Membrane technology is becoming more important for CO,_ separation from natural gas in the new era due to its process simplicity, relative ease of operation and control, compact, and easy to scale up as compared with ...Membrane technology is becoming more important for CO,_ separation from natural gas in the new era due to its process simplicity, relative ease of operation and control, compact, and easy to scale up as compared with conventional processes. Conventional processes such as absorption and adsorption for CO2 separation from natural gas are generally more energy demanding and costly for both operation and maintenance. Polymeric membranes are the current commercial membranes used for CO2 separation from natural gas. However, polymeric membranes possess drawbacks such as low permeability and selectivity, plasticization at high temperatures, as well as insufficient thermal and chemical stability. The shortcomings of commercial polymeric membranes have motivated researchers to opt for other alternatives, especially inorganic membranes due to their higher thermal stability, good chemical resistance to solvents, high mechanical strength and long lifetime. Surface modifications can be utilized in inorganic membranes to further enhance the selectivity, permeability or catalytic activities of the membrane. This paper is to provide a comprehensive review on gas separation, comparing membrane technology with other conventional methods of recovering CO2 from natural gas, challenges of current commercial polymeric membranes and inorganic membranes for CO2 removal and membrane surface modification for improved selectivity.展开更多
The disposal of spent activated carbon(AC) will inevitably create secondary pollution. In overcoming this problem, the spent AC can be regenerated by means of biological approach. Bioregeneration is the phenomenon in ...The disposal of spent activated carbon(AC) will inevitably create secondary pollution. In overcoming this problem, the spent AC can be regenerated by means of biological approach. Bioregeneration is the phenomenon in which through the action of microorganisms, the adsorbed pollutants on the surface of the AC will be biodegraded and this enables further adsorption of pollutants to occur with time elapse. This review provides the challenges and perspectives for effective bioregeneration to occur in biological activated carbon(BAC)column. Owing to very few reported works on the bioregeneration rate in BAC column, emphasis is put forward on the recently developed models of bioregeneration kinetic in batch system. All in all, providing potential solutions in increasing the lifespan of AC and the enhancement of bioregeneration rate will definitely overcome the bottlenecks in spent AC bioregeneration.展开更多
The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a...The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed.展开更多
The study of trace metals in the atmosphere and lake water is important due to their critical effects on humans, aquatic animals and the geochemical balance of ecosystems. The objective of this study was to investigat...The study of trace metals in the atmosphere and lake water is important due to their critical effects on humans, aquatic animals and the geochemical balance of ecosystems. The objective of this study was to investigate the concentration of trace metals in atmospheric and lake water samples during the rainy season （before and after precipitation） between November and December 2015. Typical methods of sample preparation for trace metal determination such as cloud point extraction, solid phase extraction and dispersive liquid- liquid micro-extraction are time-consuming and difficult to perform; therefore, there is a crucial need for development of more effective sample preparation procedure. A convection microwave assisted digestion procedure for extraction of trace metals was developed for use prior to inductively couple plasma-mass spectrometric determination. The result showed that metals like zinc （133.50-419.30 μg/m3） and aluminum （53.58-378.93 μg/m3） had higher concentrations in atmospheric samples as compared to lake samples before precipitation. On the other hand, the concentrations of zinc, aluminum, chromium and arsenic were significantly higher in lake samples after precipitation and lower in atmospheric samples. The relationship between physicochemical parameters （pH and turbidity） and heavy metal concentrations was investigated as well. Furthermore, enrichment factor analysis indicated that anthropogenic sources such as soil dust, biomass burning and fuel combustion influenced the metal concentrations in the atmosphere.展开更多
