Dynamic simulation of a forced circulation evaporating system

Lee, Joo Sang; Lee, Kun Jai

doi:10.1016/0306-4549(93)90113-4

Cited by 3 publications

(2 citation statements)

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“…The multiple‐effect evaporation model shown here is based on mass and energy conservation equations, as well as on thermodynamic equilibria in multiphase systems. It is similar to models developed for the tomato pulp concentration process and forced circulation systems , . Crystallization is modeled taking into account the crystal population conservation, using the moments transformation of the population balance for particulate systems , , coupled to expressions for the kinetics of crystallization of NaCl.…”

Section: Process Descriptionmentioning

confidence: 99%

“…Considering that the dynamics of the heat exchanger is fast enough to be considered negligible in comparison with the crystallizer dynamics and there is no heat loss to the neighborhood, it is possible to estimate the vapor production by combining Eqs. and resulting in the following expression:

true F_{normalV, i - 1} = \frac{U_{i} A_{i} Δ T_{normallm}}{[λ (T_{normals, i - 1}) + H_{v} (T_{i - 1}) - H_{v} (T_{s, i - 1})]}

…”

Section: Process Descriptionmentioning

confidence: 99%

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Application of Model Predictive Control to a Continuous Multiple‐Effect Crystallizer

2018

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An infinite horizon model predictive controller (IHMPC) with zone control is applied to a continuous five-effect evaporative sodium chloride crystallizer. Firstly, a phenomenological dynamic model of the process is developed considering mass, energy, and moment balances coupled to crystallization kinetics. The developed model plays the role of the real system in order to study the proposed optimization/control strategy. The proposed approach is compared to a classical proportional integral derivative (PID) control system. The control strategy based on the prediction of the future state of the plant provides a faster response, a better stability to the process, and a reduction in energy consumption.

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Section: Process Descriptionmentioning

confidence: 99%