Optimizing anaerobic co-digestion plants: MIR online instrumentation and dynamic real-time substrate feed optimization

Abstract Closed-loop control of the substrate feed as well as the application of online instrumentation are important to achieve optimal biogas plant operation. Therefore, this paper presents two novel approaches for online instrumentation and control to achieve optimal AD plant operation based on middle-infrared spectroscopy on the one hand and nonlinear model predictive control on the other hand. At present, research into both techniques is being performed separately, with the intention that in the future the spectroscopic measurements will be integrated into the control loop.

[1]  Daniel Gaida,et al.  Dynamic real-time substrate feed optimization of anaerobic co-digestion plants , 2014 .

[2]  Éric Marchand,et al.  Real-time markerless tracking for augmented reality: the virtual visual servoing framework , 2006, IEEE Transactions on Visualization and Computer Graphics.

[3]  Iskander Usenov,et al.  Mid‐infrared Fiber Optics for 1 — 18 μm Range , 2014 .

[4]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[5]  D Gaida,et al.  State estimation for anaerobic digesters using the ADM1. , 2012, Water science and technology : a journal of the International Association on Water Pollution Research.

[6]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[7]  Nicola Beume,et al.  Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization , 2007, EMO.

[8]  Frank Allgöwer,et al.  State and Output Feedback Nonlinear Model Predictive Control: An Overview , 2003, Eur. J. Control.

[9]  Carlos A. Coello Coello,et al.  Evolutionary Multi-Objective Optimization: Basic Concepts and Some Applications in Pattern Recognition , 2011, MCPR.

[10]  Alessandro Astolfi,et al.  Set-point regulation of an anaerobic digestion process with bounded output feedback , 2003, IEEE Trans. Control. Syst. Technol..

[11]  Mikel Larrea,et al.  Intelligent Multi-Objective Nonlinear Model Predictive Control (iMO-NMPC): Towards the 'on-line' optimization of highly complex control problems , 2012, Expert Syst. Appl..