scholarly journals A Random Forest Model for the Prediction of FOG Content in Inlet Wastewater from Urban WWTPs

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1237
Author(s):  
Vanesa Mateo Pérez ◽  
José Manuel Mesa Fernández ◽  
Joaquín Villanueva Balsera ◽  
Cristina Alonso Álvarez
Keyword(s):  
Random Forest ◽  
Wastewater Treatment Plants ◽  
Learning Algorithm ◽  
Global Improvement ◽  
Treatment Processes ◽  
Early Adoption ◽  
Novel Approach ◽  
Number Of Factors ◽  
Initial Stage ◽  
Correct Design

The content of fats, oils, and greases (FOG) in wastewater, as a result of food preparation, both in homes and in different commercial and industrial activities, is a growing problem. In addition to the blockages generated in the sanitary networks, it also represents a difficulty for the performance of wastewater treatment plants (WWTP), increasing energy and maintenance costs and worsening the performance of downstream treatment processes. The pretreatment stage of these facilities is responsible for removing most of the FOG to avoid these problems. However, so far, optimization has been limited to the correct design and initial installation dimensioning. Proper management of this initial stage is left to the experience of the operators to adjust the process when changes occur in the characteristics of the wastewater inlet. The main difficulty is the large number of factors influencing these changes. In this work, a prediction model of the FOG content in the inlet water is presented. The model is capable of correctly predicting 98.45% of the cases in training and 72.73% in testing, with a relative error of 10%. It was developed using random forest (RF) and the good results obtained (R2 = 0.9348 and RMSE = 0.089 in test) will make it possible to improve operations in this initial stage. The good features of this machine learning algorithm had not been used, so far, in the modeling of pretreatment parameters. This novel approach will result in a global improvement in the performance of this type of facility allowing early adoption of adjustments to the pretreatment process to remove the maximum amount of FOG.

An Improved Deep Learning Algorithm for Diabetes Prediction

2022 ◽  
pp. 103-119
Author(s):  
Basetty Mallikarjuna ◽  
Supriya Addanke ◽  
Anusha D. J.
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Early Detection ◽  
Learning Algorithm ◽  
The Novel ◽  
Random Forest Algorithm ◽  
Deep Learning Algorithm ◽  
Novel Approach ◽  
Diabetes Prediction ◽  
Two Parameters

This chapter introduces the novel approach in deep learning for diabetes prediction. The related work described the various ML algorithms in the field of diabetic prediction that has been used for early detection and post examination of the diabetic prediction. It proposed the Jaya-Tree algorithm, which is updated as per the existing random forest algorithm, and it is used to classify the two parameters named as the ‘Jaya' and ‘Apajaya'. The results described that Pima Indian diabetes dataset 2020 (PIS) predicts diabetes and obtained 97% accuracy.

scholarly journals Development of an Efficient Cement Production Monitoring System Based on the Improved Random Forest Algorithm

2021 ◽  
Author(s):  
Hanane Zermane ◽  
Abbes Drardja
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Manufacturing Systems ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Production Performance ◽  
Time Data ◽  
Tree Model ◽  
Global Improvement ◽  
Line Production

Abstract Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies and there is increasing competitiveness among them and increase companies’ value. Machine Learning (ML) techniques have become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0, and the extensive integration of paradigms such as big data, cloud computing, high computational power, and enormous storage capacity. Implementing a system that can identify faults early to avoid critical situations in the line production and environment is crucial. Therefore, one of the powerful machine learning algorithms is Random Forest (RF). The ensemble learning algorithm is performed to fault diagnosis and SCADA real-time data classification and predicting the state of the line production. Random Forests proved to be a better classifier with a 95% accuracy. Comparing to the SVM model, the accuracy is 94.18%, however, the K-NN model accuracy is about 93.83%, an accuracy of 80.25% is achieved using the logistic regression model, finally, about 83.73% is obtained by the decision tree model. The excellent experimental results achieved on the Random Forest model showed the merits of this implementation in the production performance, ensuring predictive maintenance, and avoid wasting energy.

