Yeasts occurring in the effluent disposal basins of a pulp mill in Saskatchewan

1974 ◽  
Vol 20 (7) ◽  
pp. 993-998 ◽  
Author(s):  
J. F. T. Spencer ◽  
P. A. J. Gorin ◽  
N. R. Gardner
Keyword(s):  
Candida Species ◽  
Treatment Process ◽  
Yeast Species ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Pulp Mill ◽  
Late Summer ◽  
Bacterial Counts ◽  
Rhodotorula Species

The numbers of yeasts in the effluent disposal system of the Prince Albert, Saskatchewan Pulp Mill occasionally reached 1 × 106 cells/liter, but were usually below 5 × 105 cells/liter. Rhodotorula species were commonly isolated and sometimes amounted to half of the population, though usually the relative numbers were considerably lower. The highest counts were found in late summer and early autumn. Bacterial counts varied from 20 × 106 to 74 × 106 cells/ml. Chemical O2 demand (COD) reached about 1200 mg/liter at times, and decreased as the effluent moved through the disposal system, about 40% of the total COD being removed during the treatment process. A considerably wider range of yeast species was found in the pulp mill disposal basins than in the Saskatoon sewage treatment plant. Most of the ascosporogenous yeasts found were Hansenula or Pichia species usually occurring in association with trees, as were many of the Candida species isolated.

MPC of Sewage Treatment Process

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Jiahui Meng ◽  
Qingyuan Zhao ◽  
Yu Zhang ◽  
Guanglei Wen ◽  
Huimin Ge ◽  
...  
Keyword(s):  
Neural Network ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Weather Conditions ◽  
Learning Ability ◽  
Control Effect ◽  
Control Goal ◽  
Control Layer

Sewage treatment is one of the main methods to promote the recycling of water resources. The control goal of sewage treatment process is to reduce energy consumption under the premise that the effluent quality reaches the standard. In recent years, model predictive control (MPC) has attracted some attention in sewage treatment. Neural network is widely used in control field because of its strong online learning ability. BP neural network is selected as the prediction layer and control layer of MPC and applied to sewage treatment plant to realize on-line control of dissolved oxygen and nitrate. The training index of traditional neural network usually only selects the error between measured value and set value as the variable, and now the change of control quantity is also taken as the training index variable of control layer to adjust the weight relation between them to get the best control effect. Considering that different weather conditions will have a greater impact on the water inflow, different coefficients of the two can be selected to achieve better results in different weather.

scholarly journals Occurrence and Sources of Synthetic Musk Fragrances in the Sewage Treatment Plants and the Han River in Seoul, Korea

2020 ◽  
Author(s):  
Ju-Hee Hong ◽  
Jun-Yeon Lee ◽  
Hyun-Ju Ha ◽  
Jin-Hyo Lee ◽  
Seok-Ryul Oh ◽  
...  
Keyword(s):  
Biological Treatment ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Synthetic Musk ◽  
Han River ◽  
Treatment Processes ◽  
Musk Fragrances ◽  
Receiving Water

Levels of synthetic musk fragrances (SMFs) and various personal care products (PCPs) were measured in the Han River and its tributaries in Seoul, Korea. The most abundant SMF in all river and PCP samples was 4,6,6,7,8,8-hexamethyl-1,3,4,7-tetrahydrocyclopenta[g]isochromene (HHCB), followed by 1-(3,5,5,6,8,8-hexamethyl-6,7-dihydronaphthalen-2-yl)ethanone (AHTN), musk ketone (MK), and 1,1,2,3,3-pentamethyl-2,5,6,7-tetrahydroinden-4-one (DPMI). There was a significant correlation between the SMF concentration in the PCPs and the Han River samples. Moving from upstream to downstream in the Han River, the median SMF concentration was 6.756, 2.945, 0.304, and 0.141 μg/L in the sewage treatment plant (STP) influent, effluent, tributaries, and mainstream, respectively, implying that effective SMF removal was achieved during the sewage treatment process, followed by dilution in the receiving water. Four STPs using advanced biological treatment processes had removal efficiencies of 55.8%, 50.6%, 43.3% for HHCB, AHTN, and MK, respectively. The highest SMF concentrations in the tributaries were observed at locations close to the STPs. Our study confirmed that the main source of SMFs in the receiving water were sewage effluent containing untreated SMFs, which are largely originated from household PCPs, especially hair care products (e.g., shampoo) and perfumes.

