Energy consumption is a primary issue needed to be considered for wastewater treatment targeting qualified effluent. In this paper, a hybrid model is proposed for rapid diagnosis of operational conditions meeting requirements of discharge standards and energy saving in the pilot-and full-scale Carrousel Oxidation Ditches (ODs). Based on a three-dimensional (3D) three-phase computational fluid dynamics (CFD) model, we developed an artificial neural network (ANN) model with back propagation algorithm and an accelerating genetic algorithm (AGA) model to achieve real-time simulation and system optimization in the Carrousel ODs. By incorporating the 3D-CFD and multi-site ANN models, the hybrid model provided reasonable predictions of liquid flow, sludge sedimentation and water quality in the Carrousel ODs. With help of the AGA model based on evolution theory, system optimization could be reached to meet multiple purposes such as energy saving, water-quality improving and normal sludge distribution, which was demonstrated that a 31% saving in total energy could possibly be made under an optimum operating condition compared to the existing operating condition in a full-scale OD

Borthwick, Alistair

Lei, Li

Li, Li

Ni, Jinren

Zheng, Maosheng

Journal of Environmental Informatics

English

Accurate modeling of Carrousel oxidation ditches (ODs) is of great importance in optimizing their operating conditions to reduce energy consumption, ensure qualified effluent, and achieve even deposits of sludge. This paper presents a hybrid model composed of a three-dimensional (3D) three-phase computational fluid dynamics (CFD) model, a multi-site artificial neural network (ANN) model with back propagation, and an accelerating genetic algorithm (AGA) model to achieve real-time simulation and synchronized system optimization in the Carrousel OD. The 3D three-phase CFD model provided comprehensive, precise simulation of OD systems by treating activated sludge as a pseudo-solid phase and considering water-sludge-gas interactions. By coupling the 3D three-phase CFD model with a multi-site ANN model, the resulting hybrid model provided computationally fast, accurate predictions of liquid flow field, sludge sedimentation, dissolved oxygen distribution, and water quality parameters in the OD. An evolution theory-based AGA model, which incorporated the ANN model to compute fitness values, was used to perform global optimization of the operating conditions in the OD, and so identify optimum conditions that reduced energy consumption, prevented uneven deposits of sludge, and satisfied effluent standards. The proposed hybrid model was successfully applied to pilot-scale and full-scale ODs. The results showed the potential of the hybrid model to realize rapid prediction of phase motions and interactions in the OD, and to achieve real-time OD process optimization

