Instrumentation and control of anaerobic digestion processes: a review and some research challenges

The final publication is available at Springer via http://dx.doi.org/10.1007/s11157-015-9382-6[EN] To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid mainly industrial then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables measured online to the prediction of overall process perfor- mance based on global off-line measurements to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates)andtheirbioaccessibility andbiodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.The authors acknowledge the financial support of INRA (the French National Institute for Agricultural Research), the French National Research Agency (ANR) for the "Phycover" project (project ANR-14-CE04-0011) and ADEME for Inter-laboratory assay financial support.Jimenez, J.; Latrille, E.; Harmand, J.; Robles Martínez, Á.; Ferrer Polo, J.; Gaida, D.; Wolf, C.... (2015). Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Reviews in Environmental Science and Biotechnology. 14(4):615-648. doi:10.1007/s11157-015-9382-6S615648144Aceves-Lara CA, Latrille E, Steyer JP (2010) Optimal control of hydrogen production in a continuous anaerobic fermentation bioreactor. 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Global Asymptotic Stability of a Functional Differential Model with Time Delay of an Anaerobic Biodegradation Process

We study a nonlinear functional differential model of an anaerobic digestion process of wastewater treatment with biogas production. The model equations of biomass include two different discrete time delays. A mathematical analysis of the model is completed including existence and local stability of nontrivial equilibrium points, existence and boundedness of the model solutions as well as global stabilizability towards an admissible equilibrium point. We propose and apply a numerical extremum seeking algorithm for maximizing the biogas flow rate in real time. Numerical simulation results are also included. ACM Computing Classification System (1998): D.2.6, G.1.10, J.2

On-line estimation of VFA concentration in anaerobic digestion via methane outflow rate measurements

"This paper deals with the design of a robust nonlinear observer as a software sensor to achieve the on-line estimation of the concentration of Volatile Fatty Acids (VFA) in a class of continuous anaerobic digesters (AD). Taking into account the limited availability of on-line sensors for AD process, in this contribution is assumed that only the methane outflow rate is available for on-line measurement. The estimation method is based on a modified version for a two-dimensional mathematical model of AD process. From the differential algebraic observability approach it is shown that the VFA concentration is detectable from the methane outflow rate measurements. The observer convergence is analyzed by using Lyapunov stability techniques. Numerical simulations illustrate the effectiveness of the proposed estimation method for a four-dimensional AD model with uncertainties associated with unmodeled dynamics and disturbances in the inflow composition.

Assessment and parameter identification of simplified models to describe the kinetics of semi continuous biomethane production from anaerobic digestion of green and food waste

Biochemical reactions occurring during anaerobic digestion have been modelled using reaction kinetic equations such as first-order, Contois and Monod which are then combined to form mechanistic models. This work considers models which include between one and three biochemical reactions to investigate if the choice of the reaction rate equation, complexity of the model structure as well as the inclusion of inhibition plays a key role in the ability of the model to describe the methane production from the semi-continuous anaerobic digestion of green waste (GW) and food waste (FW). A parameter estimation method was used to investigate the most important phenomena influencing the biogas production process. Experimental data were used to numerically estimate the model parameters and the quality of fit was quantified. Results obtained reveal that the model structure (i.e. number of reactions, inhibition) has a much stronger influence on the quality of fit compared with the choice of kinetic rate equations. In the case of GW there was only a marginal improvement when moving from a one to two reaction model, and none with inclusion of inhibition or three reactions. However, the behaviour of FW digestion was more complex and required either a two or three reaction model with inhibition functions for both ammonia and volatile fatty acids. Parameter values for the best fitting models are given for use by other authors

Real-Time Substrate Feed Optimization of Anaerobic Co-Digestion Plants

In anaerobic co-digestion plants a mix of organic materials is converted to biogas using the anaerobic digestion process. These organic materials, called substrates, can be crops, sludge, manure, organic wastes and many more. They are fed on a daily basis and significantly affect the biogas production process. In this thesis dynamic real-time optimization of the substrate feed for anaerobic co-digestion plants is developed. In dynamic real-time optimization a dynamic simulation model is used to predict the future performance of the controlled plant. Therefore, a complex simulation model for biogas plants is developed, which uses the famous Anaerobic Digestion Model No. 1 (ADM1). With this model the future economics as well as stability can be calculated resulting in a multi-objective performance criterion. Using multi-objective nonlinear model predictive control (NMPC) the model predictions are used to find the optimal substrate feed for the biogas plant. Therefore, NMPC solves an optimization problem over a moving horizon and applies the optimal substrate feed to the plant for a short while before recalculating the new optimal solution. The multi-objective optimization problem is solved using state-of-the-art methods such as SMS-EMOA and SMS-EGO. The performance of the proposed approach is validated in a detailed simulation studyAlgorithms and the Foundations of Software technolog

