A novel approach in determination of the effluent applications

In this research, general regression neural network (GRNN), Hammerstein-wiener (HW) and non-linear autoregressive with exogenous (NARX) neural network, least square support vector machine (LSSVM) models were employed for multi-parametric (Hardness (mg/L), turbidity (Turb) (μs/cm), pH and suspended solid (SS) (mg/L)) modeling of Tamburawa water treatment plant (TWTP) at Kano, Nigeria. The weekly data from the TWTP were used and the predictive models were evaluated based on several numerical indicators. Four different non-linear ensemble techniques (GRNN-E, HW-E, NARX-E, and LSSVM-E) were subsequently employed. The comparison of the results of modeling showed that HW served as the best model for the simulation of Hardness, Turb, and SS while the NARX model demonstrated high capability in predicting pH. Yet, the HW and NARX as system identification techniques attained best overall predictive performance among the four modeling approaches. The HW model offers, therefore, a reliable and intelligent approach for predicting the treated Hardness, Turb, and SS and should be explored as a predictive tool in TWTP. Among the non-linear ensemble models, GRNN-E proved of high merit and increased the accuracy of the best single models significantly up to 30% for Hardness and Turb, 34% for pH, and 37% for SS with regards to the single models.

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Multi-parametric modeling of water treatment plant using AI-based non-linear ensemble

Abba

Nourani

Elkiran

2019

Journal of Water Supply: Research and Technology-Aqua

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Evaluation of treated municipal wastewater effluent for agricultural irrigation purposes using the fuzzy effluent quality index (FEQI)

2019

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The issues of freshwater scarcity in arid and semi-arid areas could be reduced via treated municipal wastewater effluent (TMWE). Artificial intelligence methods, especially the fuzzy inference system, have proven their ability in TMWE quality evaluation in complex and uncertain systems. The primary aim of this study was to use a Mamdani fuzzy inference system to present an index for agricultural application based on the Iranian water quality index (IWQI). Since the uncertainties were disregarded in the conventional IWQI, the present study improved this procedure by using fuzzy logic and then the fuzzy effluent quality index (FEQI) was proposed as a hybrid fuzzy-based index. TMWE samples of the Gheitarie wastewater treatment plant in Tehran city recorded from 2011 to 2017 were taken into consideration for testing the ability of the proposed index. The results of the FEQI showed samples categorized as ‘Excellent’ (21), ‘Good’ (10), ‘Fair’ (4), and ‘Marginal’ (1) for the warm seasons, and for the cool seasons, the samples categorized as ‘Excellent’, ‘Good’ and ‘Fair’ were 17, 18 and 1, respectively. Generally, a comparison between the IWQI and proposed model results revealed the FEQI's superiority in TMWE quality assessment.

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A novel approach in determination of the effluent applications

Cited by 2 publications

References 23 publications

Multi-parametric modeling of water treatment plant using AI-based non-linear ensemble

Multi-parametric modeling of water treatment plant using AI-based non-linear ensemble

Evaluation of treated municipal wastewater effluent for agricultural irrigation purposes using the fuzzy effluent quality index (FEQI)

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