인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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지원사업
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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract The present study aims at development of a mathematical model for the fixed-bed column adsorption that relates the reactor parameters with the breakthrough curve. Effects of operating parameters like bed height, flow rate, initial adsorbate concentration on the adsorption were investigated by using various breakthrough curves. The arbitrary constants of the developed model were found to be dependent on the operating parameters of the breakthrough kinetics. The proposed model showed incredible results (Breakthrough Curve R 2 > 0.98) for the referenced data. The flexibility of this model can be seen from the fact that the coefficients of parameters in the Arbitrary Constants Relation for the adsorbate–adsorbent pair are required to be determined only once and can be used repeatedly considering no change in any external factors affecting the working of the adsorbent. As the general adsorption curve follows a typical sigmoid curve, once the Arbitrary Constants Relations are known, the reactor can be optimized by selecting the accurate values of the reactor parameter leading to a slower C t / C o growth with respect to time. The information about the saturation limit of adsorbent can be used to predict attainment of the saturation limit. The proposed model will reduce the significant number of complicated experiments required to optimize the reactor. The model can also determine the time after which effluent concentration becomes 63.21% of the influent adsorbate concentration without any experimentation by using the Arbitrary Constants Relation, which is of great industrial importance.
#Saturation (graph theory)
#Adsorption
#Breakthrough curve
#Sigmoid function
#Limit (mathematics)
#Thermodynamics
#Volumetric flow rate
#Constant (computer programming)
#Mathematics
#Chemistry
#Materials science
#Mechanics
#Computer science
#Mathematical analysis
#Physics
#Physical chemistry
#Artificial neural network
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