Modelling, Optimisation and Control of Anaerobic Co-digestion Processes

La digestión anaerobia es un proceso biológico que ocurre espontáneamente en la naturaleza. Sin embargo, el rendimiento de metanización varía mucho dependiendo del tipo de residuo y condiciones ambientales a las que los residuos están expuestos. La tesis “Modelling, Optimisation and Control of Anaerobic Co-digestion Processes” contribuye a la modelización, optimización y control de procesos de co-digestión con el objetivo de mejorar el rendimiento del proceso. Los fundamentos de la digestión anaerobia y co-digestión se presentan en el Capítulo 1, junto con una revisión bibliográfica sobre la modelización del proceso, centrándose principalmente en la descripción y aplicaciones del Anaerobic Digestion Model No. 1 (ADM1), y una revisión de las distintas estrategias de control que están disponibles en la actualidad. El Capítulo 2 describe detalladamente la planta piloto que se utilizó para realizar los ensayos experimentales del trabajo de investigación. En el Capítulo 3 se desarrolla y valida un método generalizado para incorporar diversos sustratos solubles fermentables en un modelo basado en ADM1. Las reacciones de fermentación de sustratos tales como el etanol, no incluidos originalmente en ADM1, se implementan como reacciones de fermentación equivalente de glucosa. Suponiendo que la acidogénesis es el paso más rápido en la digestión anaerobia, una descripción exacta de la estequiometría de la fermentación de sustratos solubles (etanol, glicerol...) y productos (acetato, butirato y propionato) no es necesaria siempre que se cumplan los balances de masa y de electrones, puesto que todos estos ácidos intermedios se convierten rápidamente en acetato, H2 y CO2 en sistemas metanogénicos. El tratamiento de residuos sólidos mediante digestión anaerobia es atractivo por su alto contenido en materia orgánica y al potencial de recuperación de energía. En el caso de sólidos, la etapa de desintegración-hidrólisis es el paso más lento del proceso. El Capítulo 4 presenta un nuevo enfoque para la modelización de las etapas de desintegración e hidrólisis de sustratos sólidos complejos. Éstos se suponen que están compuestos de una fracción fácilmente biodegradable y otra lentamente biodegradable. El modelo propuesto considera una desintegración desacoplada de estas dos fracciones para describir mejor la degradación de los residuos sólidos. La co-digestión puede mejorar el rendimiento de las plantas de biogás en términos de productividad de metano y estabilidad de la operación si se combinan adecuadamente los diferentes co-sustratos. El Capítulo 5 formula y valida un método de optimización basado en programación lineal que calcula la mejor mezcla de alimentación para sistemas de co-digestión, capaz de maximizar la producción de metano a cada velocidad de carga orgánica aplicada. La mezcla resultante está sujeta a un conjunto de restricciones fisicoquímicas, que se definen en base al conocimiento heurístico del proceso. Finalmente, el Capítulo 6 presenta una estrategia de control para co-digestión anaerobia. La mezcla óptima obtenida por programación lineal se alimenta a un digestor operando en continuo y un sistema de diagnosis evalúa el rendimiento del proceso. En función de los resultados de la diagnosis, la acción de control modifica las restricciones aplicadas en el cálculo de la alimentación. Esta acción de control permite calcular una nueva mezcla de sustratos y un nuevo TRH para el próximo período de operación. Como resultado, la estrategia funciona como un controlador en lazo cerrado que optimiza la mezcla de alimentación al digestor y posteriormente evalúa el rendimiento de operación con la mezcla alimentada

Sewage Treatment Plants

Sewage Treatment Plants: Economic Evaluation of Innovative Technologies for Energy Efficiency aims to show how cost saving can be achieved in sewage treatment plants through implementation of novel, energy efficient technologies or modification of the conventional, energy demanding treatment facilities towards the concept of energy streamlining. The book brings together knowledge from Engineering, Economics, Utility Management and Practice and helps to provide a better understanding of the real economic value with methodologies and practices about innovative energy technologies and policies in sewage treatment plants

Simulation, optimization and instrumentation of agricultural biogas plants

During the last two decades, the production of renewable energy by anaerobic digestion (AD) in biogas plants has become increasingly popular due to its applicability to a great variety of organic material from energy crops and animal waste to the organic fraction of Municipal Solid Waste (MSW), and to the relative simplicity of AD plant designs. Thus, a whole new biogas market emerged in Europe, which is strongly supported by European and national funding and remuneration schemes. Nevertheless, stable and efficient operation and control of biogas plants can be challenging, due to the high complexity of the biochemical AD process, varying substrate quality and a lack of reliable online instrumentation. In addition, governmental support for biogas plants will decrease in the long run and the substrate market will become highly competitive. The principal aim of the research presented in this thesis is to achieve a substantial improvement in the operation of biogas plants. At first, a methodology for substrate inflow optimization of full-scale biogas plants is developed based on commonly measured process variables and using dynamic simulation models as well as computational intelligence (CI) methods. This methodology which is appliquable to a broad range of different biogas plants is then followed by an evaluation of existing online instrumentation for biogas plants and the development of a novel UV/vis spectroscopic online measurement system for volatile fatty acids. This new measurement system, which uses powerful machine learning techniques, provides a substantial improvement in online process monitoring for biogas plants. The methodologies developed and results achieved in the areas of simulation and optimization were validated at a full-scale agricultural biogas plant showing that global optimization of the substrate inflow based on dynamic simulation models is able to improve the yearly profit of a biogas plant by up to 70%. Furthermore, the validation of the newly developed online measurement for VFA concentration at an industrial biogas plant showed that a measurement accuracy of 88% is possible using UV/vis spectroscopic probes