Prediction of primary treatment effluent parameters by Fuzzy Inference System (FIS) approach

Yel, Esra; Yalpır, Şükran

doi:10.1016/j.procs.2010.12.110

Cited by 31 publications

(10 citation statements)

References 14 publications

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“…Constitution, strength, and volumetric flow rate of the raw wastewater fluctuate from time to time [37]. That is why modelling study becomes mandatory for optimum operation control.…”

Section: Fuzzy Logic Modelingmentioning

confidence: 99%

“…Due to its high precision ability and flexibility in use, fuzzy logic has been applied in many of the environmental engineering problems from air pollution to water treatment systems. Research in [35][36][37] stated that fuzzy logic is one of the methods to apply the expert knowledge to form an advanced control on various treatment processes.…”

Section: Introductionmentioning

confidence: 99%

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Determination of Biological Treatability Processes of Textile Wastewater and Implementation of a Fuzzy Logic Model

Kabuk

Avşar

Kuzu

et al. 2015

International Journal of Photoenergy

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This study investigated the biological treatability of textile wastewater. For this purpose, a membrane bioreactor (MBR) was utilized for biological treatment after the ozonation process. Due to the refractory organic contents of textile wastewater that has a low biodegradability capacity, ozonation was implemented as an advanced oxidation process prior to the MBR system to increase the biodegradability of the wastewater. Textile wastewater, oxidized by ozonation, was fed to the MBR at different hydraulic retention times (HRT). During the process, color, chemical oxygen demand (COD), and biochemical oxygen demand (BOD) removal efficiencies were monitored for 24-hour, 12-hour, 6-hour, and 3-hour retention times. Under these conditions, 94% color, 65% COD, and 55% BOD removal efficiencies were obtained in the MBR system. The experimental outputs were modeled with multiple linear regressions (MLR) and fuzzy logic. MLR results suggested that color removal is more related to COD removal relative to BOD removal. A surface map of this issue was prepared with a fuzzy logic model. Furthermore, fuzzy logic was employed to the whole modeling of the biological system treatment. Determination coefficients for COD, BOD, and color removal efficiencies were 0.96, 0.97, and 0.92, respectively.

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“…Constitution, strength, and volumetric flow rate of the raw wastewater fluctuate from time to time [37]. That is why modelling study becomes mandatory for optimum operation control.…”

Section: Fuzzy Logic Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of Biological Treatability Processes of Textile Wastewater and Implementation of a Fuzzy Logic Model

Kabuk

Avşar

Kuzu

et al. 2015

International Journal of Photoenergy

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“…Other researchers (group c) tried to predict yarn parameters after the heat setting process using artificial intelligence (Behera & Guruprasad, 2012;Çeven & Ozdemir, 2007;Lin, 2007;Majumdar, Majumdar, & Sarkar, 2005;Yel & Yalpir, 2011). Lin (2007) predicted the shrinkages of warp and weft yarns in the finished woven fabrics using ANNs.…”

Section: Please Scroll Down For Articlementioning

confidence: 99%

Comparison between artificial neural network and response surface methodology in the prediction of the parameters of heat set polypropylene yarns

Dadgar

Varkiyani

Merati

2014

The Journal of The Textile Institute

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In the present paper, a response surface model has been introduced to predict the geometrical parameters of heat set polypropylene pile yarns. The input factors of the presented model include yarn twist, initial yarn count, time, and temperature of heat setting and the response factors are yarn count, yarn shrinkage, crimp contraction and packing factor after the heat setting process. To analyse the effect of this process on the yarn parameters, the dry heat setting process has been applied to all samples at different times and temperatures using an oven equipped with air circulation because of better accuracy and control of temperature. The obtained results showed that there is a positive relation between time and temperature and output parameters. Finally, the predicting equations discussions about the optimum points for maximum shrinkage and interactions of parameters have been presented. Hence, due to some disability of the RSM method, an ANN model has been designed to predict the parameters at higher accuracy. The results of the accomplished ANN model represent a higher prediction correlation coefficient compared to RSM.

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“…The initial pre-treatment phase is of great importance for the proper functioning of WWTP (wastewater treatment plants) [1]; however, as indicated by several authors [2,3], the next steps of these facilities have been studied further given their great impact on water recovery. Parameters such as pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD) and suspended solids (SS) in later stages have been the subject of numerous studies [4][5][6][7]. In contrast, the pretreatment stage has been studied much less, and efficient operation is considered to primarily depend on a good initial plant design and proper operations management.…”

Section: Introductionmentioning

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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Prediction of primary treatment effluent parameters by Fuzzy Inference System (FIS) approach

Cited by 31 publications

References 14 publications

Determination of Biological Treatability Processes of Textile Wastewater and Implementation of a Fuzzy Logic Model

Determination of Biological Treatability Processes of Textile Wastewater and Implementation of a Fuzzy Logic Model

Comparison between artificial neural network and response surface methodology in the prediction of the parameters of heat set polypropylene yarns

Sand Content Prediction in Urban WWTPs Using MARS

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