A dynamic simulation model of a continuous horizontal fluidized bed dryer

Event‐triggered data‐ and knowledge‐driven adaptive quality iterative learning control with uncertainty for a pharmaceutical cyber‐physical system

“…For any iteration in the PQC process, there are totally three states for ΔV : ΔV k < 0; ΔV k ¼ 0; ΔV k > 0. Specifically, (12) illustrates the variation in the tracking error between the previous and current iterations. If ΔV k < 0 happens, it indicates a decrease in the tracking error from the previous iteration; ΔV k ¼ 0 denotes no change in tracking error between the previous and current iterations; similarly, ΔV k > 0 signifies an increase in the tracking error from the previous iteration.…”

“…From a lateral perspective, on the other hand, the related PQC methodologies can also be divided into model-based control (MBC) and model-free control (MFC), depending on whether predictive models are used. As a typical representative of MBC, model predictive control (MPC) has found broad application in PQC studies of various pharmaceutical process units, [9] such as crystallization, [10] feed blending, [11] fluidized bed drying, [12] tableting, [13,14] etc. But such studies rely on process mechanism models, whose modelling complexities and analytical solutions seriously hinder the timeliness of decisionmaking.…”

Simulation of a continuous fluidised bed dryer for paddy

“…Most of the existing models of plug-flow fluidised bed dryers are based on knowledge of the interaction between the solid and gas phases. For instance, Gagnon et al (2021) developed a dynamic model with lumped parameters of a continuous horizontal fluidised bed dryer equipped with a screw conveyor. The dynamic model includes a two-phase tanks-in-series configuration, thus considering gas bypasses (bubbles) and product axial mixing.…”