Zukui Li
YOU?
Author Swipe
View article: Sensitivity analysis and stochastic optimization of levelized cost of hydrogen production through methane pyrolysis
Sensitivity analysis and stochastic optimization of levelized cost of hydrogen production through methane pyrolysis Open
Hydrogen plays a crucial role across multiple industrial sectors and is increasingly recognized as a clean energy carrier with significant potential in decarbonization efforts. Methane pyrolysis (MP), particularly using molten metal cataly…
View article: Multistage Adaptive Robust Binary Optimization: Uncertainty Set Lifting versus Partitioning through Breakpoints Optimization
Multistage Adaptive Robust Binary Optimization: Uncertainty Set Lifting versus Partitioning through Breakpoints Optimization Open
Two methods for multistage adaptive robust binary optimization are investigated in this work. These methods referred to as binary decision rule and finite adaptability inherently share similarities in dividing the uncertainty set into subs…
View article: Haemoglobin response modelling under erythropoietin treatment: Physiological model‐informed machine learning method
Haemoglobin response modelling under erythropoietin treatment: Physiological model‐informed machine learning method Open
Patients with renal anaemia are usually treated with recombinant human erythropoietin (EPO) because of insufficient renal EPO secretion. The establishment of a good haemoglobin (Hgb) response model is a necessary condition for dose optimiz…
View article: Can a computer “learn” non-linear chromatography?: Experimental validation of physics-based deep neural networks for the simulation of chromatographic processes
Can a computer “learn” non-linear chromatography?: Experimental validation of physics-based deep neural networks for the simulation of chromatographic processes Open
This article presents the capabilities of machine learning in addressing the challenges related to the accurate description of adsorption equilibria in the design of chromatographic processes. Our previously developed physics-based artific…
View article: Can a computer “learn” non-linear chromatography?: Experimental validation of physics-based deep neural networks for the simulation of chromatographic processes
Can a computer “learn” non-linear chromatography?: Experimental validation of physics-based deep neural networks for the simulation of chromatographic processes Open
This article presents the capabilities of machine learning in addressing the challenges related to the accurate description of adsorption equilibria in the design of chromatographic processes. Our previously developed physics-based artific…
View article: Physics-based neural networks for simulation and synthesis of cyclic adsorption processes
Physics-based neural networks for simulation and synthesis of cyclic adsorption processes Open
A computationally faster and reliable modelling approach called a physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) is developed. PANACHE uses deep neural networks for cycle synthesis a…
View article: Recurrent Neural Network-Based Joint Chance Constrained Stochastic Model Predictive Control*
Recurrent Neural Network-Based Joint Chance Constrained Stochastic Model Predictive Control* Open
A novel recurrent neural network (RNN)-based approach is proposed in this work to handle joint chance-constrained stochastic model predictive control (SMPC) problem. In the proposed approach, the joint chance constraint (JCC) is first refo…
View article: Physics-based neural networks for simulation and synthesis of cyclic adsorption processes
Physics-based neural networks for simulation and synthesis of cyclic adsorption processes Open
A computationally faster and reliable modelling approach called a physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) is developed. PANACHE uses deep neural networks for cycle synthesis a…
View article: Hybrid Strategies using Linear and Piecewise-Linear Decision Rules for Multistage Adaptive Linear Optimization
Hybrid Strategies using Linear and Piecewise-Linear Decision Rules for Multistage Adaptive Linear Optimization Open
Decision rules offer a rich and tractable framework for solving certain classes of multistage adaptive optimization problems. Recent literature has shown the promise of using linear and nonlinear decision rules in which wait-and-see decisi…
View article: EPO Dosage Optimization for Anemia Management: Stochastic Control under Uncertainty Using Conditional Value at Risk
EPO Dosage Optimization for Anemia Management: Stochastic Control under Uncertainty Using Conditional Value at Risk Open
Due to insufficient endogenous production of erythropoietin, chronic kidney disease patients with anemia are often treated by the administration of recombinant human erythropoietin (EPO). The target of the treatment is to keep the patient’…
View article: Multistage Adaptive Optimization for Steelmaking and Continuous Casting Scheduling under Processing Time Uncertainty
Multistage Adaptive Optimization for Steelmaking and Continuous Casting Scheduling under Processing Time Uncertainty Open
The scheduling of steelmaking and continuous casting process is an important exercise for the steel industry. Uncertain processing time can significantly affect the schedule performance. To tackle this uncertainty, a multistage adaptive op…
View article: Change Point Detection using the Kantorovich Distance Algorithm
Change Point Detection using the Kantorovich Distance Algorithm Open
In this article, a novel change detection algorithm is proposed based on the Kantorovich distance concept. Incorporating the proposed change detection algorithm with the existing process monitoring tools may assist the operator in detectin…
View article: Chance Constrained Planning and Scheduling under Uncertainty using Robust Optimization Approximation
Chance Constrained Planning and Scheduling under Uncertainty using Robust Optimization Approximation Open
Robust optimization can provide safe and tractable analytical approximation for the chance constrained optimization problem. In this work, we studied the application of robust optimization approximation in solving chance constrained planni…
View article: Well Placement Optimization with Geological Uncertainty Reduction
Well Placement Optimization with Geological Uncertainty Reduction Open
Well placement optimization aims to determine optimal well locations so that the economic benefit from oil production can be maximized. Geological uncertainty has a significant impact on the optimal well placement plan and therefore has to…
View article: Pipeline Leak Detection Using Particle Filters
Pipeline Leak Detection Using Particle Filters Open
While most of the available Leak Detection Systems (LDS) can detect pipeline leaks, leak localization is still an unresolved problem. The main reason for this problem is the limited number of sensors installed in long pipelines. Because of…