scholarly journals Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3507
Author(s):  
Animesh Bhattacharya ◽  
Saswata Sahu ◽  
Venkatesh Telu ◽  
Srimanti Duttagupta ◽  
Soumyajit Sarkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Removal Efficiency ◽  
Arsenic Removal ◽  
Cost Model ◽  
Critical Evaluation ◽  
Treatment Plant ◽  
Operational Cost ◽  
Activated Alumina ◽  
The Impact

A plethora of technologies has been developed over decades of extensive research on arsenic remediation, although the technical and financial perspective of arsenic removal plants in the field requires critical evaluation. In the present study, focusing on some of the pronounced arsenic-affected areas in West Bengal, India, we assessed the implementation and operation of different arsenic removal technologies using a dataset of 4000 spatio-temporal data collected from an in-depth field survey of 136 arsenic removal plants engaged in the public water supply. Our statistical analysis of this dataset indicates a 120% rise in the average cumulative capacity of the plants during 2014–2021. The majorities of the plants are based on the activated alumina with FeCl3 technology and serve about 49% of the population in the study area. The average cost of water production for the activated alumina with FeCl3 technology was found to be ₹7.56/m3 (USD $1 ≈ INR ₹70), while the lowest was ₹0.39/m3 for granular ferric hydroxide technology. A machine learning-based framework was employed to analyze the impact of water quality and treatment plant parameters on the removal efficiency, capital, and operational cost of the plants. The artificial neural network model exhibited adequate statistical significance, with a high F-value and R2 of 5830.94 and 0.72 for the capital cost model, 136,954, and 0.98 for the operational cost model, respectively. The relative importance of the process variables was identified through random forest models. The models indicated that flow rate, media, and chemicals are the predominant costs, while contaminant loading in influent water and a coagulating agent was important for removal efficiency. The established framework may be instrumental as a decision-making tool for water providers to assess the expected performance and financial involvement for proposed or ongoing arsenic removal plants concerning various design and quality parameters.

scholarly journals Photo-Transformation of Effluent Organic Matter by ZnO-Based Sunlight Irradiation

2020 ◽  
Vol 10 (24) ◽  
pp. 9002
Author(s):  
Thao Thi Nguyen ◽  
Seong Nam Nam ◽  
Jeill Oh
Keyword(s):  
Organic Matter ◽  
Photocatalytic Degradation ◽  
Removal Efficiency ◽  
Treatment Plant ◽  
Wastewater Effluent ◽  
Solar Irradiation ◽  
Effluent Organic Matter ◽  
Sunlight Irradiation ◽  
Vis Spectroscopy ◽  
The Impact

This study investigated the impact of effluent organic matter (EfOM) from wastewater effluent on the properties of organic matter in receiving water and the efficiency of its removal using photocatalysis. The organic matter is characterized using fluorescence excitation-emission matrices coupled with parallel factor analysis (EEM-PARAFAC), UV-Vis spectroscopy, and dissolved organic carbon (DOC) measurements. The experiments are conducted with water samples that were collected from upstream waters (used as a source of dissolved organic matter (DOM)), wastewater effluent (a source of EfOM), and waters downstream of a wastewater treatment plant, and with upstream water and wastewater effluent being mixed at different ratios in the lab (DOM/EfOM). EEM-PARAFAC analysis identifies three components: a humic-like component (C1), a tyrosine-like component (C2), and a terrestrial-like humic component (C3). When compared to DOM, EfOM has a higher specific ultraviolet absorbance at 254 nm (SUVA254), a higher fluorescence index (FI), and more abundant humic-like components. As the EfOM contribution increased, an increase in both humic-like components and a simultaneous decrease in the protein-like components are observed. The photocatalytic degradation of the organic matter using simulated solar irradiation with ZnO as a catalyst is examined. The removal efficiency of photocatalysis is calculated using the DOC, UV absorbance at 254 nm (UV254), and the maximum fluorescence intensity (Fmax) of the PARAFAC components. After 120 min of irradiation, the removal efficiency of photocatalysis differs between the DOM, EfOM, and EfOM-impacted samples due to the change in the properties of the organic matter in the source water. The photocatalytic degradation of organic matter follows pseudo-first-order kinetics, with the DOC and UV254 exhibiting a lower removal efficiency with the increasing contribution of EfOM, which indicated that EfOM has a potentially negative impact on the performance of drinking water treatment. The removal of PARAFAC components follows the order C3 > C1 > C2, indicating that humic-like components are preferentially removed when compared to protein-like components under sunlight irradiation.