A Novel Predictive PID Control Method for SBR Wastewater Treatment Process DO Control

2014 ◽  
Vol 955-959 ◽  
pp. 1437-1442
Author(s):  
Hai Bo Yu ◽  
Yu Zhao Feng ◽  
Wei Peng ◽  
Li Wei Sheng ◽  
Hong Lu Li ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Pid Control ◽  
Treatment Process ◽  
Control Method ◽  
Batch Reactor ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Wastewater Treatment Process ◽  
Do Control

Sequencing Batch Reactor (SBR) wastewater treatment process has lots of characteristics, such as randomness, time-varying characteristics, complexity and so on. In order to solve the above problems, a predictive PID control method based on DMC and ordinary PID for SBR wastewater treatment process dissolved oxygen (DO) control was proposed. The simulation studies were conducted with the MATLAB in a sewage treatment plant. The results showed that the proposed predictive PID control method was robust and jamproof. Meanwhile, the wastewater treatment system also had a strong capacity of shock load.

scholarly journals Occurrence and Sources of Synthetic Musk Fragrances in the Sewage Treatment Plants and the Han River, Korea

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 392
Author(s):  
Ju-Hee Hong ◽  
Jun-Yeon Lee ◽  
Hyun-Ju Ha ◽  
Jin-Hyo Lee ◽  
Seok-Ryul Oh ◽  
...  
Keyword(s):  
Biological Treatment ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Synthetic Musk ◽  
Han River ◽  
Treatment Processes ◽  
Musk Fragrances ◽  
Receiving Water

Levels of synthetic musk fragrances (SMFs) and various personal care products (PCPs) were measured in the Han River and its tributaries in Seoul, Korea. The most abundant SMF in all river and PCP samples was 4,6,6,7,8,8-hexamethyl-1,3,4,7-tetrahydrocyclopenta(g)sochromene (HHCB), followed by 1-(3,5,5,6,8,8-hexamethyl-6,7-dihydronaphthalen-2-yl)ethanone (AHTN), musk ketone (MK), and 1,1,2,3,3-pentamethyl-2,5,6,7-tetrahydroinden-4-one (DPMI). The most abundant SMF in both PCPs and the Han River samples was HHCB, followed by AHTN. Moving from upstream to downstream in the Han River, the median SMF concentration was 6.756, 2.945, 0.304, and 0.141 μg/L in the sewage treatment plant (STP) influents, effluents, tributaries, and mainstream, respectively, implying that effective SMF removal was achieved during the sewage treatment process, followed by dilution in the receiving water. Four STPs using advanced biological treatment processes had removal efficiencies of 58.5%, 56.8%, and 38.1% for HHCB, AHTN, and MK, respectively. The highest SMF concentrations in the tributaries were observed at locations close to the STPs. Our study confirmed that the main source of SMFs in the receiving water were sewage effluents containing untreated SMFs, which largely originate from household PCPs, especially hair care products (e.g., shampoo) and perfumes.

scholarly journals Reduced Bacterial Counts from a Sewage Treatment Plant but Increased Counts and Antibiotic Resistance in the Recipient Stream in Accra, Ghana—A Cross-Sectional Study

2021 ◽  
Vol 6 (2) ◽  
pp. 79
Author(s):  
Lady A. B. Adomako ◽  
Dzidzo Yirenya-Tawiah ◽  
Daniel Nukpezah ◽  
Arpine Abrahamya ◽  
Appiah-Korang Labi ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Bacterial Counts ◽  
E Coli ◽  
Amoxicillin Clavulanate

Wastewater treatment plants receive sewage containing high concentrations of bacteria and antibiotics. We assessed bacterial counts and their antibiotic resistance patterns in water from (a) influents and effluents of the Legon sewage treatment plant (STP) in Accra, Ghana and (b) upstream, outfall, and downstream in the recipient Onyasia stream. We conducted a cross-sectional study of quality-controlled water testing (January–June 2018). In STP effluents, mean bacterial counts (colony-forming units/100 mL) had reduced E. coli (99.9% reduction; 102,266,667 to 710), A. hydrophila (98.8%; 376,333 to 9603), and P. aeruginosa (99.5%; 5,666,667 to 1550). Antibiotic resistance was significantly reduced for tetracycline, ciprofloxacin, cefuroxime, and ceftazidime and increased for gentamicin, amoxicillin/clavulanate, and imipenem. The highest levels were for amoxicillin/clavulanate (50–97%) and aztreonam (33%). Bacterial counts increased by 98.8% downstream compared to the sewage outfall and were predominated by E. coli, implying intense fecal contamination from other sources. There was a progressive increase in antibiotic resistance from upstream, to outfall, to downstream. The highest resistance was for amoxicillin/clavulanate (80–83%), cefuroxime (47–73%), aztreonam (53%), and ciprofloxacin (40%). The STP is efficient in reducing bacterial counts and thus reducing environmental contamination. The recipient stream is contaminated with antibiotic-resistant bacteria listed as critically important for human use, which needs addressing.

scholarly journals DISPOSAL ALCOHOL WASTEWATER WITHOUT USING THERMAL TREATMENT

2020 ◽  
Vol 2020 (1) ◽  
pp. 43-44
Author(s):  
Vadim Krutikov ◽  
Ivan Semenov ◽  
Dmitriy Dubrovskiy
Keyword(s):  
Wastewater Treatment ◽  
Thermal Treatment ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Biological Wastewater Treatment ◽  
Wastewater Treatment Process ◽  
The Cost

The possibility of withdrawing alcohol wastewater from thermal treatment to a sewage treatment plant was considered in this article. These measures can reduce the cost of fuel used for thermal treatment, as well as improve the quality of the biological wastewater treatment process

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