Hybrid Model of a Wastewater-Treatment Electrolytic Process (original) (raw)
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Modeling of electrolysis process in wastewater treatment using different types of neural networks
Chemical Engineering …, 2011
Indirect electrolysis has been used for the removal of chlorophyll a (as indicator of algae) from the final effluent of aerated lagoons in the wastewater treatment plant of Bu-Ali Industrial Estate. The efficiency of the process was studied experimentally and by simulation using neural networks. The process analysis was done in different conditions of retention time (5-50 min) and using two types of electrodes based on aluminum and stainless steel, with different distances between electrodes (from 1.0 to 3.5 cm). The electrical current and the average voltage applied were between 5 and 90 A (0.74-12 A dm −3 ) and 50 V, respectively. The influence of the main parameters of the electrolysis process on the final values for chlorophyll a, TSS and COD is evaluated experimentally. On the other hand, predictions of the main system outputs of a treated waste as a function of initial characteristics (initial values of chlorophyll a, TSS, COD) and operation conditions (temperature, electric power, time, electrode distance, and electrode type) were performed using artificial neural networks. The modeling methodologies elaborated in this paper are based on different types of neural networks, used individually or aggregated in stacks. They were developed gradually in the sense of improving the model performance. The neural network results represent accurate predictions, useful for experimental practice.
A Neural Network Model for Control of Wastewater Treatment Processes
IFAC Proceedings Volumes, 2007
This paper discusses the development of a neural network model for the prediction of the influent disturbances, which ultimately affect the activated sludge process. Neural networks are particularly suited to problems where there is no clear understanding of the processes and the complex interrelationship between variables. The historical data used for training and testing the neural network is actual plant data obtained from a municipal plant and weather data for the same time periods. The result of the predicted influent disturbance is used in the control of the dissolved oxygen (DO). The results are applied to a pilot wastewater treatment plant located at the Cape Peninsula University of Technology (CPUT). The number of and the type inputs are varied to find an optimal model in order to predict the Chemical Oxygen Demand (COD), Total Kjeldahl Nitrogen (TKN) and the flowrate. Three different dynamic multilayer perceptron (MLP) feed-forward neural network models are developed for the influent disturbances of COD, TKN and flowrate respectively.
NEURAL NETWORKS SIMULATION AND OPTIMIZATION OF WASTE WATER TREATMENT
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ANN MODEL OF WASTEWATER TREATMENT PROCESS
In this work, total solids reduction process was numerically modeled with response surface methodology (RSM) and Artificial neural network (ANN) models. The experimental data was used for training these models. Amplitude of the ultrasonic waves, time of ultrasonication and total solids present in the sludge are input to the model. These factors are varied to five levels and by conducting design of experiments, the actual values were measured. The response surface methodology was used to determine the relation between the factors and total reduction in solids. To overcome the flaws in the response surface methodology, an artificial neural network model is developed and the results of the ANN models are compared with RSM models and experimentally measured values.
Artificial Neural Network Based Model in Effluent Treatment Process
2011
During the past 30 years the industrial sector in India has quadrupled in size, thus it increases the pressure on wastewater treatment industries to produce higher quality treated water at a lower cost. The efficiency of a treatment process closely relates to the operation of the plant. To improve the operating performance, an Artificial Neural Network (ANN) paradigm has been applied to an effluent treatment plant. An ANN which is able to learn the non-linear performance relationships of historical data of a plant has been proved to be capable of providing operational guidance for plant operators. In this investigation the application of Artificial Neural Network (ANN) techniques are used to predict the Chemical Oxygen Demand for effluent treatment process. Sets of historical plant data of COD were collected from common effluent treatment plant at Govindpura, Bhopal (India). Data were collected over a period of 3 years from the influent and effluent streams of the station. Two ANN-b...
Archives of Environmental Protection, 2023
Biological treatment in wastewater treatment plants appears to be one of the most crucial factors in water quality management and planning. Though, measuring this important factor is challenging, and obtaining reliable results requires significant effort. However, the use of artificial neural network (ANN) modeling can help to more reliably and cost-effectively monitor the pollutant characteristics of wastewater treatment plants and regulate the processing of these pollutants. To create an artifi cial neural network model, a study of the Samsun Eastern Advanced Biological WWTP was carried out. It provides a laboratory simulation and prediction option for flexible treatment process simulations. The models were created to forecast influent features that would aff ecteffluent quality metrics. For ANN models, the correlation coefficients RTRAINING and RALL are more than 0.8080. The MSE, RMSE, and MAPE were less than 0.8704. The model’s results showed compliance with the permitted wastewater quality standards set forth in the Turkish water pollution control law for the environment where the treated wastewater is discharged.
The processes can be applied in the treatment of wastewaters containing toxic or hazardous, non-biodegradable organic compounds. The aim of this work was to investigate the degradation kinetics of a petrochemical industry wastewater containing polypropylene oligomers by the UV/H 2 O 2 system. Bench-scale experiments were performed in an annular photochemical reactor using artificial light sources of different power (medium pressure mercury arc lamps of 80, 125, 250, and 400 W). Samples were withdrawn from the reactor at regular times and analyzed for total organic carbon (TOC). A model based on artificial neural networks was developed for fitting the experimental data obtained in the bench-scale batch reactor. The model describes the evolution of the total organic carbon concentration along irradiation time under different conditions, and can be used to simulate the behavior of the reaction system, thus enabling the mapping of process conditions such as hydrogen peroxide concentration and light source and their relation to degradation rate. The use of a neural network model to describe the wastewater photodegradation rate has shown promising results since it can describe the behavior of the complex-reaction system within the range of experimental conditions adopted. These information are essential for the adequate scale-up and design of photochemical reactors for industrial use.
Neural network modelling and control strategies for a pH process
Journal of Process Control, 1995
The control of a pH process using neural networks is examined. The neural network as a universal approximator is used to good effect in this nonlinear problem, as is shown in the simulation results. In the modelling task, the dynamics of the process was carefully examined to determine a suitable structure for the net. In particular, a multilayer net consisting of two single hidden layers was constructed to reflect the Wiener model of the pH process. This led to much simpler training compared to similar modelling attempts by other researchers. For the control task, two schemes were simulated. In one approach, a net was used to deal with the static nonlinearity to achieve control over a wide working range. The dynamic controller used was the PID, with its parameters tuned on a relay auto-tuner. This control design was compared with the strong acid equivalent method. In the second approach, a direct model reference adaptive neural network control scheme was proposed. The training procedure uses the more efficient least squares algorithm developed by Loh and Fong.
An Artificial Neural Network Model for Wastewater Treatment Plant of Konya
International Journal of Intelligent Systems and Applications in Engineering, 2015
In this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account of input values of pH, temperature, COD, TSS and BOD with output values TSS. Performance of the model was compared via the parameters of Mean Squared Error (MSE), and correlation coefficient (R). The suitable architecture of the neural network model is determined after several trial and error steps. According to the modelling study, the ANN can predict the plant performance with correlation coefficient (R) between the observed and predicted output variable reached up to 0.96.