Comparison Analysis of Treatment Methods and ARIMA Time-Series Forecasting of Basic Water Components in Effluent from Small-scale Public Sewage Treatment Facilities

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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Evolving Cascade-Correlation Networks for Time-Series Forecasting.

10.21236/ada289197 ◽

1994 ◽

Author(s):

J. R. McDonnell ◽

D. E. Waagen

Keyword(s):

Time Series ◽

Time Series Forecasting ◽

Correlation Networks ◽

Cascade Correlation

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Pyridine Degradation by Suspensions and Biofilms of Achromobacter pulmonis PNOS and Burkholderia dolosa BOS Strains Isolated from Activated Sludge of Sewage Treatment Plants

Biotekhnologiya ◽

10.21519/0234-2758-2020-36-2-86-98 ◽

2020 ◽

Vol 36 (2) ◽

pp. 86-98

Author(s):

A.A. Sergeeva ◽

G.V. Ovechkina ◽

A.Yu. Maksimov

Keyword(s):

Activated Sludge ◽

Sewage Treatment ◽

Local Treatment ◽

Carbon Sources ◽

Mining Institute ◽

Bacterial Strains ◽

Federal Research ◽

Registration Number ◽

Treatment Facilities ◽

Pyridine Degradation

Bacterial strains capable of degradation of 0.8-15.8 g/1 pyridine hydrochloride have been isolated from activated sludge of municipal biological treatment plants in Perm (BOS) and local treatment facilities of the LUKOIL-Permnefteorgsintez enterprise (PNOS). The strains were identified as Achromobacter pulmonis and Burkholderia dolosa. The optimal pyridine concentration for the growth of the isolated strains was 4.0 g/1. The pyridine degradation during the A. pulmonis PNOS and B. dolosa BOS cultivation on a medium with ammonium chloride and glucose and without additional nitrogen or carbon sources was studied. It was shown that the strains are able to accumulate biomass in a medium with pyridine as the sole carbon and nitrogen source; the addition of glucose to the medium (1 g/L) accelerated the pyridine degradation by A. pulmonis PNOS, but inhibited the process carried out by B. dolosa BOS. B. dolosa BOS and A. pulmonis PNOS biofilms efficiently utilized pyridine during growth on basalt and carbon fibers; the highest rate of pyridine utilization (1.8 g /(L day)) was observed in A. pulmonis PNOS biofilms on basalt fibers. pyridine, biodegradation, activated sludge, biofilms, Achromobacter pulmonis, Burkholderia dolosa The authors grateful to Dr. I.I. Tchaikovsky, Head of the Laboratory of Geology of Mineral Deposits of the Mining Institute, a branch of the Perm Federal Research Center, for help with electron microscopy of the samples. This work was carried out as part of a state assignment on the topic « Study of the Functional and Species Diversity of Microorganisms Useful for Ecocenoses and Human Practical Activity», registration number R&D AAAA-A19-119112290008-4.

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A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽

10.3390/e21050455 ◽

2019 ◽

Vol 21 (5) ◽

pp. 455 ◽

Cited By ~ 5

Author(s):

Hongjun Guan ◽

Zongli Dai ◽

Shuang Guan ◽

Aiwu Zhao

Keyword(s):

Time Series ◽

Information Entropy ◽

Stock Exchange ◽

Time Series Forecasting ◽

High Order ◽

Stock Index ◽

Neutrosophic Set ◽

Forecasting Model ◽

Forecasting Models ◽

Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

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Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

Advances in Intelligent Data Analysis VIII - Lecture Notes in Computer Science ◽

10.1007/978-3-642-03915-7_31 ◽

2009 ◽

pp. 357-368 ◽

Cited By ~ 3

Author(s):

Francisco Martínez-Álvarez ◽

Alicia Troncoso ◽

José C. Riquelme

Keyword(s):

Time Series ◽

Time Series Forecasting ◽

Frequent Episodes

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Treatment of Rural Domestic Sewage by Solar Ozonic Ecological Sewage Treatment Plant

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.955-959.3393 ◽

2014 ◽

Vol 955-959 ◽

pp. 3393-3399 ◽

Cited By ~ 1

Author(s):

Wei Zheng ◽

Yan Ming Yang ◽

Yun Long Li ◽

Jian Qiu Zheng

Keyword(s):

Municipal Wastewater ◽

Operating Cost ◽

Sewage Treatment ◽

Sewage Treatment Plant ◽

Treatment Plant ◽

Domestic Sewage ◽

Design Parameters ◽

Small Scale ◽

Municipal Wastewater Treatment ◽

Average Percentage

The process technique and design parameters of project of Solar Ozonic Ecological Sewage Treatment Plant (short for SOESTP) which consists of anaerobic reactor, horizontal subsurface flow (HSSF) constructed wetlands(CWs) and the combination of solar power and ozone disinfection are described, the paper further examines the removal efficiency for treating rural domestic sewage, running expense and recycling ability of product water. The results show that the average percentage removal values of CODcr，BOD5，SS，TN，NH3-N，TP range from 95.6% to 98.0%, 96.0% to 98.7%, 93.1% to 96.1%, 97.0% to 98.9%, 96.9% to 99.5%, 98.2% to 99.6%, respectively, the reduction of fecal coliform (FC) reaches 99.9%, the effluent quality meets the first level A criteria specified in Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant（GB18918-2002）. The running cost of SOESTP is 0.063yuan/ m3, saves much more than traditional sewage treatment, and the ozone water obtained from the reservoir will be an ideal choice for disinfection .The system has characteristics of easy manipulation, low operating cost, achieving advanced water, energy conservation and environment protection, is thought to be very suitable for use as the promotion of rural small - scale sewage treatment.

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