ArticleAIChE journal. American Institute of Chemical Engineers2020
A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities.
Article in AIChE journal. American Institute of Chemical Engineers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Hyperparameter Optimization of Non-linear Machine Learning Models Using Bi-level Data-Driven Optimization.Computers & chemical engineering · 2026Article
- Surrogate-Assisted Optimization of Highly Constrained Oil Recovery Processes Using Classification-Based Constraint Modeling.Industrial & engineering chemistry research · 2025Article
- Bi-level Data-driven Enterprise-wide Optimization with Mixed-integer Nonlinear Scheduling Problems.Digital Chemical Engineering · 2025Article
- Towards planetary boundary sustainability of food processing wastewater, by resource recovery & emission reduction: A process system engineering perspective.Carbon Capture Science & Technology · 2024Review
- The importance of incorporating ventricular-ventricular interaction (VVI) in the study of pulmonary hypertension.Mathematical biosciences · 2024Article
- From Then to Now and Beyond: Exploring How Machine Learning Shapes Process Design Problems.Systems & control transactions · 2024Article
- Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data.Chemical engineering science · 2023Article
- Continuous-Time Surrogate Models for Data-Driven Dynamic Optimization.ESCAPE. European Symposium on Computer Aided Process Engineering · 2022Article
- Data-Driven Optimization of Mixed-integer Bi-level Multi-follower Integrated Planning and Scheduling Problems Under Demand Uncertainty.Computers & chemical engineering · 2022Article
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4 authors.
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Abstract
Support Vector Machines (SVMs) based optimization framework is presented for the data-driven optimization of numerically infeasible Differential Algebraic Equations (DAEs) without the full discretization of the underlying first-principles model. By formulating the stability constraint of the numerical integration of a DAE system as a supervised classification problem, we are able to demonstrate that SVMs can accurately map the boundary of numerical infeasibility. The necessity of this data-driven approach is demonstrated on a 2-dimensional motivating example, where highly accurate SVM models are trained, validated, and tested using the data collected from the numerical integration of DAEs. Furthermore, this methodology is extended and tested for a multi-dimensional case study from reaction engineering (i.e., thermal cracking of natural gas liquids). The data-driven optimization of this complex case study is explored through integrating the SVM models with a constrained global grey-box optimization algorithm, namely the ARGONAUT framework.
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