In response to strong growth in energy intensive wastewater treatment, public agencies and industry began to explore and implement measures to ensure achievement of the targets indicated in the 2020 Climate and Energy Package. However, in the absence of fundamental and globally recognized approach evaluating wastewater treatment plant (WWTP) energy performance, these policies could be economically wasteful.This paper gives an overview of the literature of WWTP energy-use performance and of the state of the art methods for energy benchmarking. The literature review revealed three main benchmarking approaches: normalization, statistical techniques and programming techniques, and advantages and disadvantages were identified for each one. While these methods can be used for comparison, the diagnosis of the energy performance remains an unsolved issue. Besides, a large dataset of WWTP energy consumption data, together with the methods for synthesizing the information, are presented and discussed. It was found that no single key performance indicators (KPIs) used to characterize the energy performance could be used universally. The assessment of a large data sample provided some evidence about the effect of the plant size, dilution factor and flowrate. The technology choice, plant layout and country of location were seen as important elements that contributed to the large variability observed.

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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 controi 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 N0.1. 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.

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