scholarly journals Efficiency of Sequential Batch Reactor (SBR) based sewage treatment plant and its discharge impact on Dal Lake, Jammu & Kashmir, India

2020 ◽  
Vol 5 (4) ◽  
pp. 517-524
Author(s):  
Mukesh Ruhela ◽  
Adil Ahmad Wani ◽  
Faheem Ahamad
Keyword(s):  
Removal Efficiency ◽  
Batch Reactor ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Continuous Increase ◽  
Sequential Batch Reactor ◽  
Dal Lake ◽  
Confluence Zone ◽  
The Impact

Dal Lake is the second largest and most beautiful Lake in the state of Jammu and Kashmir and is the major centre of tourist activities. Due to the continuous increase in the population, the generation of domestic wastewater also increased. The present study was carried out to assess the efficiency of Sequential Batch Reactor (SBR) based Sewage Treatment Plant (STP) located at Brari Numbal and its discharge impact on the physicochemical properties of Dal Lake. The sample was collected from the selected sampling sites (inlet and outlet of SBR based STP, upstream, confluence zone, and downstream of Dal Lake) for five months (November 2019 to March 2020) and analysed using the standard methodologies. The plant shows maximum removal efficiency for BOD (79.85%) although the effluent BOD was found above the standard limit. The minimum removal efficiency of the plant was observed in the case of pH (3.46%). The gain in the case of DO was observed +851.55%. All the sites of Dal Lake were found polluted but the confluence zone and downstream were more polluted in comparison to the upstream due to the discharge of STP outlet into Dal Lake with higher BOD and COD (21.39% increase in BOD, 43.29% increase in COD; 80.10% increase in iron, 65.61% increase in ammonical nitrogen, and 101% increase in phosphate concentration). Besides this, discharge of the huge quantity of untreated wastewater from the city into the lake is also responsible for the degraded water quality of Dal Lake. It can be concluded that efficiency of the plant was in moderate condition and it needs further modifications. This is the first study showing the impact of SBR-STP effluent on Dal Lake.

scholarly journals Arsenic removal by the micellar-enhanced ultrafiltration using response surface methodology

2019 ◽  
Vol 20 (2) ◽  
pp. 574-585 ◽  
Author(s):  
Oznur Begum Gokcek ◽  
Nigmet Uzal
Keyword(s):  
Response Surface Methodology ◽  
Aqueous Solutions ◽  
Response Surface ◽  
Removal Efficiency ◽  
Surfactant Concentration ◽  
Arsenic Removal ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Micellar Enhanced Ultrafiltration ◽  
The Impact

Abstract The present research investigates the removal of arsenic (As) from aqueous solutions using micellar-enhanced ultrafiltration (MEUF) by utilizing two different surfactants: benzethonium chloride and dodecyl pyridinium chloride (BCl and DPCl). The impact of the operating variables and maximum removal efficiency were found under different conditions for BCl and DPCl surfactants. The maximum As rejection efficiency for MEUF with BCl and DPCl surfactants is 92.8% and 84.1%, respectively. In addition to this, a statistics-based experimental design with response surface methodology was used for the purpose of examining the impact of operating conditions, including initial pH, initial As concentration (ppb), and surfactant concentration (BCl, mM) in As-removal from aqueous solutions. In the analysis of the experimental data, a second-order polynomial model that was validated by statistical analysis for the BCl surfactant was used. On the basis of the response model created, the removal of As ions was acquired at optimum operating parameters, including the initial As concentration of 150 ppb, surfactant concentration of 5 mM and pH 10 for the BCl surfactant with 92.8% As-removal efficiency.

scholarly journals Development of Indigenous Iron-Coated Pottery Granules for Arsenic (V) Removal from Groundwater

2020 ◽  
Vol 18 (02) ◽  
pp. 127-132
Author(s):  
Khadija Qureshi ◽  
Kashif Hussain Mangi ◽  
Zulfiqar Ali Solangi ◽  
Zulfiqar Ali Bhatii ◽  
Mukhtiar Ali ◽  
...  
Keyword(s):  
Removal Efficiency ◽  
Contact Time ◽  
Arsenic Removal ◽  
Mixing Time ◽  
Underground Water ◽  
Batch Adsorption ◽  
Physical Parameters ◽  
Adsorbate Concentration ◽  
The Impact ◽  
Maximum Removal

