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Zukui Li

Professor

University of Alberta

Canada

Research Interests

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About

Dr. Zukui Li is an Associate Professor at the University of Alberta, Canada. His research focuses on data-driven modeling, optimization techniques, and the application of neural networks in industrial processes. Recent publications include work on molecular composition reconstruction, multiparametric linear programming, and soft sensor development for multi-product pipelines.

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Collaborators (2)

Said Salim Rahal

Assistant Professor

American University of Beirut

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Rajagopalan Srinivasan

Indian Institute of Technology Madras

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Zukui Li

Research Interests

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Articles (22)

Distributionally robust optimization for Gaussian mixture model ambiguity under moment variations

Adaptive physics-informed neural network-based digital twins integrated with Ensemble Kalman Filter

Sensitivity analysis and stochastic optimization of levelized cost of hydrogen production through methane pyrolysis

Simultaneous outlier-exclusion and distributionally robust learning through partial optimal transport

Molecular composition reconstruction of naphtha fractions through data-driven modeling and interpretable optimization

Decision rule-based method for multiparametric linear programming

Dynamic Process Flexibility Analysis Using Neural Networks and a Volumetric Flexibility Index

A Multi-Sensor Feature Representation and Fusion Method for Data-Driven Industrial Process Fault Diagnosis

Soft sensor development for mixed oil interface tracking in multi-product pipelines based on knowledge-informed semi-supervised Variational Bayesian Gaussian mixture regression

Novel Feasible Set Learning and Process Flexibility Analysis Method Using Deep Neural Networks

Distributionally robust chance-constrained optimization with Gaussian mixture ambiguity set

Data-driven distributionally robust chance-constrained optimization with large data set and outliers: Sequential sample removal algorithm for solution improvement

Benchmarking chemical neural ordinary differential equations to obtain reaction network-constrained kinetic models from spectroscopic data

Distributionally robust chance‐constrained optimization with Sinkhorn ambiguity set

How Can (or Why Should) Process Engineering Aid the Screening and Discovery of Solid Sorbents for CO2 Capture?

Can a Computer “Learn” Nonlinear Chromatography?: Experimental Validation of Physics-Based Deep Neural Networks for the Simulation of Chromatographic Processes

Bioinspired Encoder–Decoder Recurrent Neural Network with Attention for Hydroprocessing Unit Modeling

Real-time monitoring of reaction mechanisms from spectroscopic data using hidden semi-Markov models for mode identification

A recurrent neural network-based approach for joint chance constrained stochastic optimal control

Physics-Based Neural Networks for Simulation and Synthesis of Cyclic Adsorption Processes

Structure-Preserving Joint Non-negative Tensor Factorization to Identify Reaction Pathways Using Bayesian Networks

Multistage adaptive stochastic mixed integer optimization under endogenous and exogenous uncertainty

Collaborators (2)

Said Salim Rahal

Rajagopalan Srinivasan

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