Inconsiderable Amount of Phytoplankton Leakage by Algal Mat Peeling from a Slow Sand Filter.

1997 ◽  
Vol 33 (4) ◽  
pp. 179-186 ◽  
Author(s):  
NOBUTADA NAKAMOTO ◽  
NORIYASU IWASE ◽  
KENTARO NOZAKI ◽  
MASASHI SAKAI
Keyword(s):  
Sand Filter ◽  
Slow Sand Filter ◽  
Algal Mat

Oxygen production and bubble formation by algal mat on the bed in a slow sand filter pond

2000 ◽  
Vol 27 (5) ◽  
pp. 3092-3096
Author(s):  
Nobutada Nakamoto ◽  
Mitsuharu Yasuda ◽  
Masashi Sakai ◽  
Kentaro Nozaki
Keyword(s):  
Bubble Formation ◽  
Oxygen Production ◽  
Sand Filter ◽  
Slow Sand Filter ◽  
Algal Mat

scholarly journals Effect of Supernatant Water Depth on Algal Mat Development in Slow Sand Filter.

1997 ◽  
Vol 33 (3) ◽  
pp. 117-125
Author(s):  
NOBUTADA NAKAMOTO ◽  
DAISUKE IKEDA ◽  
KAYO TAGUCHI ◽  
MASUO YAMAMOTO
Keyword(s):  
Water Depth ◽  
Sand Filter ◽  
Slow Sand Filter ◽  
Algal Mat

scholarly journals Verification of filter efficiency of horizontal roughing filter by Weglin's design criteria and Artificial Neural Network

2009 ◽  
Vol 2 (1) ◽  
pp. 21-27 ◽  
Author(s):  
◽  
◽  
◽  
◽  
Keyword(s):  
Genetic Model ◽  
Neural Model ◽  
Model Verification ◽  
Design Criteria ◽  
Experimental Setup ◽  
Mean Square ◽  
Raw Water ◽  
Neural Models ◽  
Sand Filter ◽  
Slow Sand Filter

Abstract. The general objective of this study is to estimate the performance of the Horizontal Roughing Filter (HRF) by using Weglin's design criteria based on 1/3–2/3 filter theory. The main objective of the present study is to validate HRF developed in the laboratory with Slow Sand Filter (SSF) as a pretreatment unit with the help of Weglin's design criteria for HRF with respect to raw water condition and neuro-genetic model developed based on the filter dataset. The results achieved from the three different models were compared to find whether the performance of the experimental HRF with SSF output conforms to the other two models which will verify the validity of the former. According to the results, the experimental setup was coherent with the neural model but incoherent with the results from Weglin's formula as lowest mean square error was observed in case of the neuro-genetic model while comparing with the values found from the experimental SSF-HRF unit. As neural models are known to learn a problem with utmost efficiency, the model verification result was taken as positive.

scholarly journals A23 Seasonal abundance of chironomid larvae in slow sand filter bed

2002 ◽  
Vol 53 (Supplement) ◽  
pp. 41
Author(s):  
K. Hirabayashi ◽  
N. Nakamoto ◽  
S. Tanizaki
Keyword(s):  
Seasonal Abundance ◽  
Sand Filter ◽  
Chironomid Larvae ◽  
Slow Sand Filter ◽  
Filter Bed

scholarly journals Influence of slow sand filter cleaning process type on filter media biomass: backwashing versus scraping

2021 ◽  
Vol 189 ◽  
pp. 116581 ◽  
Author(s):  
F.H. de Souza ◽  
P.B. Roecker ◽  
D.D. Silveira ◽  
M.L. Sens ◽  
L.C. Campos
Keyword(s):  
Cleaning Process ◽  
Filter Media ◽  
Sand Filter ◽  
Slow Sand Filter

scholarly journals Streptophyta and Acetic Acid Bacteria Succession Promoted by Brass in Slow Sand Filter System Schmutzdeckes

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ma. Carmen E. Delgado-Gardea ◽  
Patricia Tamez-Guerra ◽  
Ricardo Gomez-Flores ◽  
Mariela Garfio-Aguirre ◽  
Beatriz A. Rocha-Gutiérrez ◽  
...  
Keyword(s):  
Acetic Acid ◽  
Acetic Acid Bacteria ◽  
Sand Filter ◽  
Filter System ◽  
Slow Sand Filter

scholarly journals Occurrence of Phytophthora Species in Commercial Nursery Production

