An advanced simulation model for membrane bioreactors: development, calibration and validation.

Membrane wastewater treatment plants (WWTPs) have several advantages compared with conventionally designed WWTPs with classical purification techniques. The filtration process is the key to their commercial success in Germany with respect to energy consumption and effectiveness, enabled by the optimization of filtration using a dynamic simulation model. This work is focused on the development of a robust, flexible and practically applicable membrane simulation model for submerged hollow-fibre and flat-sheet membrane modules. The model is based on standard parameters usually measured on membrane WWTPs. The performance of the model is demonstrated by successful calibration and validation for three different full-scale membrane WWTPs achieving good results. Furthermore, the model is combinable with Activated Sludge Models.

State estimation for anaerobic digesters using the ADM1.

The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.

Simulation and optimization of an experimental membrane wastewater treatment plant using computational intelligence methods.

The optimization of relaxation and filtration times of submerged microfiltration flat modules in membrane bioreactors used for municipal wastewater treatment is essential for efficient plant operation. However, the optimization and control of such plants and their filtration processes is a challenging problem due to the underlying highly nonlinear and complex processes. This paper presents the use of genetic algorithms for this optimization problem in conjunction with a fully calibrated simulation model, as computational intelligence methods are perfectly suited to the nonconvex multi-objective nature of the optimization problems posed by these complex systems. The simulation model is developed and calibrated using membrane modules from the wastewater simulation software GPS-X based on the Activated Sludge Model No.1 (ASM1). Simulation results have been validated at a technical reference plant. They clearly show that filtration process costs for cleaning and energy can be reduced significantly by intelligent process optimization.

Computational intelligence-based optimisation of wastewater treatment plants.

Methods of computational intelligence (CI), especially fuzzy control and neuronal networks, are used for controlling and optimising of wastewater treatment plants. Areas of application are the control of sludge water dosage, of phosphate elimination by optimal precipitant dosage as well as an optimal aeration in the nitrification zone. In two municipal wastewater treatment plants with 60,000 and 12,600 person equivalents the controllers have been installed and optimised and they have been in operation for several years. Results of operation of the plants are presented in comparison to previously used classical control. Performance increased significantly and the outflow values could be kept securely below the government requirements without increase of the energy consumption. Peak loads in the inflow were eliminated in the plant and did not increase outflow concentrations. Results of operation for more than three years clearly show that the CI controller is a cost-efficient method for a sustainable rise of performance in municipal wastewater treatment plants.

Improving the efficiency of a wastewater treatment plant by fuzzy control and neural network.

One of the main problems in operating a wastewater treatment plant is the purification of the excess water from dewatering and pressing of sludge. Because of a high load of organic material and of nitrogen it has to be buffered and treated together with the inflowing wastewater. Different control strategies are discussed. A combination of neural network for predicting outflow values one hour in advance and fuzzy controller for dosing the sludge water are presented. This design allows the construction of a highly non-linear predictive controller adapted to the behaviour of the controlled system with a relatively simple and easy to optimise fuzzy controller. Measurement results of its operation on a municipal wastewater treatment plant of 60,000 inhabitant equivalents are presented and discussed. In several months of operation the system has proved very reliable and robust tool for improving the system's efficiency.