Analysis of municipal wastewater treatment plant performance using artificial neural network approach

Artificial neural network (ANN) was used in this research as a statistical modeling tool for predicting the performance of wastewater treatment plant. A two years data of the waste water treatment plants’ effluent and influent parameters was collected and applied in developing and training the ANN using the ANN toolbox in MATLAB. The data were obtained from Bandar Tun Razak Sewage Treatment Plant (BTR STP), that is managed by Indah Water Konsurtium (IWK), Malaysia's national sewerage company. The input and output parameters for the ANN were BOD, SS, and COD. It was found that the use of data screening is essential to come up with better ANNs model. Moreover, using multiple input-single output models was even a better model than single input-single output. The optimum number of hidden layer and neurons were determined which gave excellent results in predicting both the BOD and COD of the effluent which are required by the DOE. From the regression analysis, networks with one hidden layer and 20 nodes and BOD as input and COD as output were found to be the best one. The optimum number of hidden layers is 10 and the R value is improved by 30 %. The Mean Squared Error (MSE) is the lowest for the network. From the regression analysis, it is obvious that networks using screened data give better results in term of R values and MSE, and were selected for the subsequent modeling analysis in this study, that is prediction

Application of Neural Network in the Prediction of NOx Emissions from Degrading Gas Turbine

This paper is aiming to apply neural network algorithm for predicting the process response (NOx emissions) from degrading natural gas turbines. Nine different process variables, or predictors, are considered in the predictive modelling. It is found out that the model trained by neural network algorithm should use part of recent data in the training and validation sets accounting for the impact of the system degradation. R-Square values of the training and validation sets demonstrate the validity of the model. The residue plot, without any clear pattern, shows the model is appropriate. The ranking of the importance of the process variables are demonstrated and the prediction profile confirms the significance of the process variables. The model trained by using neural network algorithm manifests the optimal settings of the process variables to reach the minimum value of NOx emissions from the degrading gas turbine system

Artificial Neural Network-Cuckoo Optimization Algorithm (ANN-COA) for Optimal Control of Khorramabad Wastewater Treatment Plant, Iran

In this study a hybrid estimation model ANN-COA developed to provide an accurate prediction of a Wastewater Treatment Plant (WWTP). An effective strategy for detection of some output parameters tested on a hardware setup in WWTP. This model is designed utilizing Artificial Neural Network (ANN) and Cuckoo Optimization Algorithm (COA) to improve model performances; which is trained by a historical set of data collected during a 6 months operation. ANN-COA based on the difference between the measured and simulated values, allowed a quick revealing of the faults. The method could obtain the fault detection and used in solving continuous and discrete optimization problems, successfully. After constructing and modelling the method, selected performance indices including coefficient of Regression, Mean-Square Error, Root-Mean-Square Error and Aggregated Measure used to compare the obtained results. This analysis revealed that the hybrid ANN-COA model offers a higher degree of accuracy for predicting and control the WWTP

Experimental and neural model analysis of styrene removal from polluted air in a biofilter

BACKGROUND: Biofilters are efficient systems for treating malodorous emissions. The mechanism involved during pollutant transfer and subsequent biotransformation within a biofilm is a complex process. The use of artificial neural networks to model the performance of biofilters using easily measurable state variables appears to be an effective alternative to conventional phenomenological modelling. RESULTS: An artificial neural network model was used to predict the extent of styrene removal in a perlite-biofilter inoculated with a mixed microbial culture. After a 43 day biofilter acclimation period, styrene removal experiments were carried out by subjecting the bioreactor to different flow rates (0.15–0.9 m3 h−1) and concentrations (0.5–17.2 g m−3), that correspond to inlet loading rates up to 1390 g m−3 h−1. During the different phases of continuous biofilter operation, greater than 92% styrene removal was achievable for loading rates up to 250 g m−3 h−1. A back propagation neural network algorithm was applied to model and predict the removal efficiency (%) of this process using inlet concentration (g m−3) and unit flow (h−1) as input variables. The data points were divided into training (115 × 3) and testing set (42 × 3). The most reliable condition for the network was selected by a trial and error approach and by estimating the determination coefficient (R2) value (0.98) achieved during prediction of the testing set. CONCLUSION: The results showed that a simple neural network based model with a topology of 2–4–1 was able to efficiently predict the styrene removal performance in the biofilter. Through sensitivity analysis, the most influential input parameter affecting styrene removal was ascertained to be the flow rate

Application of artificial neural networks for the prediction of Gaza wastewater treatment plant performance-Gaza strip

This paper is concerned with the use of artificial neural network and multiple linear regression (MLR) models for the prediction of three major water quality parameters in the Gaza wastewater treatment plant. The data sets used in this study consist of nine years and collected from Gaza wastewater treatment plant during monthly records. Treatment efficiency of the plant was determined by taking into account of influent input values of pH, temperature (T), biological oxygen demand (BOD), chemical oxygen demand (COD) and total dissolved solids (TSS) with effluent output values of BOD, COD and TSS. Performance of the model was compared via the parameters of root mean squared error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r). The suitable architecture of the neural network model is determined after several trial and error steps. Results showed that the artificial neural network (ANN) performance model was better than the MLR model. It was found that the ANN model could be employed successfully in estimating the BOD, COD and TSS in the outlet of Gaza wastewater treatment plant. Moreover, sensitive examination results showed that influent TSS and T parameters have more effect on BOD, COD and TSS predicting to other parameters

Deducing water parameters in rivers via statistical modelling

Advanced monitoring of water quality in order to perform a real-time hazard analysis prior to Water Treatment Works (WTW) is more of a necessity nowadays, both to give warning of any contamination and also to avoid downtime of the WTW. Downtimes could be a major contributor to risk. Any serious accident will cause a significant loss in customer and investor confidence. This has challenged the industry to become more efficient, integrated and attractive, with benefits for its workforce and society as a whole. The reality is that water companies are not yet prepared to invest heavily in trials, before another company announces its success in implementing a new monitoring strategy. This has slowed down the development of the water industry. This research has taken the theoretical idea that the use of advanced online monitoring technique in the water industry would be beneficial and a step further; demonstrating by means of a state-of-the-art assessment, usability trials, case studies and demonstration that the barriers to mainstream adoption can be overcome. The findings of this work have been presented in four peer-reviewed papers. The research undertaken has shown that Turbidity levels in rivers can be measured from the rivers’ mean flow rate, using either Doppler Ultrasound device for real-time readings or based on past performance history. In both cases, the Turbidity level can also help estimate both the Colour and Conductivity levels of the subject river. Recalibration of the equations used is a prerequisite as each individual river has its own unique “finger print”