Arsenic is a carcinogenic element capable to get into water bodies and drinking water supplies from natural deposits and industrial practices. Its presence in drinking underground water is highly toxic to human health. The study is focused on the development of indigenous Iron-Coated Pottery Granules (ICPG) to remove As from groundwater of Hala City. The developed ICPG was agitated with local clay white flour and water. A low-cost adsorbent namely ICPG was synthesized for the expulsion of As from underground water. The ICGP was characterized with SEM and FTIR techniques. Furthermore, the impact of physical parameters including adsorbate concentration, dosage, mixing time, pH, and contact time on As removal efficiency was investigated in batch experiments. The maximum removal efficiency was achieved with an adsorbent dosage of 0.5 grams at pH =7 for a contact time of 90 minutes when agitated at a speed of 150 r/min. The arsenic removal efficiency was found highly dependent on contact time increase and optimum pH (maximum removal achieved at strong adsorption of As at pH 4–7), however, the rise of adsorbate concentration resulted in the decrement in the efficiency after certain range. Batch adsorption study of underground water sample collected from Hala, Sindh, Pakistan was performed with satisfactory results, i.e. 94 arsenic removal from water. All the water samples were analyzed through atomic absorption Spectrophotometer. The investigation has indicated that ICPG is an exceptionally favourable material for As removal from drinking underground water and can be applied to handle the arsenic issue in most of the regions of Sindh province.

scholarly journals Assessing The Impact of Land Cover On Groundwater Quality In a Smart City Using GIS And Machine Learning Algorithms

2021 ◽  
Author(s):  
Rukhsar Anjum ◽  
Sk Ajim Ali ◽  
Mansoor Alam Siddiqui
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Land Cover ◽  
Groundwater Quality ◽  
Monsoon Season ◽  
Post Monsoon ◽  
The Impact

Abstract The present study aimed to assess the impacts of land cover on groundwater quality by integrating physico-chemical data and satellite imageries. Initially, fourteen groundwater parameters of both pre-monsoon and post-monsoon were collected from nineteen sampled stations and water quality index (WQI) was calculated. Consequently, Google earth, Landsat-8, and Sentinel-2A imageries were considered for land cover mapping including the concentration of settlement and urban built-up, greenery coverage, and micro-level land use. Two machine learning models such as artificial neural network (ANN) and random forest (RF) were used for pixel-based classification and establish spatial relation between water quality and land cover. This study trained and tested the models for the whole study area as well as 500m buffers from each sampled station. The result of model’s validation including mean absolute error (MAE), root mean squared error (RMSE), Kappa statistic (K), Overall accuracy of model (OAC), and receiver operating characteristic (ROC), indicated that random forest classifier has better performance than the artificial neural network. The results show that the testing dataset of pre-monsoon season has higher accuracy with MAE 0.343, RMSE 0.397, K-value 0.55, and ROC value 0.838 to envisage the impact of land cover on groundwater quality in comparison to post-monsoon season. The results also reveal that the classification accuracy is greater within 500m buffer areas in comparison to the whole study area with a close to 0 value of MAE and RMSE, and absolutely 1 value of K and ROC. Based on the above findings, the present study suggested to consider a large scale for determining the controlling factors of groundwater degradation.

scholarly journals Application of Machine Learning for Fenceline Monitoring of Odor Classes and Concentrations at a Wastewater Treatment Plant

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4716
Author(s):  
Federico Cangialosi ◽  
Edoardo Bruno ◽  
Gabriella De Santis
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Wastewater Treatment ◽  
Random Forest ◽  
Wastewater Treatment Plant ◽  
Learning Algorithms ◽  
Treatment Plant ◽  
Machine Learning Algorithms ◽  
Industrial Plants ◽  
Cohen's Kappa

