other · 2026
Process Modeling
Cited by 0Open access
Abstract
Process models can be broadly categorized as steady state and dynamic.Steady-state models are largely used for process and equipment design and real-time optimization (RTO) of continuous processes.Dynamic models are used for system acceptance testing (SAT), operator training systems (OTS), and process control improvement (PCI).A steady-state or dynamic model can be experimental or first principle.Experimental models are identified from process testing.First principle models are developed using the equations for charge, energy, material, and momentum balances; equilibrium relationships; and driving forces.Steady-state models can be multivariate statistical, neural-network, or first principle models.Multivariate statistical and neural-network models are primarily used to detect abnormalities and make predictions in process operation.First principle steady-state models are widely used by process design engineers to develop process flow diagrams and, to a much lesser extent, by advanced control engineers for RTO.Multivariate statistical and neural-network dynamic models presently rely on the insertion of dead-time blocks on process inputs to model the effect on downstream process outputs.The lack of a process time constant or integrating response means that these models cannot be used for testing feedback control systems.For batch processes, the prediction of batch endpoint conditions does not require dead-time blocks because synchronization with current values is not required.Dynamic first principle models should include the dynamics of the automation system in addition to the process as shown in Figure 17-1.Valve and variable-speed drive (VSD) models should include the installed flow characteristic, resolution and sensitivity limits, deadband, dead time, and a velocity limited exponential response.Measurement models should include transportation delays, sensor lag and delay, signal filtering, transmitter damping, resolution and sensitivity limits, and update delays.For analyzers, the measurement models A Guide to the Automation Body of Knowledge.
Cite this paper
McMillan, G. K. (2026). Process Modeling. https://doi.org/10.1002/9781394438105.ch17
McMillan, Gregory K. “Process Modeling.” 2026, pp. 285–98, https://doi.org/10.1002/9781394438105.ch17.
McMillan, Gregory K. 2026. “Process Modeling.” https://doi.org/10.1002/9781394438105.ch17.
McMillan, G.K. (2026) ‘Process Modeling’. Available at: https://doi.org/10.1002/9781394438105.ch17.
@misc{mcmillan2026process,
title = {{Process Modeling}},
author = {McMillan, Gregory K.},
pages = {285--298},
year = {2026},
doi = {10.1002/9781394438105.ch17}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.