Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Kumar, Vinod; Yadav, Manish; Singh, Vijai; Padhiyar, Hirendrasinh; Kumar, Vinod; Bhatia, Shashi Kant; Show, Pau Loke

doi:10.1016/j.biortech.2022.128486

Cited by 78 publications

(22 citation statements)

References 127 publications

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“…Four ML algorithms were selected for developing the soft sensors, including PLS, SVR, CUB, and QRNN. These algorithms have successfully been implemented in wastewater treatment applications with a track record of high prediction accuracy. ,− However, their selection for this study was based on their exceptional performance across a wide range of data scenarios, their ability to accommodate diverse feature distributions, and their capacity to handle complex correlations. These characteristics make them potentially suitable for application in the context of OWTS.…”

Section: Methodsmentioning

confidence: 99%

Development of a Soft Sensor Using Machine Learning Algorithms for Predicting the Water Quality of an Onsite Wastewater Treatment System

et al. 2023

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Developing advanced onsite wastewater treatment systems (OWTS) requires accurate and consistent water quality monitoring to evaluate treatment efficiency and ensure regulatory compliance. However, off-line parameters such as chemical oxygen demand (COD), total suspended solids (TSS), and Escherichia coli (E. coli) require sample collection and time-consuming laboratory analyses that do not provide real-time information of system performance or component failure. While real-time COD analyzers have emerged in recent years, they are not economically viable for onsite systems due to cost and chemical consumables. This study aimed to design and implement a real-time remote monitoring system for OWTS by developing several multi-input and single-output soft sensors. The soft sensor integrates data that can be obtained from well-established in-line sensors to accurately predict key water quality parameters, including COD, TSS, and E. coli concentrations. The temporal and spatial water quality data of an existing field-tested OWTS operated for almost two years (n = 56 data points) were used to evaluate the prediction performance of four machine learning algorithms. These algorithms, namely, partial least square regression (PLS), support vector regression (SVR), cubist regression (CUB), and quantile regression neural network (QRNN), were chosen as candidate algorithms for their prior application and effectiveness in wastewater treatment predictions. Water quality parameters that can be measured in-line, including turbidity, color, pH, NH4 +, NO3 –, and electrical conductivity, were selected as model inputs for predicting COD, TSS, and E. coli. The results revealed that the trained SVR model provided a statistically significant prediction for COD with a mean absolute percentage error (MAPE) of 14.5% and R 2 of 0.96. The CUB model provided the optimal predictive performance for TSS, with a MAPE of 24.8% and R 2 of 0.99. None of the models were able to achieve optimal prediction results for E. coli; however, the CUB model performed the best with a MAPE of 71.4% and R 2 of 0.22. Given the large fluctuation in the concentrations of COD, TSS, and E. coli within the OWTS wastewater dataset, the proposed soft sensor models adequately predicted COD and TSS, while E. coli prediction was comparatively less accurate and requires further improvement. These results indicate that although water quality datasets for the OWTS are relatively small, machine learning-based soft sensors can provide useful predictive estimates of off-line parameters and provide real-time monitoring capabilities that can be used to make adjustments to OWTS operations.

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Section: Methodsmentioning

confidence: 99%

Development of a Soft Sensor Using Machine Learning Algorithms for Predicting the Water Quality of an Onsite Wastewater Treatment System

et al. 2023

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“…Fortunately, monitoring nutrients is more straightforward, with well-established procedures to measure nitrogen and phosphorus typically incorporated into preexisting monitoring programs at wastewater treatment plants . To improve the utility of these monitoring techniques, automation and artificial intelligence will likely play a significant role in reducing the cost and increasing the accessibility of many of these analyses, which is why this is an active area of research. , …”

Section: Enabling Reuse Through Mwrc Regulationsmentioning

confidence: 99%

“…81 To improve the utility of these monitoring techniques, automation and artificial intelligence will likely play a significant role in reducing the cost and increasing the accessibility of many of these analyses, which is why this is an active area of research. 82,83 Fluorescence spectroscopy is gaining prominence as an inexpensive, noninvasive, and highly sensitive characterization technique that can provide real-time data on the organic constituents for secondary wastewater effluent. 84−86 Fluorescence spectroscopy uses high-energy light to excite electrons and cause fluorescence of specific molecules or moieties.…”

Section: ■ Introductionmentioning

confidence: 99%

The Future of Municipal Wastewater Reuse Concentrate Management: Drivers, Challenges, and Opportunities

Finnerty,

Childress,

Hardy

et al. 2023

Environ. Sci. Technol.

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Water reuse is rapidly becoming an integral feature of resilient water systems, where municipal wastewater undergoes advanced treatment, typically involving a sequence of ultrafiltration (UF), reverse osmosis (RO), and an advanced oxidation process (AOP). When RO is used, a concentrated waste stream is produced that is elevated in not only total dissolved solids but also metals, nutrients, and micropollutants that have passed through conventional wastewater treatment. Management of this RO concentrate�dubbed municipal wastewater reuse concentrate (MWRC)�will be critical to address, especially as water reuse practices become more widespread. Building on existing brine management practices, this review explores MWRC management options by identifying infrastructural needs and opportunities for multi-beneficial disposal. To safeguard environmental systems from the potential hazards of MWRC, disposal, monitoring, and regulatory techniques are discussed to promote the safety and affordability of implementing MWRC management. Furthermore, opportunities for resource recovery and valorization are differentiated, while economic techniques to revamp cost-benefit analysis for MWRC management are examined. The goal of this critical review is to create a common foundation for researchers, practitioners, and regulators by providing an interdisciplinary set of tools and frameworks to address the impending challenges and emerging opportunities of MWRC management.

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“…Deep learning has emerged in recent years as an influential technique for solving many engineering problems, particularly those involving image and speech recognition. 101,102 AI has been successfully employed in areas such as banking, entertainment, healthcare, transportation, finance, and education and can be used to improve traffic flow and security in transportation. In the field of education, AI can be used to personalize learning and offer students personalized feedback and help.…”

Section: Introduction To Ai and Its Applications In Renewable Energymentioning

confidence: 99%

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Nguyen,

Tarełko,

Sharma

et al. 2024

Energy Fuels

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Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systems needs a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) is an attractive option to solve the issue of poor interoperability in black-box methods. This review investigates the relationship between renewable energy (RE) and XAI. It emphasizes the potential advantages of XAI in improving the performance and efficacy of RE systems. It is realized that although the integration of XAI with RE has enormous potential to alter how energy is produced and consumed, possible hazards and barriers remain to be overcome, particularly concerning transparency, accountability, and fairness. Thus, extensive research is required to address the societal and ethical implications of using XAI in RE and to create standardized data sets and evaluation metrics. In summary, this paper shows the potential, perspectives, opportunities, and challenges of XAI application to RE system management and operation aiming to target the efficient energy-use goals for a more sustainable and trustworthy future.

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Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Cited by 78 publications

References 127 publications

Development of a Soft Sensor Using Machine Learning Algorithms for Predicting the Water Quality of an Onsite Wastewater Treatment System

Development of a Soft Sensor Using Machine Learning Algorithms for Predicting the Water Quality of an Onsite Wastewater Treatment System

The Future of Municipal Wastewater Reuse Concentrate Management: Drivers, Challenges, and Opportunities

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

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