Using response surface methodology to investigate moving-bed reactor for lead removal

碩士 === 淡江大學 === 水資源及環境工程學系碩士班 === 99 === Fluidized-bed type reactors are frequently used in crystallization process. In this type of reactor, suspend solids (SS) produced by primary nucleation under supersaturated condition would flow out with upward flow, resulting in high SS in the effluent. Insta...

Full description

Bibliographic Details
Main Authors: Shang-Hung Weng, 翁上紘
Other Authors: 李奇旺
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/34182240619128994279
id ndltd-TW-099TKU05087006
record_format oai_dc
spelling ndltd-TW-099TKU050870062016-04-13T04:17:35Z http://ndltd.ncl.edu.tw/handle/34182240619128994279 Using response surface methodology to investigate moving-bed reactor for lead removal 以反應曲面法(RSM)探討移動結晶床除鉛之研究 Shang-Hung Weng 翁上紘 碩士 淡江大學 水資源及環境工程學系碩士班 99 Fluidized-bed type reactors are frequently used in crystallization process. In this type of reactor, suspend solids (SS) produced by primary nucleation under supersaturated condition would flow out with upward flow, resulting in high SS in the effluent. Installation of filter unit is needed to prevent suspend solids in the discharge. In this study, a novel reactor named “moving-bed reactor” having two functionalities, namely filtration and crystallization, was studied for lead (Pb) removal from aqueous solutions. According to literatures and design parameters of moving-bed reactor, five potential factors which may affect the process performance, are indentified, including lead concentration, pH, air-flow rate, the ratio of CO32-/Pb2+ and the height of sand. A 25-1 fractional factorial design is utilized to discuss the effect of factors on Pb removal efficiency, recovery efficiency and turbidity. Lead concentration and pH are the most significant factors affecting process performance. Subsequently, RSM with CCD design are used to build regression models for removal efficiency and recovery efficiency, respectively. The best operating condition was determined by uniting two regression models with pH 8.6 and 1.45×〖10〗^(-4)M of lead concentration obtained. Under the best operation condition, Pb removal efficiency and recovery efficiency are predicted to be 100.00% and 94.90%, respectively, by regression models. However, they are only 98.75% and 87.28%, respectively, experimentally. The differences are 1.25% and 8.00%, respectively, for Pb removal efficiency and recovery efficiency. The experiment can not reach the target that models predict because prediction of models is not precise. Based on quality analysis, crystallized Pb on the sand surface and precipitated Pb filtered by sand are 18.59% and 68.69%, respectively. The result indicated design of moving-bed reactor is inadequate to recover Pb. 李奇旺 2011 學位論文 ; thesis 73 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 淡江大學 === 水資源及環境工程學系碩士班 === 99 === Fluidized-bed type reactors are frequently used in crystallization process. In this type of reactor, suspend solids (SS) produced by primary nucleation under supersaturated condition would flow out with upward flow, resulting in high SS in the effluent. Installation of filter unit is needed to prevent suspend solids in the discharge. In this study, a novel reactor named “moving-bed reactor” having two functionalities, namely filtration and crystallization, was studied for lead (Pb) removal from aqueous solutions. According to literatures and design parameters of moving-bed reactor, five potential factors which may affect the process performance, are indentified, including lead concentration, pH, air-flow rate, the ratio of CO32-/Pb2+ and the height of sand. A 25-1 fractional factorial design is utilized to discuss the effect of factors on Pb removal efficiency, recovery efficiency and turbidity. Lead concentration and pH are the most significant factors affecting process performance. Subsequently, RSM with CCD design are used to build regression models for removal efficiency and recovery efficiency, respectively. The best operating condition was determined by uniting two regression models with pH 8.6 and 1.45×〖10〗^(-4)M of lead concentration obtained. Under the best operation condition, Pb removal efficiency and recovery efficiency are predicted to be 100.00% and 94.90%, respectively, by regression models. However, they are only 98.75% and 87.28%, respectively, experimentally. The differences are 1.25% and 8.00%, respectively, for Pb removal efficiency and recovery efficiency. The experiment can not reach the target that models predict because prediction of models is not precise. Based on quality analysis, crystallized Pb on the sand surface and precipitated Pb filtered by sand are 18.59% and 68.69%, respectively. The result indicated design of moving-bed reactor is inadequate to recover Pb.
author2 李奇旺
author_facet 李奇旺
Shang-Hung Weng
翁上紘
author Shang-Hung Weng
翁上紘
spellingShingle Shang-Hung Weng
翁上紘
Using response surface methodology to investigate moving-bed reactor for lead removal
author_sort Shang-Hung Weng
title Using response surface methodology to investigate moving-bed reactor for lead removal
title_short Using response surface methodology to investigate moving-bed reactor for lead removal
title_full Using response surface methodology to investigate moving-bed reactor for lead removal
title_fullStr Using response surface methodology to investigate moving-bed reactor for lead removal
title_full_unstemmed Using response surface methodology to investigate moving-bed reactor for lead removal
title_sort using response surface methodology to investigate moving-bed reactor for lead removal
publishDate 2011
url http://ndltd.ncl.edu.tw/handle/34182240619128994279
work_keys_str_mv AT shanghungweng usingresponsesurfacemethodologytoinvestigatemovingbedreactorforleadremoval
AT wēngshànghóng usingresponsesurfacemethodologytoinvestigatemovingbedreactorforleadremoval
AT shanghungweng yǐfǎnyīngqūmiànfǎrsmtàntǎoyídòngjiéjīngchuángchúqiānzhīyánjiū
AT wēngshànghóng yǐfǎnyīngqūmiànfǎrsmtàntǎoyídòngjiéjīngchuángchúqiānzhīyánjiū
_version_ 1718223097285312512