Process Modelling and SimulationCésar de Prada, Constantinos Pantelides, José Luis Pitarch Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications. |
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addition analysis applications approach approximation balance barley behavior calculated changes Chem coefficient combined computational concentration considered constraints correlation corresponding CrossRef defined depends determined developed differential diffusion directions distribution drying dynamic effect efficiency energy Engineering Equation error estimation experimental experiments extents extraction factor Figure final flow fluid function GEKKO given granulation heat identification important indices industrial initial inlet input kinetic limited linear liquid mass materials mathematical matrix mean measurements melting method moisture nonlinear objective observable obtained operation optimization output parameter estimation parameters particles performance phase plant predicted pressure problem production proposed pump range reaction reactor regression respectively robust sample scrap sensitivity shown simulation solution solve steam structure subset surface Table temperature transfer validation variables volume wave