderson‐Cook, Christine M.; Tong, Charles; Carroll, John P.; Saha, Chiranjib; Omell, Benjamin; Bhattacharyya, Debangsu; Matuszewski, Michael; Bhat, K. Sham; Miller, David C.

doi:10.1016/j.apenergy.2020.114533

Cited by 30 publications

(20 citation statements)

References 60 publications

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“…Sequential Design of Experiments (SDoE) is a framework that incorporates uncertainty-based criteria for selection of operating conditions for data collection and the use of the data for refining a stochastic process model in a cyclical manner. The SDoE procedure was previously summarized in Soepyan et al [197], and demonstrated at pilot scale for a solvent-based CO2 capture system in Morgan et al [198]. The SDoE process is represented schematically in Figure 3-9.…”

Section: Using Uncertainty Analysis For Design Of Experimentsmentioning

confidence: 99%

Towards improved guidelines for cost evaluation of carbon capture and storage

Roussanaly

Rubin

Spek

et al. 2021

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Understanding the costs of carbon capture and storage (CCS) is essential to understand the role for and potential of CCS technology in addressing climate change, for guidance in research activities aiming to reduce the cost and improve the performance of promising new CCS technologies in different applications. In practice, however, there are many challenges in establishing reliable cost estimates for CCS technologies. To help identify and overcome these challenges, a group of experts from industry, government, academia and other organisations came together in 2011 to form the CCS Cost Network (which came under the aegis of IEAGHG in 2017 [1]). Following discussions at the first CCS Cost Network workshop [1], several members of the workshop steering committee formed a task force to focus on the basic structure of CCS cost estimates. That effort produced a White Paper entitled, "Toward a Common Method of Cost Estimation for CCS at Fossil Fuel Power Plants" [2]. This white paper aimed at overcoming identified pitfalls in CCS cost evaluations for fossil fuel power plants arising from the different methodologies used by various organisations. Towards this aim, the white paper established a common costing methodology and nomenclature, as well as guidelines for CCS cost reporting to improve the clarity and consistency of cost estimates for greenhouse gas mitigation measures. While that work laid the foundation for establishing a common costing methodology for CCS, several important cost issues still remained to be addressed. Building on that earlier work and the interest from additional organisations, the current white paper is an effort to draw up a complementary set of CCS costing guidelines in three complementary areas where further guidelines and better practices are needed, and where efforts are underway to address those topics. This effort is a collaboration among researchers at several industrial research institutes (Electric

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Section: Using Uncertainty Analysis For Design Of Experimentsmentioning

confidence: 99%

Towards improved guidelines for cost evaluation of carbon capture and storage

Roussanaly

Rubin

Spek

et al. 2021

Self Cite

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“…The experiment was part of the United States Department of Energy's Carbon Capture Simulation for Industry Impact 2 collaboration among national laboratories, industrial partners, and academic institutions. Details of the experiment conducted at the Technology Center Mongstad in Norway are provided in Morgan et al 21 . The goal of the experiment was to validate a computer model of the full process using sub‐models for standalone property models (viscosity, surface tension, molar volume), a thermodynamic framework using multiple sources of data (heat capacity, heat of absorption, vapor‐liquid equilibrium) and process dependent models (interfacial area, mass transfer, and hydraulics).…”

Section: A Carbon Capture Examplementioning

confidence: 99%

Practical choices for space‐filling designs

Anderson‐Cook

Martin

et al. 2021

Quality & Reliability Eng

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Space-filling designs are now commonly used as a flexible model-free strategy for providing good coverage throughout an input space of interest for a variety of computer and physical experiment design scenarios. Some of the preliminary choices about how to frame the problem and which type of design to use can have a substantial impact on the success or failure of the experiment, and yet how to make these critical choices is often under-emphasized in the literature. In this paper, we explore several of the practical choices required by the experimenter and describe a sequence of steps to help create an ideal design that matches the goals and constraints of the experiment. These choices include the specification of the input space, the scaling of the variables, the degree of uniformity of the design points across the input space, and the space-filling characteristics. In addition, some new tools for defining the weights to implement non-uniform space-filling designs are provided. The methods are demonstrated with several illustrative examples and a real-world chemical engineering experiment for carbon capture.

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Section: Introductionmentioning

confidence: 99%

“…This new approach is advantageous for several experimental scenarios: (a) the user wants to protect against obtaining too narrow a range of response values, 9 (b) the obtained responses become inputs for models of a different part of the process, 10 (c) the inverse predictions of the unknown inputs based on the observed responses, 11 or (d) sequential experiments where a strategic augmentation of the initial experiment is based on an estimated model of the relationship(s) between inputs and responses. 12 The proposed approach is flexible to adapt to different input space dimensions, where considerations of good model estimation and prediction should dictate a suitable minimum size for the design. The input-response space-filling (IRSF) approach is easily adapted for implementation with one or more responses, where for the latter case possible correlations between the responses can be incorporated to define constraints on the anticipated response space.…”

Section: Introductionmentioning

confidence: 99%

Input‐response space‐filling designs

Anderson‐Cook

2021

Quality & Reliability Eng

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Traditional space‐filling designs are a convenient way to explore throughout an input space of flexible dimension and have design points close to any region where future predictions might be of interest. In some applications, there may be a model connecting the input factors to the response(s), which provides an opportunity to consider the spacing not only in the input space but also in the response space. In this paper, we present an approach for leveraging current understanding of the relationship between inputs and responses to generate designs that allow the experimenter to flexibly balance the spacing in these two regions to find an appropriate design for the experimental goals. Applications where good spacing of the observed response values include calibration problems where the goal is to demonstrate the adequacy of the model across the range of the responses, sensitivity studies where the outputs from a submodel may be used as inputs for subsequent models, and inverse problems where the outputs of a process will be used in the inverse prediction for the unknown inputs. We use the multi‐objective optimization method of Pareto fronts to generate multiple non‐dominated designs with different emphases on the input and response space‐filling criteria from which the experimenter can choose. The methods are illustrated through several examples and a chemical engineering case study.

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Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process

Cited by 30 publications

References 60 publications

Towards improved guidelines for cost evaluation of carbon capture and storage

Towards improved guidelines for cost evaluation of carbon capture and storage

Practical choices for space‐filling designs

Input‐response space‐filling designs

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