Combining pH and electrical conductivity measurements to improve titrimetric methods to determine ammonia nitrogen, volatile fatty acids and inorganic carbon concentrations.

Volatile fatty acids (VFA), inorganic carbon (IC) and total ammonia nitrogen (TAN) are key variables in the current context of anaerobic digestion (AD). Accurate measurements like gas chromatography and infrared spectrometry have been developed to follow the concentration of these compounds but none of these methods are affordable for small AD units. Only titration methods answer the need for small plant monitoring. The existing methods accuracy was assessed in this study and reveals a lack of accuracy and robustness to control AD plants. To solve these issues, a new titrimetric device to estimate the VFA, IC and TAN concentrations with an improved accuracy was developed. This device named SNAC (System of titration for total ammonia Nitrogen, volatile fatty Acids and inorganic Carbon) has been developed combining the measurement of electrical conductivity and pH. SNAC were tested on 24 different plant samples in a range of 0-0.16 mol.L(-1) TAN, 0.01-0.21 mol.L(-1) IC and 0-0.04 mol.L(-1) VFA. The standard error was about 0.012 mol.L(-1) TAN, 0.015 mol.L(-1) IC and 0.003 mol.L(-1) VFA. The coefficient of determination R(2) between the estimated and reference data was 0.95, 0.94 and 0.95 for TAN, IC and VFA respectively. Using the same data, current methods based on key pH points lead to standard error more than 14.5 times higher on VFA and more than 1.2 times higher on IC. These results show that SNAC is an accurate tool to improve the management of AD plant.

On-line supervision and control of an aerobic SBR process.

This paper presents a new software developed in MATLAB for analyzing on-line data of an aerobic SBR, detecting faults and, in this case, proposing the most probable causes of fault. Process diagnosis is achieved using a statistical method divided in two main phases: off-line model building and on-line data diagnosis. The off-line model identifies the correct working conditions of the system (standard operative conditions). It includes the characterization of the deviation of the system from these standard conditions in the case of changing in the biomass properties or carbon and nitrogen load characteristics. The on-line diagnosis aims at collecting and analyzing all the available data available through industrial sensors, and at classifying the behavior of each treatment cycle. The diagnosis performance of the proposed method is tested using a data set of an aerobic SBR pilot plant.

Modular expert system for the diagnosis of operating conditions of industrial anaerobic digestion plants.

Anaerobic digestion (AD) plants are highly efficient wastewater treatment processes with possible energetic valorisation. Despite these advantages, many industries are still reluctant to use them because of their instability in the face of changes in operating conditions. To the face this drawback and to enhance the industrial use of anaerobic digestion, one solution is to develop and to implement knowledge base (KB) systems that are able to detect and to assess in real-time the quality of operating conditions of the processes. Case-based techniques and heuristic approaches have been already tested and validated on AD processes but two major properties were lacking: modularity of the system (the knowledge base system should be easily tuned on a new process and should still work if one or more sensors are added or removed) and uncertainty management (the assessment of the KB system should remain relevant even in the case of too poor or conflicting information sources). This paper addresses these two points and presents a modular KB system where an uncertain reasoning formalism is used to combine partial and complementary fuzzy diagnosis modules. Demonstration of the interest of the approach is provided from real-life experiments performed on an industrial 2,000 m3 CSTR anaerobic digester.

An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet.

The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.

Sensors network diagnosis in anaerobic digestion processes using evidence theory.

Instrumentation defines a sensors network on a process. Hardware sensors indeed allow one to get different information sources that can be often cross-checked to provide reliable data. However, each of these sources of information contains some uncertainties, either due to the hardware sensors' measurement principles, to their possible fouling, to the estimated parameters of the models used in software sensors and/or to the specific structures of the software sensors. This paper demonstrates that, in this context, the evidence theory is a very well suited formalism for fault detection and diagnosis. This theory indeed allows one to take into account the exact knowledge supported by each source of information and to combine them in order to detect the occurring faults. Moreover, this combination guarantees the best fault isolability from a practical point of view and is suitable for multiple faults occurring at the same time. Finally, the evidence theory is a highly modular formalism since new information sources can be very easily added and old ones can be removed. Validation is performed using real-life experiments from a 1 m3 anaerobic digestion fixed bed process used applied to the treatment of winery wastewaters.