2016 ◽  
Vol 17 (2) ◽  
pp. 64-75 ◽  
Author(s):  
Corina Junker ◽  
Patrick Goff ◽  
Stefan Wagner ◽  
Sabine Werres
Keyword(s):  
Phytophthora Species ◽  
Phytophthora Ramorum ◽  
Water Recycling ◽  
Infection Rates ◽  
Sand Filter ◽  
Detection Rates ◽  
Water And Sediment ◽  
Nursery Production ◽  
Slow Sand Filter ◽  
Soil Substrates

Two commercial woody ornamental nurseries were sampled for the presence of Phytophthora species over a period of three years between 2011 and 2014. The samples were taken every two months at different propagation (greenhouses, plastic tunnels) and cultivation (container stands) areas as well as from nearby pathways and from a water recycling system with a slow sand filter. Furthermore, different materials like soil, substrates, residues, wind-carried leaves, water and sediment were sampled. In total, 12 known Phytophthora species could be detected. Further, three isolates did not match any of the known species. Phytophthora ramorum, P. gonapodyides, and P. plurivora were the species with the highest detection rates. Phytophthora ramorum could be detected during all seasons of the year. In total, the puddles on the pathways had the highest percentage of positive detections. Residues, wind-carried leaves and water and sediment from the water runoffs were also good places for Phytophthora survival. In both nurseries, the plant samples showed very low infection rates. Ideas for surveys and management are discussed. Accepted for publication 14 March 2016. Published 11 April 2016.

Slow sand filtration pre-treatment with alum is efficient, but is it effective?

2013 ◽  
Vol 3 (2) ◽  
pp. 106-111 ◽  
Author(s):  
C. C. Dorea
Keyword(s):  
Process Control ◽  
High Efficiency ◽  
Sand Filters ◽  
Sand Filter ◽  
Limited Effectiveness ◽  
Scale Field ◽  
Slow Sand Filter ◽  
High Turbidity ◽  
Pre Treatment ◽  
Run Lengths

Enhancing the performance of slow sand filter pre-treatment with alum (i.e. direct or contact filtration) has been proposed as an alternative to overcome limitations of conventional gravel (roughing) pre-filtration. Experimental results revealed high turbidity reduction efficiencies in alum-dosed pre-filtration. However, due to the alum coagulation, the nature of the particulates in the pre-treatment effluent changed and had a potential to shorten the downstream slow sand filter run lengths by approximately 50% under the conditions tested. Hence, depending on the effluent turbidity levels the effectiveness of the alum-dosed pre-treatment could be compromised, despite its high efficiency. Relatively low turbidity levels (<2 NTU) were needed to minimise excessive headloss development in alum-dosed filters. However, the necessary resources to achieve such process control may not be available in developing country contexts. Furthermore, full-scale field experiences indicated the limited effectiveness of alum dosing prior to slow sand filters and the difficulties in maintaining an adequate chemical dosing in under-resourced settings.

Removal of particulates, natural organic matters, and microorganisms in a surface amended slow sand filter

2002 ◽  
Vol 2 (5-6) ◽  
pp. 387-394
Author(s):  
H.-B. Jun ◽  
Y.-J. Lee ◽  
S.-S. Shin
Keyword(s):  
Surface Treatment ◽  
Raw Water ◽  
Sand Filters ◽  
Water Turbidity ◽  
Turbidity Removal ◽  
Sand Filter ◽  
Organic Matters ◽  
Slow Sand Filter ◽  
Natural Organic Matters ◽  
Filter Bed

Removal characteristics of particulates, natural organic matters, and microorganisms with six slow sand filter units were measured with a diameter of 50 mm and packed with sand to a depth of 50, 150, 300, 600, and two 700 mm, respectively. One of the 700 mm depth filters was amended by covering the surface of the filter bed with a prefilter. The raw water turbidity and pH was in the range of 1.5-2.0 NTU, and 7.0-7.7, respectively. Turbidity in each filter effluent was decreased as the depth of filter medium increased. However, a greater part of influent turbidity was removed within the top layer of the slow sand filters. Turbidity removal in the 700 mm depth filter with prefilter was similar to that without the prefilter, however, the removal of particles smaller than 2 mm was improved with the prefilter. The particles greater than 10 mm could be removed within the upper 50 mm depth in the slow sand filter. A greater fraction of the particles smaller than 2 mm was removed within the upper 50 mm, however, they were also removed in the deeper sand bed. The removal efficiency of DBP precursors represented by DOC and UV-254 absorbance was 9.2-31% and 2-31%, respectively. pH drop in the 50 mm depth filter was 0.12, while that in the 700 mm depth filter was 0.19. The effects of surface treatment with prefilter on UVA and DOC were not apparent.

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