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This paper deals with the optimization of a two-phase anaerobic process treating biowaste for hydrogen and methane production. Neither physical nor chemical pre-treatments were used to optimize the process. The work was carried out at pilot scale, using two CSTRs (200 and 380 L working volume respectively) both maintained at thermophilic temperature (55 C) and fed semi-continuously with biowaste. The experiment was divided into three periods; during the first two periods the organic loading rate was maintained at 20 kg TVS/m3 d and the hydraulic retention time was changed from 6.6 to 3.3 days, while in the last period the digestate of the second reactor was recirculated to the first reactor in order to buffer the system and control pH at levels around 5. The HRT was maintained at 3.3 days and the OLR was decreased at 16.5 kg TVS/m3 d. The best yield was obtained in the last period where a specific hydrogen production of 50.9 L/kg VSfed was reached, with a H2 content in biogas from the first reactor of 36%. The methanogenic stage after the hydrogen conversion reached a specific biogas production of 0.62 m3/kg VSfed and an overall organic removal above 70%, without any stability problem. The overall biogas production was some 1.5 m3 per day with a gas composition of 10% H2 and 50% CH4.

Considerable concern exists regarding the appearance and effects of trace and ultra trace pollutants in the aquatic environment. In this context, it is necessary to identify relevant hot spot wastewater - such as hospital wastewater - and to implement specific wastewater treatment solutions. Membrane bioreactor (MBR) technology seems to be a suitable pre-treatment approach for the subsequent advanced treatment by high pressure membrane systems such as nanofiltration (NF) and reverse osmosis (RO). This paper is based upon investigations on the first full scale MBR for separate treatment of hospital wastewater in Germany. In this study an NF as well as an RO module for further treatment of the MBR filtrate were tested. The removal efficiencies were assessed using the following target compounds: bezafibrate, bisoprolol, carbamazepine, clarithromycin, ciprofloxacin, diclofenac, ibuprofen, metronidazole, moxifloxacin, telmisartan and tramadol. In summary, the results of this study confirmed that MBR technology followed by an advanced treatment for trace pollutant removal is an adequate approach for specific treatment of hot spot wastewater such as hospital wastewater. In particular, it was shown that - comparing the tested NF and RO - only (a two stage) RO is appropriate to remove pharmaceutical residues from hospital wastewater entirely. The recommended yield of the 2-stage RO is 70% which results in a retentate sidestream of 9%. Our investigations proved that RO is a very efficient treatment approach for elimination of trace pollutants.

Environmental monitoring of biological wastewater treatment plants (BWWTP) treating industrial effluents produces large amount of data. Frequent sampling is done in the influent and effluent but also in intermediate points. Samples are analyzed for classical and specific contaminants and physical-chemical parameters are monitored. In this paper data from a BWWTP treating the effluents of a coke and steel-processing factory are analyzed. Due to a complex situation, this BWWTP gave poor performances that did not match environmental regulations, meanwhile upgrading proved to be uneasy. Data analysis using principal component analyses (PCA) or kinetic modeling with a Haldane model was unsuccessful in handling these data, which was attributed to undetermined toxic effects. A new methodology is reported, that allowed to identify a kinetics for thiocyanate degradation and a relation between pH and toxic effects. This analysis of the plant data allowed to make hypothesis on the process control parameters and to recommend management modifications, allowing a further increase of the performances.

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.