The Characteristics of Community Wastewater Biological Treatment Processes in Taiwan

1986 ◽  
Vol 18 (7-8) ◽  
pp. 289-296
Author(s):  
C. F. Ouyang ◽  
T. J. Wan
Keyword(s):  
Biological Treatment ◽  
Wastewater Treatment Plants ◽  
Oxygen Demand ◽  
Field Investigation ◽  
Oxidation Ditch ◽  
Trickling Filter ◽  
Rotating Biological Contactor ◽  
Treatment Processes ◽  
Treatment Characteristics ◽  
Sludge Thickening

This study investigated and compared the treatment characteristics of three different kinds of biological wastewater treatment plants (including rotating biological contactor, trickling filter and oxidation ditch) which are currently operated in Taiwan. The field investigation of this study concentrated on the following items: the performance of biological oxygen demand (BOD) and suspended solids (SS) removal; the sludge yield rate of BOD removal; the settleability of sludge solids; the properties of sludge thickening; the power consumption and land area requirement per unit volume of wastewater. Finally, based on the results of the field investigation, a comparison of the treatment characteristics of the three different biological treatment processes was evaluated.

scholarly journals Occurrence of SARS-CoV-2 RNA in Six Municipal Wastewater Treatment Plants at the Early Stage of COVID-19 Pandemic in The United States

Pathogens ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 798
Author(s):  
Samendra P. Sherchan ◽  
Shalina Shahin ◽  
Jeenal Patel ◽  
Lauren M. Ward ◽  
Sarmila Tandukar ◽  
...  
Keyword(s):  
United States ◽  
Wastewater Treatment ◽  
Municipal Wastewater ◽  
Wastewater Treatment Plants ◽  
Quantitative Polymerase Chain Reaction ◽  
The United States ◽  
Secondary Effluent ◽  
Untreated Wastewater ◽  
Treatment Processes ◽  
Wastewater Samples

In this study, we investigated the occurrence of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) RNA in primary influent (n = 42), secondary effluent (n = 24) and tertiary treated effluent (n = 34) collected from six wastewater treatment plants (WWTPs A–F) in Virginia (WWTP A), Florida (WWTPs B, C, and D), and Georgia (WWTPs E and F) in the United States during April–July 2020. Of the 100 wastewater samples analyzed, eight (19%) untreated wastewater samples collected from the primary influents contained SARS-CoV-2 RNA as measured by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assays. SARS-CoV-2 RNA were detected in influent wastewater samples collected from WWTP A (Virginia), WWTPs E and F (Georgia) and WWTP D (Florida). Secondary and tertiary effluent samples were not positive for SARS-CoV-2 RNA indicating the treatment processes in these WWTPs potentially removed SARS-CoV-2 RNA during the secondary and tertiary treatment processes. However, further studies are needed to understand the log removal values (LRVs) and transmission risks of SARS-CoV-2 RNA through analyzing wastewater samples from a wider range of WWTPs.

scholarly journals Detecting suicidal risk using MMPI-2 based on machine learning algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sunhae Kim ◽  
Hye-Kyung Lee ◽  
Kounseok Lee
Keyword(s):  
Machine Learning ◽  
Suicidal Ideation ◽  
Random Forest ◽  
Minnesota Multiphasic Personality Inventory ◽  
Learning Algorithm ◽  
Suicidal Risk ◽  
K Nearest Neighbors ◽  
Large Group ◽  
Suicidal Attempts ◽  
Scale Scores

