31To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes 32 require advanced instrumentation and control tools. Over the years, research on these topics has evolved and 33 followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid 34-mainly industrial -then municipal organic fraction of solid waste and agricultural residues. Time constants of 35 the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. 36Since fast closed loop control is needed for short time constant processes, human operator is now included in the

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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.