Prediction of Major Wastewater Characteristics of Urban Wastewater Treatment Plant Using Fuzzy Composite Programing

Khambete, A. K.; Christian, R. A.; Anaokar, G. S.

doi:10.1007/s41101-017-0029-z

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…Small WWTPs in the UK were found to benefit from the application of an RF prediction model 12 . Another pre-processing approaches and selection of various features of digestion system, increases the training speed, improves prediction accuracy, and simplifies the models 13 . Most forecasting studies, however, use correlation models, such as the Pearson correlation approach.…”

Section: Introductionmentioning

confidence: 99%

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

Mathur,

Sharma,

Loganathan

et al. 2024

Sci Rep

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The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.

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Section: Introductionmentioning

confidence: 99%

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

Mathur,

Sharma,

Loganathan

et al. 2024

Sci Rep

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show abstract

“…Fuzzy indexing, fuzzy composite programming, Fuzzy neural networks, etc. are few attempts found in environmental modeling 25–27 …”

Section: Introductionmentioning

confidence: 99%

“…are few attempts found in environmental modeling. [25][26][27] A nomogram is a graphical calculating scale or a two-dimensional diagram which allows the approximate computation of a function.…”

mentioning

confidence: 99%

Nomograms as a graphical model to predict biogas at municipal wastewater treatment plant using fuzzy indices

Anaokar¹,

Khambete

Christian

2022

Env Prog and Sustain Energy

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The strength of municipal wastewater and sludge considering their potential for biogasification can be expressed in terms of multiple parameter comprehensive indices (MPCI), namely, Wastewater Comprehensive Index (WWCI) and Sludge Comprehensive Index (SCI), respectively. Both of these indices can be calculated using fuzzy multiple criteria decisions making (FMCDM). Simple linear equations are established to express the relation between these indices, suspended solids concentration and biogas. The linear equation is developed to predict biogas from multiple parameter comprehensive indices, which indicate the linear relation between strength of wastewater, sludge and biogas. Nomogram was developed to get immediate quantification of biogas by linear scaling of suspended solid concentration and multiple parameter comprehensive indices (MPCI). Using a linear equation obtained from regression analysis; a suitable scale was developed to construct the nomogram. The biogas was found 96% precise with reference to measured one when estimated using nomograms. A single parameter, that is, suspended solids concentration will be monitored at municipal wastewater treatment plant, by digital device; to save chemicals, time, power and human resource. While taking efforts toward smart cities; digitalization and resource conservation will be creditable.

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Prediction of Major Wastewater Characteristics of Urban Wastewater Treatment Plant Using Fuzzy Composite Programing

Cited by 2 publications

References 11 publications

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

Nomograms as a graphical model to predict biogas at municipal wastewater treatment plant using fuzzy indices

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