The influence of oversizing on maintenance cost in wastewater treatment plants

Hernández-Chover, Vicent; Bellver-Domingo, Águeda; Hernández-Sancho, Francesc

doi:10.1016/j.psep.2020.12.033

Cited by 14 publications

(3 citation statements)

References 53 publications

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“…Hybrid system/ industries 25 Electric overhead travelling crane (Dhalmahapatra et al, 2019), electrical traction system (Chen et al, 2020), anode production equipment (Kolokas et al, 2018), carbon black production (Jones et al, 2010), actuator system (Waghen & Ouali, 2019), pneumatic systems (Demetgul et al, 2009;Nasiri et al, 2017), electronics system (Cai et al, 2016b), and fixture fault (Jin et al, 2012), moving window (Melani et al, 2021), water tank (Lampis & Andrews, 2009;Lindner & Auret, 2014), heavy-duty horizontal lathe (Mi et al, 2018), waste water treatment equipment (Hernandez-Chover et al, 2021;Kutyłowska, 2015;Lee, Yoo, & Lee, 2004), Tennessee Eastman process (Cai et al, 2015;Ge & Song, 2007;Oliveira et al, 2017), reactor for cyclopentenol ( de Souza D et al, 2014), distillation column (Chetouani, 2014), continuously-stirred tank heater (Rodriguez Ramos et al, 2017;Rodriguez Ramos et al, 2019;Smith & Powell, 2019), and automotive exhaust systems (Soleimani et al, 2021).…”

Section: Aerospace and Aviation Industry 11mentioning

confidence: 99%

“…The US companies are spending more than $200 billion annually on maintaining their equipment and machinery (Jun & Kim, 2017). The maintenance cost of a waste water treatment plant can reach up to 25% of total operational costs (Hernandez‐Chover et al, 2021). Hence, it is necessary to put all efforts to make the industrial equipment operational so that continuous production can be achieved with improved reliability and availability of equipment.…”

Section: Introductionmentioning

confidence: 99%

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Data‐driven fault diagnosis approaches for industrial equipment: A review

Sahu¹,

Palei

Mishra

2023

Expert Systems

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Undetected and unpredicted faults in heavy industrial machines/equipment can lead to unwanted failures. Therefore, prediction of faults puts paramount importance on maintaining the reliability and availability of capital‐intensive equipment. Due to large number of interconnected and interdependent mechanical and electrical components in the machines, fault analysis becomes a complex and challenging task. Under these circumstances, data‐driven fault diagnosis (DDFD) is one of the most powerful, reliable and cost‐effective artificial intelligence tools to detect, isolate, identify and classify the occurrence of faults. This article aims to make a comprehensive literature survey of various DDFD approaches used for analysing faults in industrial machines/equipment; and summarizing the strengths, limitations, and possible applications of existing fault diagnosis methods. Analysing and synthesizing 190 research works conducted on DDFD in last two decades revealed three types of DDFD approaches: supervised‐learning, semi‐supervised‐learning and unsupervised‐learning‐based fault diagnosis. The supervised‐learning is predominantly applied for fault diagnosis contributing to 82% of research works. Therefore, this article puts special emphasis on two supervised‐learning‐based approaches for fault diagnosis: (i) classification‐based artificial neural network approach, and (ii) inference‐based Bayesian network approach. Finally, these fault diagnosis approaches have been briefly discussed with effectiveness of the models and their possible inclusion in future industrial applications.

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Section: Aerospace and Aviation Industry 11mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data‐driven fault diagnosis approaches for industrial equipment: A review

Sahu¹,

Palei

Mishra

2023

Expert Systems

View full text Add to dashboard Cite

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“…The improvement of operations in treatment plants and its impact on their performance, the reduction of energy consumption, and the reduction of maintenance costs is receiving more and more attention from researchers [15][16][17][18]. The increasingly strict legal and environmental requirements force us to seek an improvement in the operation of these facilities [19,20].…”

mentioning

confidence: 99%

Gross Solids Content Prediction in Urban WWTPs Using SVM

Pérez

Fernández

et al. 2021

Water

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The preliminary treatment of wastewater at wastewater treatment plants (WWTPs) is of great importance for the performance and durability of these plants. One fraction that is removed at this initial stage is commonly called gross solids and can cause various operational, downstream performance, or maintenance problems. To avoid this, data from more than two operation years of the Villapérez Wastewater Treatment Plant, located in the northeast of the city of Oviedo (Asturias, Spain), were collected and used to develop a model that predicts the gross solids content that reaches the plant. The support vector machine (SVM) method was used for modelling. The achieved model precision (Radj2 = 0.7 and MSE = 0.43) allows early detection of trend changes in the arrival of gross solids and will improve plant operations by avoiding blockages and overflows. The results obtained indicate that it is possible to predict trend changes in gross solids content as a function of the selected input variables. This will prevent the plant from suffering possible operational problems or discharges of untreated wastewater as actions could be taken, such as starting up more pretreatment lines or emptying the containers.

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