arXiv subjects

Jose A. Romagnoli

Publications and source records attributed to Jose A. Romagnoli.

2 recordsLinked to original sources

arXiv2026

A Plantwide Simulation Library for Control-Oriented Design: Analytical and Data-driven Use Cases

Control structures and operability indices for plantwide processes are developed and compared on fixed simulated flowsheets, and the configuration of a study is rarely reported in a form that another group can reproduce. In this contribution, plantbench, a library of plantwide process simulations in which the design, the controller tuning and the instrumentation of a plant are declared as hashed plain-data configurations, is presented. On a heat-integrated reactor--separator--recycle plant with an open-loop unstable reactor, a heuristic plantwide control design prices four heat-exchanger networks (HENs) by the least damping ratio of the linearized closed loop, and 2016 closed-loop runs over networks, tuning, analyzer deadtime and upsets test that ranking. Results show that the damping ratio orders the networks as the runs do with the network that recovers the most heat reaching 0.013 against 0.157 for the selected one and cycling under a throughput increase, but that it is a weak detector of which operation will oscillate. At the recall of a random forest on the first 100 min after an upset, 60 of 82 limit cycles, a damping-ratio threshold raises 173 false alarms against 5. The integral time of the temperature loop, to which the damping ratio is nearly insensitive, decides in part whether the plant holds an operating point.

eess.SY↗

arXiv2021

PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

Widespread adoption of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) and HT-PEM electrochemical hydrogen pumps (HT-PEM ECHPs) requires models and computational tools that provide accurate scale-up and optimization. Knowledge-based modeling has limitations as it is time consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling on the other hand, is easier to implement, but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are uniquely combined by implementing a Few-Shot Learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFC was used to generate simulated data (887,735 points) and used to pretrain a neural network source model. Furthermore, the source model developed for HT-PEMFCs was successfully applied to HT-PEM ECHPs - a different electrochemical system that utilizes similar materials to the fuel cell. Experimental datasets from both HT-PEMFCs and HT-PEM ECHPs with different materials and operating conditions (~50 points each) were used to train 8 target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE between 1.04 and 3.73% for HT-PEMCs and between 6.38 and 8.46% for HT-PEM ECHPs).

cs.LG↗