A Type-2 Fuzzy Logic Approach for Forecasting of Effluent Quality Parameters of Wastewater Treatment

In this investigation, we have studied and designed a type-2 fuzzy logic controller (IT2FLC) for the wastewater treatment plant at Haldia, India. To avoid modeling complex physical, chemical, and biological treatment processes of wastewater, this present work represents an ensemble of fuzzy models as surrogates for the wastewater treatment plant (WWTP). Using measured influent water quality data, each fuzzy model predicts water quality after the process of water treatment parameters. The pH, biological oxygen demand (BOD), total suspended particles (TSS), chemical oxygen demand (COD), and temperature are taken into account as input variables. Finally, the sensitivity of the IT2FLC model is evaluated by several statistical parameters like RMSE, MAE, MAPE, and most importantly R 2 value. For the current model, the values of the three parameters are almost 0, whereas the value of the R 2 is almost close to 1, which signifies that the IT2FLC model is accurate and more efficient in predicting response compared to other conventional methods reported in various literatures.

[1]  David O. Omole,et al.  RSM and ANN modelling of the mechanical properties of self-compacting concrete with silica fume and plastic waste as partial constituent replacement , 2022, Cleaner Materials.

[2]  Yaé Ulrich Gaba,et al.  Energy Source Allocation Decision-Making in Textile Industry: A Novel Symmetric and Asymmetric Spherical Fuzzy Linear Optimization Approach , 2022, Mathematical Problems in Engineering.

[3]  J. Kavikumar,et al.  Optimization in Fuzzy Economic Order Quantity Model Involving Pentagonal Fuzzy Parameter , 2021, Int. J. Fuzzy Syst..

[4]  Udaya Mouni Boppana,et al.  Comparative Analysis of Single-Stage YOLO Algorithms for Vehicle Detection Under Extreme Weather Conditions , 2022, IOT with Smart Systems.

[5]  Y. U. Gaba,et al.  Semigroup of Finite-State Deterministic Intuitionistic Fuzzy Automata with Application in Fault Diagnosis of an Aircraft Twin-Spool Turbofan Engine , 2021, Journal of Function Spaces.

[6]  Aihua Hu,et al.  Dynamic Event-Triggered Predictive Control for Interval Type-2 Fuzzy Systems with Imperfect Premise Matching , 2021, Entropy.

[7]  S. Uslu,et al.  Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine , 2021, Energy.

[8]  Harish Garg,et al.  Evidence Theory in Picture Fuzzy Set Environment , 2021 .

[9]  Said Broumi,et al.  Interval type-2 fuzzy aggregation operator in decision making and its application , 2021, Complex & Intelligent Systems.

[10]  S. P. Tiwari,et al.  On a factorized L-fuzzy automaton and its L-fuzzy topological characterization , 2020, Fuzzy Sets Syst..

[11]  P. Dostál,et al.  Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty , 2021 .

[12]  Siqi Zhao,et al.  Feed requirement determination of grass carp (Ctenopharyngodon idella) using a hybrid method of bioenergetics factorial model and fuzzy logic control technology under outdoor pond culturing systems , 2020 .

[13]  Lei Xie,et al.  Integrated forward and reverse logistics network design for a hybrid assembly-recycling system under uncertain return and waste flows: A fuzzy multi-objective programming , 2020, Journal of Cleaner Production.

[14]  Z. Zou,et al.  Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant , 2019, Mathematical Problems in Engineering.

[15]  Sachin S. Kamble,et al.  Fuzzy multi-criteria decision analysis for environmentally conscious solid waste treatment and disposal technology selection , 2019, Technology in Society.

[16]  Z. Zou,et al.  Forecasting of Wastewater Discharge and the Energy Consumption in China Based on Grey Model , 2019, Mathematical Problems in Engineering.

[17]  F. Tsai,et al.  Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model , 2018 .

[18]  L. Cárdenas-Barrón,et al.  Coordinating a socially responsible closed-loop supply chain with product recycling , 2017 .

[19]  Jinde Cao,et al.  Finite-time stabilisation for discrete-time T–S fuzzy model system with channel fading and two types of parametric uncertainty , 2017, Int. J. Syst. Sci..

[20]  Michel Gendreau,et al.  Accelerating Benders decomposition for closed-loop supply chain network design: Case of used durable products with different quality levels , 2016, Eur. J. Oper. Res..

[21]  H. Akyar.  Fuzzy Risk Analysis for a Production System Based on the Nagel Point of a Triangle , 2016 .

[22]  Nadiri Ata Allah,et al.  EFFICIENCY CONSIDERATION OF WASTEWATER TREATMENT PLANT OF TABRIZ USING ARTIFICIAL INTELLIGENCE MODELS , 2015 .

[23]  Damià Barceló,et al.  Fuzzy logic based risk assessment of effluents from waste-water treatment plants. , 2012, The Science of the total environment.

[24]  Tien-Fu Liang,et al.  Application of Fuzzy Sets to Aggregate Production Planning With Multiproducts and Multitime Periods , 2011, IEEE Transactions on Fuzzy Systems.

[25]  Sukran Yalpir,et al.  Prediction of primary treatment effluent parameters by Fuzzy Inference System (FIS) approach , 2011, WCIT.

[26]  A. Malik,et al.  Artificial neural network modeling of the river water quality—A case study , 2009 .

[27]  Witold Pedrycz,et al.  Type-2 Fuzzy Logic: Theory and Applications , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[28]  J. Kavikumar,et al.  Fuzzy ideals and fuzzy quasi-ideals in ternary semirings , 2007 .