Article-Journal

Data-driven dynamic inferential sensors based on causality analysis
Considering the stringent requirements for product quality of complex industrial processes, the purpose of this study is to apply …
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Arcobacter Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks
Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne …
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Multiscale model predictive control of battery systems for frequency regulation markets using physics-based models
We propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to …
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A scalable global optimization algorithm for stochastic nonlinear programs
We present a global optimization algorithm for two-stage stochastic nonlinear programs (NLPs). The algorithm uses a tailored …
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Graph-based modeling and simulation of complex systems
We present graph-based modeling abstractions to represent cyber-physical dependencies arising in complex systems. Specifically, we …
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Using stochastic programming and statistical extrapolation to mitigate long-term extreme loads in wind turbines
We propose stochastic programming formulations to enforce mechanical load requirements in wind turbine controller design procedures. …
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Machine learning algorithms for liquid crystal-based sensors
We present a machine learning (ML) framework to optimize the specificity and speed of liquid crystal (LC)-based chemical sensors. …
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Optimal PID controller tuning using stochastic programming techniques
We argue that stochastic programming provides a powerful framework to tune and analyze the performance limits of controllers. In …
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Scalable solution strategies for chance-constrained nonlinear programs
Probabilistic (chance) constraints are a powerful modeling paradigm that helps decision-makers control risk. Unfortunately, chance …
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Real-time feasible multi-objective optimization based nonlinear model predictive control of particle size and shape in a batch crystallization process
This paper presents nonlinear model predictive control (NMPC) and nonlinear moving horizon estimation (MHE) formulations for …
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