2016
DOI: 10.1016/j.compchemeng.2016.04.028
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General optimization strategy of simulated moving bed units through design of experiments and response surface methodologies

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Cited by 29 publications
(18 citation statements)
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“…7(j)–(l)) represent the cases of initial feed pattern, middle feed pattern and late feed pattern, respectively. The second‐order polynomial correlation 29 as expressed in Eq. (30) was used to describe each surface plot and the associated coefficients are presented in Table 3.…”
Section: Resultsmentioning
confidence: 99%
“…7(j)–(l)) represent the cases of initial feed pattern, middle feed pattern and late feed pattern, respectively. The second‐order polynomial correlation 29 as expressed in Eq. (30) was used to describe each surface plot and the associated coefficients are presented in Table 3.…”
Section: Resultsmentioning
confidence: 99%
“…Recently, simple design approaches were reported to find expressions for performance parameters as functions of control variables [26,42,43]. The standing wave concept was developed for analytic solutions for solvent consumption and productivity in size-exclusive SMB (SEC-SMB) as functions of the material and design parameters [42,43].…”
Section: Boundary Conditionsmentioning
confidence: 99%
“…Speedy standing wave design (SSWD) offers guidelines for designing the operating parameters as well as equipment parameters such as column length, maximum pressure drop, etc. In addition, experimental design and response surface methodology (DoE-RSM) was introduced to optimize the SMB operations [26]. Since DoE-RSM is a statistical tool to evaluate the relationships between independent variables and dependent parameters, purity and productivity are presented as second-degree polynomial functions of the flow rate ratios of zones II and III from data sets of a few simulation runs.…”
Section: Boundary Conditionsmentioning
confidence: 99%
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“…Marco Mazzotti first proposed the triangle theory (Mazzotti et al., 1997) under ideal assumptions, ignoring axial dispersion and mass transfer resistance. In recent years, genetic algorithms or particle swarm optimization (PSO) have been used for SMB process design (Aniceto, 2016; Neto et al., 2016). The genetic algorithm requires copy, crossover, and mutation operations, affecting its efficiency.…”
Section: Introductionmentioning
confidence: 99%