Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.     

A probabilistic framework for comparison of dam breach parameters and outflow hydrograph generated by different empirical prediction methods

This study presents a probabilistic framework to simulate dam breach and evaluates the impact of using four empirical dam breach prediction methods on breach parameters (i.e., geometry and timing) and outflow hydrograph attributes (i.e., time to peak, hydrograph duration and peak). The methods that are assessed here include MacDonald and Langridge-Monopolis (1984), Von Thun and Gillette (1990), Froehlich (1995), 2008). Mean values and percentiles of breach parameters and outflow hydrograph attributes are compared for hypothetical overtopping failure of Burnett Dam in the state of North Carolina, USA. Furthermore, utilizing the probabilistic framework, the least and most uncertain methods alongside those giving the most critical value are identified for these parameters. The multivariate analysis also indicates that lone use of breach parameters is not necessarily sufficient to characterize outflow hydrograph attributes. However, timing characteristic of the breach is generally a more important driver than its geometric features.     

Energy and nutrient recovery for municipal wastewater treatment: How to design a feasible plant layout?

Activated sludge systems are commonly used for robust and efficient treatment of municipal wastewater. However, these systems cannot achieve their maximum potential to recover valuable resources from wastewater. This study demonstrates a procedure to design a feasible novel configuration for maximizing energy and nutrient recovery. A simulation model was developed based on literature data and recent experimental research using steady-state energy and mass balances with conversions. The analysis showed that in the Netherlands, proposed configuration consists of four technologies: bioflocculation, cold partial nitritation/Anammox, P recovery, and anaerobic digestion. Results indicate the possibility to increase net energy yield up to 0.24 kWh/m³ of wastewater, while reducing carbon emissions by 35%. Moreover, sensitivity analysis points out the dominant influence of wastewater organic matter on energy production and consumption. This study provides a good starting point for the design of promising layouts that will improve sustainability of municipal wastewater management in the future.     

A mathematical programming framework for early stage design of wastewater treatment plants

The increasing number of alternative wastewater treatment technologies and stricter effluent requirements make the optimal treatment process selection for wastewater treatment plant design a complicated problem. This task, defined as wastewater treatment process synthesis, is currently based on expert decisions and previous experiences. This paper proposes a new approach based on mathematical programming to manage the complexity of the problem. The approach generates/identifies novel and optimal wastewater treatment process selection, and the interconnection between unit operations to create a process flow diagram. Towards this end, a superstructure approach is used to represent the treatment alternatives for reaction and separation. A generic process interval model is used to describe each alternative in terms of input–output mass balances including conversion and separation factors. Next the design problem is formulated as a Mixed Integer (Non)linear Programming problem – MI(N)LP – and solved. A case study is formulated and solved to highlight the application of the framework.     

Advanced decision control system for effluent violations removal in wastewater treatment plants

This paper presents the application of control strategies for wastewater treatment plants with the goal of effluent limits violations removal as well as achieving a simultaneous improvement of effluent quality and reduction of operational costs. The evaluation is carried out with the Benchmark Simulation Model No. 2. The automatic selection of the suitable control strategy is based on risk detection of effluent violations by Artificial Neural Networks. Fuzzy Controllers are implemented to improve the denitrification or nitrification process based on the proposed objectives. Model Predictive Control is applied for the improvement of dissolved oxygen tracking.     

Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method

In this study, a hybrid sequential data assimilation and probabilistic collocation (HSDAPC) approach is proposed for analyzing uncertainty propagation and parameter sensitivity of hydrologic models. In HSDAPC, the posterior probability distributions of model parameters are first estimated through a particle filter method based on streamflow discharge data. A probabilistic collocation method (PCM) is further employed to show uncertainty propagation from model parameters to model outputs. The temporal dynamics of parameter sensitivities are then generated based on the polynomial chaos expansion (PCE) generated by PCM, which can reveal the dominant model components for different catchment conditions. The maximal information coefficient (MIC) is finally employed to characterize the correlation/association between model parameter sensitivity and catchment precipitation, potential evapotranspiration and observed discharge. The proposed method is applied to the Xiangxi River located in the Three Gorges Reservoir area. The results show that: (i) the proposed HSDAPC approach can generate effective 2nd and 3rd PCE models which provide accuracy predictions; (ii) 2^nd-order PCE, which can run nearly ten time faster than the hydrologic model, can capably represent the original hydrological model to show the uncertainty propagation in a hydrologic simulation; (iii) the slow (R_s) and quick flows (R_q) in Hymod show significant sensitivities during the simulation periods but the distribution factor (α) shows a least sensitivity to model performance; (iv) the model parameter sensitivities show significant correlation with the catchment hydro-meteorological conditions, especially during the rainy period with MIC values larger than 0.5. Overall, the results in this paper indicate that uncertainty propagation and temporal sensitivities of parameters can be effectively characterized through the proposed HSDAPC approach.     

A framework for propagation of uncertainty contributed by parameterization,input data,model structure,and calibration/validation data in watershed modeling

Failure to consider major sources of uncertainty may bias model predictions in simulating watershed behavior. A framework entitled the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT), was developed utilizing Bayesian inferences, an input error model and modified goodness-of-fit statistics to incorporate uncertainty in parameter, model structure, input data, and calibration/validation data in watershed modeling. Applications of the framework at the Eagle Creek Watershed in Indiana shows that watershed behavior was more realistically represented when the four uncertainty sources were considered jointly without having to embed watershed behavior constraints in auto-calibration. Accounting for the major sources of uncertainty associated with watershed modeling produces more realistic predictions, improves the quality of calibrated solutions, and consequently reduces predictive uncertainty. IPEAT is an innovative tool to investigate and explore the significance of uncertainty sources, which enhances watershed modeling by improved characterization and assessment of predictive uncertainty.     

Ensemble model of wastewater treatment plant based on rich diversity of principal component determining by genetic algorithm for status monitoring

Wastewater treatment plants (WWTPs) is a complex process, effective process monitoring can make it stable and prevent the destruction of the ecological environment. Principal component analysis (PCA) has been widely used in process monitoring. However, most PCA-based methods construct a single PCA model using several principal components (PCs), causing loss of information on some faults and less generalization ability of the PCA model. Thus, this study proposed a novel ensemble process monitoring method based on genetic algorithm (GA) for selective diversity of PCs. GA is used to determine a set of principal component subspaces with the greatest diversity as the base models. Bayesian inference is adopted to combine the results of base models into a probability index. Cases study on TE benchmark process and an actual WWTP show the excellent performance of the proposed method compared with several PCA-based methods and the strong generalization ability of the ensemble model.