ArticleIndustrial & engineering chemistry research2025
Surrogate-Assisted Optimization of Highly Constrained Oil Recovery Processes Using Classification-Based Constraint Modeling.
Article in Industrial & engineering chemistry research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Hyperparameter Optimization of Non-linear Machine Learning Models Using Bi-level Data-Driven Optimization.Computers & chemical engineering · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Real-world problems often involve constraints that must be carefully managed for feasible and efficient operations. In optimization, this becomes especially challenging with complex, high-dimensional problems that are computationally expensive and subject to hundreds or even thousands of constraints. We address these challenges by optimizing the highly constrained waterflooding process using a surrogate model of the reservoir and a classification-based constraint handling technique. Our study uses benchmark reservoir simulations, beginning with the low-dimensional Egg model and extending to the high-dimensional UNISIM model. We employ a Feedforward Neural Network (FFNN) surrogate for objective quantification and use classification-based modeling to transform the numerous constraints into a binary problem, distinguishing between feasible and infeasible reservoir settings. Our methodology involves an offline phase to develop and train models using reservoir simulation data, achieving high predictive accuracy (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.