Superstructure Optimization of Oleochemical Processes with Surrogate Models

Jones, Mark Nicholas; Forero-Hernandez, Hector; Zubov, Alexandr; Sarup, Bent; Sin, Gürkan

doi:10.1016/b978-0-444-64241-7.50041-0

Cited by 14 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…linearization of the objective function, surrogate model-assisted SSO, or decomposition algorithms [50,51]. Especially the capacity of surrogate model-assisted SSO alleviating the computational burden has been exploited in various studies [49,52,53]. This allows for an elegant solution to integrate complex high-fidelity models from different platforms indirectly via their surrogates into a simple superstructure formulation, compared to extensive equation-based approaches as, e.g., generalized disjunctive programming [49,51].…”

Section: Superstructure Optimizationmentioning

confidence: 99%

Synergistic optimization framework for the process synthesis and design of biorefineries

Vollmer

Gernaey

et al. 2021

Front. Chem. Sci. Eng.

Self Cite

View full text Add to dashboard Cite

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

show abstract

Section: Superstructure Optimizationmentioning

confidence: 99%

Synergistic optimization framework for the process synthesis and design of biorefineries

Vollmer

Gernaey

et al. 2021

Front. Chem. Sci. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A surrogate model is an engineering approach used when an intended outcome of interest cannot be easily and directly predicted, so a representation of the result is used instead [32]. Many engineering design problems involve tests or simulations as a function of design variables to determine the design objective and constraint functions.…”

Section: Surrogate Modelsmentioning

confidence: 99%

The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)

et al. 2020

View full text Add to dashboard Cite

Pump-as-turbine (PAT) technology permits two operating states—as a pump or turbine, depending on the demand. Nevertheless, designing the geometrical components to suit these operating states has been an unending design issue, because of the multi-conditions for the PAT technology that must be attained to enhance the hydraulic performance. Also, PAT has been known to have a narrow operating range and operates poorly at off-design conditions, due to the lack of flow control device and poor geometrical designs. Therefore, for the PAT to have a wider operating range and operate effectively at off-design conditions, the geometric parameters need to be optimized. Since it is practically impossible to optimize more than one objective function at the same time, a suitable surrogate model is needed to mimic the objective functions for it to be solvable. In this study, the Latin hypercube sampling method was used to obtain the objective function values, the Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) were used as surrogate models to approximate the objective functions in the design space. Then, a suitable surrogate model was chosen for the optimization. The Pareto-optimal solutions were obtained by using the Pareto-based genetic algorithm (PBGA). To evaluate the results of the optimization, three representative Pareto-optimal points were selected and analyzed. Compared to the baseline model, the Pareto-optimal points showed a great improvement in the objective functions. After optimization, the geometry of the impeller was redesigned to suit the operating conditions of PAT. The findings show that the efficiencies of the optimized design variables of PAT were enhanced by 23.7%, 11.5%, and 10.4% at part load, design point, and under overload flow conditions, respectively. Moreover, the results also indicated that the chosen design variables (b2, β2, β1, and z) had a substantial impact on the objective functions, justifying the feasibility of the optimization method employed in this study.

show abstract

“…Surrogate models are common in applications where the original model does not easily lend itself to optimisation, e.g. Palmer and Realff (2002), Caballero and Grossmann (2008), Fahmi and Cremaschi (2012), Boukouvala et al (2017), Beykal et al (2018), Jones et al (2018), Carpio et al (2018) and Yang et al (2019). Common surrogate models include e.g.…”

Section: Design Of Experiments For Black-box Model Discriminationmentioning

confidence: 99%

GPdoemd: A Python package for design of experiments for model discrimination

Olofsson

Hebing

Niedenführ

et al. 2019

Computers & Chemical Engineering

View full text Add to dashboard Cite

Model discrimination identifies a mathematical model that usefully explains and predicts a given system's behaviour. Researchers will often have several models, i.e. hypotheses, about an underlying system mechanism, but insufficient experimental data to discriminate between the models, i.e. discard inaccurate models. Given rival mathematical models and an initial experimental data set, optimal design of experiments suggests maximally informative experimental observations that maximise a design criterion weighted by prediction uncertainty. The model uncertainty requires gradients, which may not be readily available for black-box models. This paper (i) proposes a new design criterion using the Jensen-Rényi divergence, and (ii) develops a novel method replacing black-box models with Gaussian process surrogates. Using the surrogates, we marginalise out the model parameters with approximate inference. Results show these contributions working well for both classical and new test instances. We also (iii) introduce and discuss GPdoemd, the open-source implementation of the Gaussian process surrogate method.

show abstract

Superstructure Optimization of Oleochemical Processes with Surrogate Models

Cited by 14 publications

References 6 publications

Synergistic optimization framework for the process synthesis and design of biorefineries

Synergistic optimization framework for the process synthesis and design of biorefineries

The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)

GPdoemd: A Python package for design of experiments for model discrimination

Contact Info

Product

Resources

About