The aim of this study was to determine the source apportionment of dust fall around Lake Chini, Malaysia. Samples were collected monthly between December 2012 and March2013 at seven sampling stations located around La...The aim of this study was to determine the source apportionment of dust fall around Lake Chini, Malaysia. Samples were collected monthly between December 2012 and March2013 at seven sampling stations located around Lake Chini. The samples were filtered to separate the dissolved and undissolved solids. The ionic compositions（NO-3, SO2-4, Cl-and NH＋4） were determined using ion chromatography（IC） while major elements（K, Na, Ca and Mg） and trace metals（Zn, Fe, Al, Ni, Mn, Cr, Pb and Cd） were determined using inductively coupled plasma mass spectrometry（ICP-MS）. The results showed that the average concentration of total solids around Lake Chini was 93.49 ± 16.16 mg/（m2·day）. SO2-4, Na and Zn dominated the dissolved portion of the dust fall. The enrichment factors（EF） revealed that the source of the trace metals and major elements in the rain water was anthropogenic, except for Fe. Hierarchical agglomerative cluster analysis（HACA） classified the seven monitoring stations and 16 variables into five groups and three groups respectively. A coupled receptor model, principal component analysis multiple linear regression（PCA-MLR）, revealed that the sources of dust fall in Lake Chini were dominated by agricultural and biomass burning（42%）,followed by the earth＇s crust（28%）, sea spray（16%） and a mixture of soil dust and vehicle emissions（14%）.展开更多
This paper studies the effects of integrating four inventory classification methods into constant work-in-process(CONWIP)systems in a multistage batch production.The inventory classification methods are multiple close...This paper studies the effects of integrating four inventory classification methods into constant work-in-process(CONWIP)systems in a multistage batch production.The inventory classification methods are multiple closed-loop CONWIP(MCC),parallel CONWIP(PC),analytical hierarchy procedure,and weighted linear optimization(WLO).Discrete-event simulation models were built.A full-factorial experimental design was employed.Response surface methodology generated suitable regression models for performance comparison of different ABC analysis methods.In the second stage of experiment,the ABC analysis methods from the first stage were fine-tuned and compared.PC and WLO are the most preferred methods because of higher total outputs,lower work-in-process levels,and shorter flow times compared with the other two methods.However,the revenue accrued through WLO is higher than that accrued via PC.This study complements existing literature on price setting in manufacturing organizations by delineating classification of finished goods based on both exogenous and production factors.展开更多
The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual elec...The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggrega-tion methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodol-ogy. The contribution of this paper is in utilizing the “k- value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.展开更多
基金supported by the Ministry of Higher Education Malaysia through Long Term Research Grant Scheme (A/C Number 2110226-113-00)
文摘Membrane technology is becoming more important for CO,_ separation from natural gas in the new era due to its process simplicity, relative ease of operation and control, compact, and easy to scale up as compared with conventional processes. Conventional processes such as absorption and adsorption for CO2 separation from natural gas are generally more energy demanding and costly for both operation and maintenance. Polymeric membranes are the current commercial membranes used for CO2 separation from natural gas. However, polymeric membranes possess drawbacks such as low permeability and selectivity, plasticization at high temperatures, as well as insufficient thermal and chemical stability. The shortcomings of commercial polymeric membranes have motivated researchers to opt for other alternatives, especially inorganic membranes due to their higher thermal stability, good chemical resistance to solvents, high mechanical strength and long lifetime. Surface modifications can be utilized in inorganic membranes to further enhance the selectivity, permeability or catalytic activities of the membrane. This paper is to provide a comprehensive review on gas separation, comparing membrane technology with other conventional methods of recovering CO2 from natural gas, challenges of current commercial polymeric membranes and inorganic membranes for CO2 removal and membrane surface modification for improved selectivity.
基金financial support from the Universiti Teknologi PETRONAS via YUTP-FRG(0153AA-E48)
文摘The disposal of spent activated carbon(AC) will inevitably create secondary pollution. In overcoming this problem, the spent AC can be regenerated by means of biological approach. Bioregeneration is the phenomenon in which through the action of microorganisms, the adsorbed pollutants on the surface of the AC will be biodegraded and this enables further adsorption of pollutants to occur with time elapse. This review provides the challenges and perspectives for effective bioregeneration to occur in biological activated carbon(BAC)column. Owing to very few reported works on the bioregeneration rate in BAC column, emphasis is put forward on the recently developed models of bioregeneration kinetic in batch system. All in all, providing potential solutions in increasing the lifespan of AC and the enhancement of bioregeneration rate will definitely overcome the bottlenecks in spent AC bioregeneration.
