Big Data Analytics from a Wastewater Treatment Plant

Wongburi, Praewa; Park, Jae K.

doi:10.3390/su132212383

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…Using big data, companies provide opportunities for employees to grow and develop through learning, improving performance, and predicting company growth (Hotaling & Spinrad, 2021). For example, Google uses people analytics to invest in their infrastructure better, answer critical questions that matter to the business, and as an essential component in building business partnerships (Wongburi & Park, 2021). The analytical capabilities possessed by Big Data can help identify human resources who have the potential to grow and develop rapidly so that they are entitled to further training so they can get a high career path (Jain, 2018).…”

Section: Employee Developmentmentioning

confidence: 99%

Big Data Analysis in Human Resources Decision Making: Optimizing Workforce Management

Heliana,

Wahyuni

2024

jrmsi

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This research aims to conduct an in-depth analysis of the application of Big Data in human resource decision-making, focusing on optimizing workforce management. By measuring four main variables, namely recruitment efficiency, accuracy of employee selection, employee development, and organizational performance based on HR decision-making—Big Data analysis is expected to provide a deeper understanding of workforce dynamics. The research results show that applying Big Data can build recruitment efficiency, increase the accuracy of employee selection, support more appropriate employee development, and ultimately improve overall organizational performance. Big Data makes organizational performance based on HR decision-making by providing a solid foundation for more effective human resource management strategies. This research uses qualitative methods with the help of the NVivo 12 Plus tool to identify research results. Implementing Big Data in various aspects of human resource management is recognized as a catalyst for positive change in workforce management.

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Section: Employee Developmentmentioning

confidence: 99%

Big Data Analysis in Human Resources Decision Making: Optimizing Workforce Management

Heliana,

Wahyuni

2024

jrmsi

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“…Additionally, combining multiple models generally reduces errors compared to a single model, and the process of averaging or following the majority opinion mitigates extreme predictions, providing more stable results [16]. The governing equation for the weighted ensemble models is represented by Equation (1), where n represents the number of regressors in the ensemble, wi is the weight assigned to the i-th regressor, and yi(x) is the prediction made by the i-th regressor for the input x.…”

Section: Ensemble Modelmentioning

confidence: 99%

“…In recent years, there has been a remarkable surge in the volume of data generated and stored within wastewater treatment plants (WWTPs) since 2010, leading to an exponential increase. Notably, it has been observed that 90% of global data have been generated within the past two years alone, signifying the onset of the big data era [1].…”

Section: Introductionmentioning

confidence: 99%

Novel Ensemble Learning Approach for Predicting COD and TN: Model Development and Implementation

Cheng,

Kim,

Wang

et al. 2024

Water

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Wastewater treatment plants (WWTPs) generate useful data, but effectively utilizing these data remains a challenge. This study developed novel ensemble tree-based models to enhance real-time predictions of chemical oxygen demand (COD) and total nitrogen (TN) concentrations, which are difficult to monitor directly. The effectiveness of these models, particularly the Voting Regressor, was demonstrated by achieving excellent predictive performance even with the small, volatile, and interconnected datasets typical of WWTP scenarios. By utilizing real-time sensor data from the anaerobic–anoxic–oxic (A2O) process, the model successfully predicted COD concentrations with an R² of 0.7722 and TN concentrations with an R² of 0.9282. In addition, a novel approach was proposed to assess A2O process performance by analyzing the correlation between the predicted C/N ratio and the removal efficiencies of COD and TN. During a one and a half year monitoring period, the predicted C/N ratio accurately reflected changes in COD and TN removal efficiencies across the different A2O bioreactors. The results provide real-time COD and TN predictions and a method for assessing A2O process performance based on the C/N ratio, which can significantly aid in the operation and maintenance of biological wastewater treatment processes.

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“…As indicated by prior research titled "Big Data Analytics from a Wastewater Treatment Plant", the significance of big data analytics in constructing a wastewater treatment plant model is noteworthy. The phases of big data analytics encompass data collection, data comprehension, data preprocessing, and data mining [21]. The details of big data analytics are as follows:…”

Section: Big Data Analyticsmentioning

confidence: 99%

Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models

Wongburi,

Park

2023

Water

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Artificial Intelligence (AI) has recently emerged as a powerful tool with versatile applications spanning various domains. AI replicates human intelligence processes through machinery and computer systems, finding utility in expert systems, image and speech recognition, machine vision, and natural language processing (NLP). One notable area with limited exploration pertains to using deep learning models, specifically Recurrent Neural Networks (RNNs), for predicting water quality in wastewater treatment plants (WWTPs). RNNs are purpose-built for handling sequential data, featuring a feedback mechanism. However, standard RNNs may exhibit limitations in accommodating both short-term and long-term dependencies when addressing intricate time series problems. The solution to this challenge lies in adopting Long Short-Term Memory (LSTM) cells, known for their inherent memory management through a ‘forget gate’ mechanism. In general, LSTM architecture demonstrates superior performance. WWTP data represent a historical series influenced by fluctuating environmental conditions. This study employs simple RNNs and LSTM architecture to construct prediction models for effluent parameters, systematically assessing their performance through various training data scenarios and model architectures. The primary objective was to determine the most suitable WWTP dataset model. The study revealed that an epoch setting of 50 and a batch size of 100 yielded the lowest training time and root mean square error (RMSE) values for both RNN and LSTM models. Furthermore, when these models are applied to predict effluent parameters, they exhibit precise RMSE values for all parameters. The study results can be applied to detect potential upsets in WWTP operations.

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Big Data Analytics from a Wastewater Treatment Plant

Cited by 10 publications

References 4 publications

Big Data Analysis in Human Resources Decision Making: Optimizing Workforce Management

Big Data Analysis in Human Resources Decision Making: Optimizing Workforce Management

Novel Ensemble Learning Approach for Predicting COD and TN: Model Development and Implementation

Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models

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