AbstractMinnesota Multiphasic Personality Inventory-2 (MMPI-2) is a widely used tool for early detection of psychological maladjustment and assessing the level of adaptation for a large group in clinical settings, schools, and corporations. This study aims to evaluate the utility of MMPI-2 in assessing suicidal risk using the results of MMPI-2 and suicidal risk evaluation. A total of 7,824 datasets collected from college students were analyzed. The MMPI-2-Resturcutred Clinical Scales (MMPI-2-RF) and the response results for each question of the Mini International Neuropsychiatric Interview (MINI) suicidality module were used. For statistical analysis, random forest and K-Nearest Neighbors (KNN) techniques were used with suicidal ideation and suicide attempt as dependent variables and 50 MMPI-2 scale scores as predictors. On applying the random forest method to suicidal ideation and suicidal attempts, the accuracy was 92.9% and 95%, respectively, and the Area Under the Curves (AUCs) were 0.844 and 0.851, respectively. When the KNN method was applied, the accuracy was 91.6% and 94.7%, respectively, and the AUCs were 0.722 and 0.639, respectively. The study confirmed that machine learning using MMPI-2 for a large group provides reliable accuracy in classifying and predicting the subject's suicidal ideation and past suicidal attempts.

scholarly journals mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Prabina Kumar Meher ◽  
Anil Rai ◽  
Atmakuri Ramakrishna Rao
Keyword(s):  
Random Forest ◽  
Cellular Localization ◽  
Learning Algorithm ◽  
Elastic Net ◽  
Messenger Rnas ◽  
Computational Tool ◽  
Temporal Gene Expression ◽  
Single Location ◽  
Spatio Temporal ◽  
Localization Prediction

Abstract Background Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs. Results The mRNA sequences from 9 different localizations were considered. Each sequence was first transformed to a numeric feature vector of size 5460, based on the k-mer features of sizes 1–6. Out of 5460 k-mer features, 1812 important features were selected by the Elastic Net statistical model. The Random Forest supervised learning algorithm was then employed for predicting the localizations with the selected features. Five-fold cross-validation accuracies of 70.87, 68.32, 68.36, 68.79, 96.46, 73.44, 70.94, 97.42 and 71.77% were obtained for the cytoplasm, cytosol, endoplasmic reticulum, exosome, mitochondrion, nucleus, pseudopodium, posterior and ribosome respectively. With an independent test set, accuracies of 65.33, 73.37, 75.86, 72.99, 94.26, 70.91, 65.53, 93.60 and 73.45% were obtained for the respective localizations. The developed approach also achieved higher accuracies than the existing localization prediction tools. Conclusions This study presents a novel computational tool for predicting the multiple localization of mRNAs. Based on the proposed approach, an online prediction server “mLoc-mRNA” is accessible at http://cabgrid.res.in:8080/mlocmrna/. The developed approach is believed to supplement the existing tools and techniques for the localization prediction of mRNAs.

scholarly journals A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Shuo Xu ◽  
Jie Cheng ◽  
Quan Zhang
Keyword(s):  
Random Forest ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Mainland China ◽  
Fusion Method ◽  
Moisture Condition

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

Performance evaluation of different wastewater treatment technologies operating in a developing country

2011 ◽  
Vol 1 (1) ◽  
pp. 37-56 ◽  
Author(s):  
Sílvia C. Oliveira ◽  
Marcos von Sperling
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Septic Tank ◽  
Operational Parameters ◽  
Technical Literature ◽  
Effluent Quality ◽  
Loading Rates ◽  
Treatment Processes ◽  
Treatment Technologies ◽  
Uasb Reactors

This article analyses the performance of 166 wastewater treatment plants operating in Brazil, comprising six different treatment processes: septic tank + anaerobic filter, facultative pond, anaerobic pond + facultative pond, activated sludge, UASB reactors alone, UASB reactors followed by post-treatment. The study evaluates and compares the observed effluent quality and the removal efficiencies in terms of BOD, COD, TSS, TN, TP and FC with typical values reported in the technical literature. In view of the large performance variability observed, the existence of a relationship between design/operational parameters and treatment performance was investigated. From the results obtained, no consistent relationship between loading rates and effluent quality was found. The influence of loading rates differed from plant to plant, and the effluent quality was dictated by several combined factors related to design and operation.

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