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     Edinburgh Research Explorer                                      Stochastic evolutionary-based optimization for rapid diagnosisand energy-saving in pilot- and full-scale Carrousel oxidationditchesCitation for published version:Borthwick, A, Li, L, Lei, L, Zheng, M & Ni, J 2020, 'Stochastic evolutionary-based optimization for rapiddiagnosis and energy-saving in pilot- and full-scale Carrousel oxidation ditches', Journal of environmentalinformatics. https://doi.org/10.3808/jei.201700377Digital Object Identifier (DOI):10.3808/jei.201700377Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Peer reviewed versionPublished In:Journal of environmental informaticsGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 11. May. 2020 Fig. 1 Framework of hybrid model of conditions in the oxidation ditch, comprising a three-dimensional (3D) three-phase computational fluid dynamics (CFD) model, multi-site artificial neural network (ANN) model and accelerating genetic algorithm (AGA) model.  Fig. 2 Schematic of pilot-scale oxidation ditch and monitoring sites (Unit: mm).  Fig. 3 Comparisons between calculated and measured values of: (a) ammonia nitrogen concentration; (b) nitrate concentration; (c) TN concentration; (d) DO concentration; (e) COD concentration; (f) MLSS concentration; and (g) liquid velocity in a pilot-scale OD. Fig. 4 Stream-wise profiles of (a) predicted liquid velocity and (b) concentration of MLSS at elevation 0.1 m above the bottom of the pilot-scale oxidation ditch, for an aeration rate of 1.4 m3/h.  Fig. 5 Stream-wise profiles of dissolved oxygen at elevation 0.1 m above the bottom of the pilot-scale oxidation ditch, for aeration rates of (a) 1.4 m3/h and (b) 3.0 m3/h.    Fig. 6 Predicted effluent concentrations of (a) ammonia nitrogen, (b) nitrate, (c) TN, and (d) COD as functions of aeration rate, at an elevation 0.25 m above the bottom of the pilot-scale OD under three different operation modes. Fig. 7 Full-scale oxidation ditch and monitoring sites, Ping Dingshan, Henan Province, China (Unit: m).Table 1 Rotational modes of impellers and stirrers.   Moving part Case I Case II Case III Case IV Speed (rpm) Direction Speed (rpm) Direction Speed (rpm) Direction Speed (rpm) Direction Impeller 1 40 +1 80 + 180 + 40 + Impeller 2 40 + 80 + 180 + 40 + Stirrer 1 40 -2 90 - 120 - 70 - Stirrer 2 40 + 90 + 120 + 70 + Stirrer 3 40 + 90 + 120 + 70 + Stirrer 4 50 + 70 + 115 + 50 + 1 + clockwise rotation. 2 – anticlockwise rotation. Table 2 Experimental conditions for testing the ANN model in the pilot-scale oxidation ditch. Inflow discharge (L/h) TN (mg/L) Carbon-nitrogen ratio MLSS (g/L) Aeration rate  (m3/h) Speeds of impellers and stirrers 100 50 3 3.9 1.4 Case IV 100 50 3 3.9 1.8 Case IV 100 50 3 3.9 2.2 Case IV 100 50 3 3.9 2.6 Case IV 100 50 3 3.9 3 Case IV 100 50 5 3.9 1.4 Case IV 100 50 5 3.9 1.8 Case IV 100 50 5 3.9 2.2 Case IV 100 50 5 3.9 2.6 Case IV 100 50 5 3.9 3 Case IV 100 50 7 3.9 1.4 Case IV 100 50 7 3.9 1.8 Case IV 100 50 7 3.9 2.2 Case IV 100 50 7 3.9 2.6 Case IV 100 50 7 3.9 3 Case IV Table 3 Optimum structures and test results of the ANN model in the pilot-scale oxidation ditch. Variable Structure MSE OF R2 Ammonia nitrogen 12−11−10−1 -27.34 10   0.33% 0.9996 Nitrate 12−8−13−1 -12.21 10  0.32% 0.9990 TN 12−14−4−1 -15.69 10  0.33% 0.9963 DO 12−7−14−1 -33.34 10  1.13% 0.9922 COD 12−12−9−1 -34.87 10  0.04% 0.9982 MLSS 12−14−13−1 -37.38 10  0.23% 0.9031 Liquid velocity 12−11−8−1 -55.26 10  1.36% 0.9822 Table 4 Optimized operating condition in the pilot-scale OD.  Operation condition Effluent quality v9 E10 x1 a21 a2 b31 b2 b3 b4 NH4 NO5 TN6 DO7 COD8 1.60 46.35 43.02 47.06 55.73 78.67 88.76 4.36 0.14 4.50 0.01 25.0336 0.07 216.90 x1: aeration rate, m3/h; a2: rotating speed of impeller, rpm; b3: rotating speed of stirrer, rpm; NH4: concentration of ammonia nitrogen, mg/L; NO5: concentration of nitrate, mg/L; TN6: concentration of total nitrogen, mg/L; DO7: concentration of dissolved oxygen, mg/L; COD8: concentration of COD, mg/L; v 9: averaged liquid velocity, m/s; E10: energy consumption, W. Table 5 Optimum structures and test results of the ANN model in the full-scale oxidation ditch, Ping Dingshan, Henan Province, China. Variable Structure MSE RSD R2 BLE Ammonia nitrogen 23-15-13-1 0.2701 0.17% 0.9864 y=0.9973x+0.0571 Nitrate 23-14-4-1 0.1570 0.59% 0.9523 y=0.9602x+0.085 TN 23-14-9-1 0.3416 0.14% 0.9704 y=0.9952x+0.0709 COD 23-12-2-1 2.4733 0.21% 0.9886 y=0.9786x+0.5775 Liquid velocity 23-7-8-1 0.0004 0.60% 0.9216 y=0.9421x+0.0078 R2: Correlation coefficient BLE: Best linear fitting equationTable 6 Optimized operating condition in full-scale OD, Ping Dingshan, Henan Province, China.   Number Surface aeration Submerged impeller Effluent quality 7v  E8 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 S1 S2 S3 S4 S5 S6 S7 S8 S9 NH3 NO4 TN5 COD6 1 01 12 0 1 0 1 0 1 0 1 0 0 1 0 0 1 0 1 1 0 0 1 4.76 9.08 13.84 30.19 0.16 238.76 2 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 0 0 1 4.12 3.39 7.51 30.04 0.16 238.76 3 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 4.43 2.46 6.89 28.65 0.16 238.76 4 1 0 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 0 0 1 4.89 4.64 9.53 29.96 0.16 238.76 5 1 0 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 2.48 4.01 6.49 29.76 0.16 238.76 01: Non-operation; 12: Operation; NH3: concentration of ammonia nitrogen, mg/L; NO3: concentration of nitrate, mg/L; TN5: concentration of total nitrogen, mg/L; COD6: concentration of COD, mg/L; v7: averaged liquid velocity, m/s; E8: energy consumption, kW.   

Stochastic evolutionary-based optimization for rapid diagnosis and energy-saving in pilot- and full-scale Carrousel oxidation ditches

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