A fuzzy logic based diagnosis system for the on-line supervision of an anaerobic digestor pilot-plant

A fuzzy-logic-based controller for methane production in anaerobic fixed-film reactors

The main objective of this work was to develop a controller for biogas production in continuous anaerobic fixed-bed reactors, which used effluent total volatile fatty acids (VFA) concentration as control input in order to prevent process acidification at closed loop. To this aim, a fuzzy-logic-based control system was developed, tuned and validated in an anaerobic fixed-bed reactor at pilot scale that treated industrial winery wastewater. The proposed controller varied the flow rate of wastewater entering the system as a function of the gaseous outflow rate of methane and VFA concentration. Simulation results show that the proposed controller is capable to achieve great process stability even when operating at high VFA concentrations. Pilot results showed the potential of this control approach to maintain the process working properly under similar conditions to the ones expected at full-scale plants.

A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage

A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures.

Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state

A conceptual neural fuzzy model based on adaptive-network based fuzzy inference system, ANFIS, was proposed using available input on-line and off-line operational variables for a sugar factory anaerobic wastewater treatment plant operating under unsteady state to estimate the effluent chemical oxygen demand, COD. The predictive power of the developed model was improved as a new approach by adding the phase vector and the recent values of COD up to 5-10 days, longer than overall retention time of wastewater in the system. History of last 10 days for COD effluent with two-valued phase vector in the input variable matrix including all parameters had more predictive power. History of 7 days with two-valued phase vector in the matrix comprised of only on-line variables yielded fairly well estimations. The developed ANFIS model with phase vector and history extension has been able to adequately represent the behavior of the treatment system.

Optimisation of the anaerobic digestion of agricultural resources

It is in the interest of operators of anaerobic digestion plants to maximise methane production whilst concomitantly reducing the chemical oxygen demand of the digested material. Although the production of biogas through anaerobic digestion is not a new idea, commercial anaerobic digestion processes are often operated at well below their optimal performance due to a variety of factors. This paper reviews current optimisation techniques associated with anaerobic digestion and suggests possible areas where improvements could be made, including the basic design considerations of a single or multi-stage reactor configuration, the type, power and duration of the mixing regime and the retention of active microbial biomass within the reactor. Optimisation of environmental conditions within the digester such as temperature, pH, buffering capacity and fatty acid concentrations is also discussed. The methane-producing potential of various agriculturally sourced feedstocks has been examined, as has the advantages of co-digestion to improve carbon-to-nitrogen ratios and the use of pre-treatments and additives to improve hydrolysis rates or supplement essential nutrients which may be limiting. However, perhaps the greatest shortfall in biogas production is the lack of reliable sensory equipment to monitor key parameters and suitable, parallelised control systems to ensure that the process continually operates at optimal performance. Modern techniques such as software sensors and powerful, flexible controllers are capable of solving these problems. A direct comparison can be made here with, for instance, oil refineries where a more mature technology uses continuous in situ monitoring and associated feedback procedures to routinely deliver continuous, optimal performance.

Monitoring and control of anaerobic reactors

The current status in monitoring and control of anaerobic reactors is reviewed. The influence of reactor design and waste composition on the possible monitoring and control schemes is examined. After defining the overall control structure, and possible control objectives, the possible process measurements are reviewed in detail. In the sequel, possible manipulated variables, such as the hydraulic retention time, the organic loading rate, the sludge retention time, temperature, pH and alkalinity are evaluated with respect to the two main reactor types: high-rate and low-rate. Finally, the different control approaches that have been used are comprehensively described. These include simple and adaptive controllers, as well as more recent developments such as fuzzy controllers, knowledge-based controllers and controllers based on neural networks.