The development of low-cost sensors, the introduction of technical performance specifications, and increasingly effective machine learning algorithms for managing big data have led to a growing interest in the use of instrumental odor monitoring systems (IOMS) for odor measurements from industrial plants. The classification and quantification of odor concentration are the main goals of IOMS installed inside industrial plants in order to identify the most important odor sources and to assess whether the regulatory thresholds have been exceeded. This paper illustrates the use of two machine learning algorithms applied to the concurrent classification and quantification of odors. Random Forest was employed, which is a machine learning algorithm that thus far has not been used in the field of odor quantification and classification for complex industrial situations. Furthermore, the results were compared with commonly used algorithms in this field, such as artificial neural network (ANN), which was here employed in the form of a deep neural network. Both techniques were applied to the data collected from an IOMS installed for fenceline monitoring at a wastewater treatment plant. Cohen’s kappa and Normalized RMSE are used as specifical performance indicators for classification and regression: the indicators were calculated for the test dataset, and the results were compared with data in the literature obtained in contexts of similar complexity. A Cohen’s kappa of 97% was reached for the classification task, while the best Normalized RMSE, namely 4%, for the interval 20–2435 ouE/m3 was obtained with Random Forest.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

European Economies Stability Faced With Potential Outburst of Sovereign Debt Crisis. An Empirical Study Using Neural Network

2013 ◽  
Vol 12 (2) ◽  
pp. 3255-3260
Author(s):  
Stelian Stancu ◽  
Alexandra Maria Constantin
Keyword(s):  
Neural Network ◽  
Sovereign Debt ◽  
Debt Crisis ◽  
Banking System ◽  
Sovereign Debt Crisis ◽  
European Level ◽  
Continuous Growth ◽  
Economic Context ◽  
European Economies ◽  
The Impact

Instilment, on a European level, of a state incompatible with the state of stability on a macroeconomic level and in the financial-banking system lead to continuous growth of vulnerability of European economies, situated at the verge of an outburst of sovereign debt crises. In this context, the current papers main objective is to produce a study regarding the vulnerability of European economies faced with potential outburst of sovereign debt crisis, which implies quantitative analysis of the impact of sovereign debt on the sensitivity of the European Unions economies. The paper also entails the following specific objectives: completing an introduction in the current European economic context, conceptualization of the notion of “sovereign debt crisis, presenting the methodology and obtained empirical results, as well as exposition of the conclusions.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

The Random Forest Method in Research of Impact of Macroeconomic Indicators of Regional Development on Informal Employment Rate

2020 ◽  
Vol 27 (6) ◽  
pp. 37-55
Author(s):  
E. V. Zarova ◽  
E. I. Dubravskaya
Keyword(s):  
Random Forest ◽  
Regional Development ◽  
Russian Federation ◽  
Regional Level ◽  
Informal Employment ◽  
Macroeconomic Factors ◽  
Random Forest Method ◽  
Macroeconomic Indicators ◽  
The Russian Federation ◽  
The Impact

The topic of quantitative research on informal employment has a consistently high relevance both in the Russian Federation and in other countries due to its high dependence on cyclicality and crisis stages in economic dynamics of countries with any level of economic development. Developing effective government policy measures to overcome the negative impact of informal employment requires special attention in theoretical and applied research to assessing the factors and conditions of informal employment in the Russian Federation including at the regional level. Such effects of informal employment as a shortfall in taxes, potential losses in production efficiency, and negative social consequences are a concern for the authorities of the federal and regional levels. Development of quantitative indicators to determine the level of informal employment in the regions, taking into account their specifics in the general spatial and economic system of Russia are necessary to overcome these negative effects. The article proposes and tests methods for solving the problem of assessing the impact of hierarchical relationships on macroeconomic factors at the regional level of informal employment in constituent entities of the Russian Federation. Majority of the works on the study of informal employment are based on basic statistical methods of spatial-dynamic analysis, as well as on the now «traditional» methods of cluster and correlation-regression analysis. Without diminishing the merits of these methods, it should be noted that they are somewhat limited in identifying hidden structural connections and interdependencies in such a complex multidimensional phenomenon as informal employment. In order to substantiate the possibility of overcoming these limitations, the article proposes indicators of regional statistics that directly and indirectly characterize informal employment and also presents the possibilities of using the «random forest» method to identify groups of constituent entities of the Russian Federation that have similar macroeconomic factors of informal employment. The novelty of this method in terms of research objectives is that it allows one to assess the impact of macroeconomic indicators of regional development on the level of informal employment, taking into account the implicit, not predetermined by the initial hypotheses, hierarchical relationships of factor indicators. Based on the generalization of the studies presented in the literature, as well as the authors’ statistical calculations using Rosstat data, the authors came to the conclusion about the high importance of macroeconomic parameters of regional development and systemic relationships of macroeconomic indicators in substantiating the differentiation of the informal level across the constituent entities of the Russian Federation.

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