Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

A gas permeation model (Coupling Model) has been developed which has the flexibility to be used for different membrane module configurations. The aim of this work is to predict the performance of a single stage gas separation process using membranes and provide a comprehensive description of process parameters like flow rates, composition, stage cut and stream pressure. The significant feature ...

We present a first-order extension of the algebraic theory about processes known as ACP and its main models. Useful predicates on processes, such as deadlock freedom and determinism, can be added to this theory through first-order definitional extensions. Model theory is used to analyse the discrepancies between identity in the models of the first-order extension of ACP and bisimilarity of the ...

Bringing system and process models together in repositories facilitates the interchange of model information between modelling tools, and allows the combination and interlinking of complementary models. Petriweb is a web application for managing such repositories. It supports hierarchical process models, represented in a standard format, PNML (the Petri Net Markup Language). Properties can be a...

Consider the situation where we have some pre-trained classification models for bike rental stations (or any other spatially located data). Given a new rental station (deployment context), we imagine that there might be some rental stations that are more similar to this station in terms of the daily usage patterns, whether or not these stations are close by or not. We propose to use a Gaussian ...