Unleashing Analytics to Reduce Costs and Improve Quality in Wastewater Treatment

Zadorojniy, Alexander; Wasserkrug, Segev; Zeltyn, Sergey; Lipets, Vladimir

doi:10.1287/inte.2019.0990

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The intervention model for maintenance was optimized using these two indicators. Advanced analytics were applied to optimize WWTP operation and reduce cost (Zadorojniy et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Wastewater management decision‐making: A literature review and synthesis

Ko,

Norton,

Daigger

2024

Water Environment Research

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Wastewater management decision‐making is complicated because of: (1) a complex regulatory structure, (2) the wide variety of conflicting expectations by stakeholders external and internal to the responsible utility, and (2) constrains including regulatory requirements, available technologies and practices, and customer willingness to pay. This review synthesizes the results from over 200 papers published since 2000 and presents a decision‐making structure and process which is (1) science and fact‐based, (2) reflects sustainability, (3) clear and transparent, (4) inclusive, (5) produces an objective‐oriented decision, (6) scalable, (7) repeatable, and (8) efficient. Tools supporting the decision‐making process are reviewed, including Multi‐Criteria Decision Analysis (MCDA), Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), process modeling, economic assessments, Life Cycle Assessment (LCA), and Social Life Cycle Assessment (SLCA). Ultimately it was determined that engagement of decision‐makers and relevant stakeholders to assess their values and preferences, coupled with supporting data and analyses, is necessary to reach a decision that, critically, has the support needed for it to be implemented. The results demonstrate that an understanding of the components of the decision process, coupled with an orderly process, enables good wastewater management decision‐making.Practitioner Points A decision‐making structure and process leading to the selection of implementable solutions is presented. The process possesses the following attributes: (1) science and fact‐based, (2) reflect sustainability, (3) clear and transparent, (4) inclusive, (5) produce an objective‐oriented decision, (6) scalable, (7) repeatable, and (8) efficient An extensive summary and analysis of tools supporting the decision process are provided, including Multi‐Criteria Decision Analysis (MCDA), Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), process modeling, economic assessments, Life Cycle Assessment (LCA), and Social Life Cycle Assessment (SLCA). The critical role of internal and external stakeholders and differentiating their involvement relative to decision‐makers is emphasized.

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“…The intervention model for maintenance was optimized using these two indicators. Advanced analytics were applied to optimize WWTP operation and reduce cost (Zadorojniy et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Wastewater management decision‐making: A literature review and synthesis

Ko,

Norton,

Daigger

2024

Water Environment Research

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“…Among its advantages are its ability to identify non-linear relationships in the data, to generate simple and more easily interpreted models from a large number of input variables, to show their relative importance, and to be computationally efficient compared to other techniques [12][13][14]. In the field of WWTPs, it has recently been used in different studies to predict the biochemical and chemical oxygen demand, the nitrogen, phosphorus and total suspended solids concentration [7,15], the activated sludge sedimentation capacity [15] or cost reduction [16].…”

Section: Methodsmentioning

confidence: 99%

Sand Content Prediction in Urban WWTPs Using MARS

Pérez

Fernández

et al. 2020

Water

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The pre-treatment stage of wastewater treatment plants (WWTP), where most of the larger waste, including sand and fat, is removed, is of great importance for the performance and durability of these plants. This work develops a model that predicts the sand content that reaches the plant. For this purpose, data were collected from one operation year of the Villapérez Wastewater Treatment Plant located in the northeast of the city of Oviedo (Asturias, Spain) and the MARS (Multivariate Adaptive Regression Splines) method was used for modelling. The accuracy of the MARS model developed using the determination coefficient is R2 = 0.74 for training data and R2 = 0.70 in validation data. These results indicate that it is possible to predict trend changes in sand production as a function of input variables changes such as flow rate, pH, ammonia, etc. This will prevent the plant from possible operational problems, as actions could be taken, such as starting up more pre-treatment lines or emptying the containers, so that the arrival of the sand can be assumed without any problem. In this way, the possibility of letting sand contents over the established limits pass that could affect the following processes of the treatment plant is avoided.

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“…The results obtained yielded an airflow reduction of greater than 31%. MARS was also used comparatively with reinforcement learning (RL) and a constrained Markov decision process (CMDP) for the WWTP of Lleida, Spain [78]. In that study, RL had some calibration limitations, while MARS had a long runtime of four hours compared with the CMDP, which ran successfully in eight seconds.…”

Section: Machine Learning and Data Mining (Ml-dm) Control And Optimiz...mentioning

confidence: 99%

A Review of AI-Driven Control Strategies in the Activated Sludge Process with Emphasis on Aeration Control

Monday,

Zaghloul,

Krishnamurthy

et al. 2024

Water

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Recent concern over energy use in wastewater treatment plants (WWTPs) has spurred research on enhancing efficiency and identifying energy-saving technologies. Treating one cubic meter of wastewater consumes at least 0.18 kWh of electricity. About 50% of the energy consumed during this process is attributed to aeration, which varies based on treatment quality and facility size. To harness energy savings in WWTPs, the transition from traditional controls to artificial intelligence (AI)-based strategies has been observed. Research in this area has demonstrated significant improvements to the efficiency of wastewater treatment. This contribution offers an extensive review of the literature from the past decade. It aims to contribute to the ongoing discourse on improving the efficiency and the sustainability of WWTPs. It covers conventional and advanced control strategies, with a particular emphasis on AI-based control utilizing algorithms such as neural networks and fuzzy logic. The review includes four key areas of wastewater treatment AI research as follows: parameter forecasting, performance analysis, modeling development, and process optimization. It also points out potential disadvantages of using AI controls in WWTPs as well as research gaps such as the limited translation of AI strategies from research to real-world implementation and the challenges associated with implementing AI models outside of simulation environments.

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Unleashing Analytics to Reduce Costs and Improve Quality in Wastewater Treatment

Abstract: The authors discuss their development of an innovative process that applies descriptive, predictive, and prescriptive analytics to improve efficiency and reduce costs at wastewater treatment plants.

Cited by 8 publications

References 8 publications

Wastewater management decision‐making: A literature review and synthesis

Wastewater management decision‐making: A literature review and synthesis

Sand Content Prediction in Urban WWTPs Using MARS

A Review of AI-Driven Control Strategies in the Activated Sludge Process with Emphasis on Aeration Control

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