Anaerobic treatment of tequila vinasses under seasonal operating conditions: start-up, normal operation and restart-up after a long stop and starvation period.

This study examines the performance of an anaerobic fixed-film bioreactor under seasonal operating conditions prevailing in medium and small size Tequila factories: start-up, normal operation and particularly, during the restart-up after a long stop and starvation period. The proposed start-up procedure attained a stable biofilm in a rather short period (28 days) despite unbalanced COD/N/P ratio and the use of non-acclimated inoculum. The bioreactor was restarted-up after being shut down for 6 months during which the inoculum starved. Even when biofilm detachment and bioreactor clogging were detected at the very beginning of restart-up, results show that the bioreactor performed better as higher COD removal and methane yield were attained. CE-SSCP and Q-PCR analyses, conducted on the biofilm prokaryotic communities for each operating condition, confirmed that the high COD removal results after the bioreactor clogging and the severe starvation period were mainly due to the stable archaeal and resilient bacterial populations.

Neural network modeling of the light profile in a novel photobioreactor.

An artificial neural network (ANN) was implemented to model the light profile pattern inside a photobioreactor (PBR) that uses a toroidal light arrangement. The PBR uses Tequila vinasses as culture medium and purple non-sulfur bacteria Rhodopseudomonas palustris as biocatalyzer. The performance of the ANN was tested for a number of conditions and compared to those obtained by using deterministic models. Both ANN and deterministic models were validated experimentally. In all cases, at low biomass concentration, model predictions yielded determination coefficients greater than 0.9. Nevertheless, ANN yielded the more accurate predictions of the light pattern, at both low and high biomass concentration, when the bioreactor radius, the depth, the rotational speed of the stirrer and the biomass concentration were incorporated in the ANN structure. In comparison, most of the deterministic models failed to correlate the empirical data at high biomass concentration. These results show the usefulness of ANNs in the modeling of the light profile pattern in photobioreactors.

Regulation of the organic pollution level in anaerobic digesters by using off-line COD measurements.

A sampled delayed scheme is proposed to regulate the organic pollution level in anaerobic digestion processes by using off-line COD measurements. The proposed scheme is obtained by combining an error feedback control with a steady state estimator to track constant references and attenuate process load disturbances. The controller performance is tested experimentally for the treatment of tequila vinasses over a period of 68days under different set-point values and several uncertain scenarios which include badly known kinetic parameters and load disturbances. Experimental results show that the COD concentration can be effectively regulated under the influence of set-point changes and high load disturbances by using only a daily off-line COD measurement, which makes the industrial application of the proposed control scheme feasible.

Observer-based input estimation in continuous anaerobic wastewater treatment processes.

In this paper a "virtually controlled observer" (VCO) is proposed to estimate simultaneously the influent substrate concentration and unmeasured state variables in continuous anaerobic digestion processes. The hypothetical (unmeasured) influent substrate concentration is updated by a feedback control, which is regulated by the estimation error of a measured output. The proposed approach is firstly illustrated upon a simple continuous bioprocess. Then, the performance of the observer is tested via numerical simulations in a continuous anaerobic digestion model. Results showed that it is possible to estimate both, the influent substrate concentration and unmeasured states in the digester in the face of parametric uncertainties.

Kinetic parameters estimation in an anaerobic digestion process using successive quadratic programming.

In this work, an optimization method is implemented in an anaerobic digestion model to estimate its kinetic parameters and yield coefficients. This method combines the use of advanced state estimation schemes and powerful nonlinear programming techniques to yield fast and accurate estimates of the aforementioned parameters. In this method, we first implement an asymptotic observer to provide estimates of the non-measured variables (such as biomass concentration) and good guesses for the initial conditions of the parameter estimation algorithm. These results are then used by the successive quadratic programming (SQP) technique to calculate the kinetic parameters and yield coefficients of the anaerobic digestion process. The model, provided with the estimated parameters, is tested with experimental data from a pilot-scale fixed bed reactor treating raw industrial wine distillery wastewater. It is shown that SQP reaches a fast and accurate estimation of the kinetic parameters despite highly noise corrupted experimental data and time varying inputs variables. A statistical analysis is also performed to validate the combined estimation method. Finally, a comparison between the proposed method and the traditional Marquardt technique shows that both yield similar results; however, the calculation time of the traditional technique is considerable higher than that of the proposed method.

Robust interval-based regulation for anaerobic digestion processes.

A robust regulation law is applied to the stabilization of a class of biochemical reactors exhibiting partially known highly nonlinear dynamic behavior. An uncertain environment with the presence of unknown inputs is considered. Based on some structural and operational conditions, this regulation law is shown to exponentially stabilize the aforementioned bioreactors around a desired set-point. This approach is experimentally applied and validated on a pilot-scale (1 m3) anaerobic digestion process for the treatment of raw industrial wine distillery wastewater where the objective is the regulation of the chemical oxygen demand (COD) by using the dilution rate as the manipulated variable. Despite large disturbances on the input COD and state and parametric uncertainties, this regulation law gave excellent performances leading the output COD towards its set-point and keeping it inside a pre-specified interval.

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.

Software sensors for highly uncertain WWTPs: a new approach based on interval observers.

This paper presents the practical implementation of a new robust interval observer on a 1 m3 continuous fixed bed anaerobic reactor used for the treatment of industrial wine distillery wastewater. This interval observer is able to generate guaranteed intervals for the unmeasured variables (i.e. acidogenic and methanogenic bacteria, alkalinity and chemical oxygen demand) from few on-line measurements (i.e. input liquid flow rate, CO2 gaseous flow rate, volatile fatty acids and total inorganic carbon). The main advantage of this approach is its independance with respect to disturbances and uncertainty in the initial conditions, in the kinetics and, last but not least, in the process inputs.