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.

[1]  O Bernard,et al.  Advanced monitoring and control of anaerobic wastewater treatment plants: software sensors and controllers for an anaerobic digester. , 2001, Water science and technology : a journal of the International Association on Water Pollution Research.

[2]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[3]  André Stuhlsatz,et al.  Feature Extraction for Simple Classification , 2010, 2010 20th International Conference on Pattern Recognition.

[4]  Manfred Lübken,et al.  Monofermentation of grass silage under mesophilic conditions: measurements and mathematical modeling with ADM 1. , 2009, Bioresource technology.

[5]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[6]  André Stuhlsatz,et al.  Discriminative feature extraction with Deep Neural Networks , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[7]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[8]  K. Koch,et al.  Biogas from grass silage - Measurements and modeling with ADM1. , 2010, Bioresource technology.

[9]  S. Grimberg,et al.  Modeling anaerobic digestion of dairy manure using the IWA Anaerobic Digestion Model no. 1 (ADM1). , 2008, Water science and technology : a journal of the International Association on Water Pollution Research.

[10]  Thomas Bäck,et al.  Optimal Control of Biogas Plants Using Nonlinear MPC , 2011 .

[11]  Manfred Lübken,et al.  Modelling the energy balance of an anaerobic digester fed with cattle manure and renewable energy crops. , 2007, Water research.

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  K. Koch,et al.  Eignung des Anaerobic Digestion Model No. 1 (ADM1) zur Prozesssteuerung landwirtschaftlicher Biogasanlagen. , 2007 .

[14]  J Wiese,et al.  From a black-box to a glass-box system: the attempt towards a plant-wide automation concept for full-scale biogas plants. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[15]  Bernhard Wett,et al.  Population dynamics at digester overload conditions. , 2009, Bioresource technology.

[16]  James B. Rawlings,et al.  Particle filtering and moving horizon estimation , 2006, Comput. Chem. Eng..

[17]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[18]  M. Gerardi.  The Microbiology of Anaerobic Digesters , 2003 .