文摘The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed.
基金supported by the Universiti Tunku Abdul Rahman under Universiti Research Grant(No.IPSR/RMC/UTARRF/C1-13/G03)
文摘The study of trace metals in the atmosphere and lake water is important due to their critical effects on humans, aquatic animals and the geochemical balance of ecosystems. The objective of this study was to investigate the concentration of trace metals in atmospheric and lake water samples during the rainy season （before and after precipitation） between November and December 2015. Typical methods of sample preparation for trace metal determination such as cloud point extraction, solid phase extraction and dispersive liquid- liquid micro-extraction are time-consuming and difficult to perform; therefore, there is a crucial need for development of more effective sample preparation procedure. A convection microwave assisted digestion procedure for extraction of trace metals was developed for use prior to inductively couple plasma-mass spectrometric determination. The result showed that metals like zinc （133.50-419.30 μg/m3） and aluminum （53.58-378.93 μg/m3） had higher concentrations in atmospheric samples as compared to lake samples before precipitation. On the other hand, the concentrations of zinc, aluminum, chromium and arsenic were significantly higher in lake samples after precipitation and lower in atmospheric samples. The relationship between physicochemical parameters （pH and turbidity） and heavy metal concentrations was investigated as well. Furthermore, enrichment factor analysis indicated that anthropogenic sources such as soil dust, biomass burning and fuel combustion influenced the metal concentrations in the atmosphere.
基金the Malaysian Ministry of Higher Education for funding via Fundamental Research Grants (UKMTOPDOWN-ST-08-FRGS0003-2010 and FRGS/1/ 2013/SPWN01/UKM/02/)
文摘The aim of this study was to determine the source apportionment of dust fall around Lake Chini, Malaysia. Samples were collected monthly between December 2012 and March2013 at seven sampling stations located around Lake Chini. The samples were filtered to separate the dissolved and undissolved solids. The ionic compositions（NO-3, SO2-4, Cl-and NH＋4） were determined using ion chromatography（IC） while major elements（K, Na, Ca and Mg） and trace metals（Zn, Fe, Al, Ni, Mn, Cr, Pb and Cd） were determined using inductively coupled plasma mass spectrometry（ICP-MS）. The results showed that the average concentration of total solids around Lake Chini was 93.49 ± 16.16 mg/（m2·day）. SO2-4, Na and Zn dominated the dissolved portion of the dust fall. The enrichment factors（EF） revealed that the source of the trace metals and major elements in the rain water was anthropogenic, except for Fe. Hierarchical agglomerative cluster analysis（HACA） classified the seven monitoring stations and 16 variables into five groups and three groups respectively. A coupled receptor model, principal component analysis multiple linear regression（PCA-MLR）, revealed that the sources of dust fall in Lake Chini were dominated by agricultural and biomass burning（42%）,followed by the earth＇s crust（28%）, sea spray（16%） and a mixture of soil dust and vehicle emissions（14%）.
基金This work was supported by The Ministry of Education Malaysia[USM FRGS 4471/J02]Universiti Tunku Abdul Rahman[UTARRF 6200/J09].
文摘This paper studies the effects of integrating four inventory classification methods into constant work-in-process(CONWIP)systems in a multistage batch production.The inventory classification methods are multiple closed-loop CONWIP(MCC),parallel CONWIP(PC),analytical hierarchy procedure,and weighted linear optimization(WLO).Discrete-event simulation models were built.A full-factorial experimental design was employed.Response surface methodology generated suitable regression models for performance comparison of different ABC analysis methods.In the second stage of experiment,the ABC analysis methods from the first stage were fine-tuned and compared.PC and WLO are the most preferred methods because of higher total outputs,lower work-in-process levels,and shorter flow times compared with the other two methods.However,the revenue accrued through WLO is higher than that accrued via PC.This study complements existing literature on price setting in manufacturing organizations by delineating classification of finished goods based on both exogenous and production factors.
文摘The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggrega-tion methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodol-ogy. The contribution of this paper is in utilizing the “k- value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.
