Membrane bioreactor fouling behaviour assessment through principal component analysis and fuzzy clustering.

Adequate membrane bioreactor operation requires frequent evaluation of the membrane state. A data-driven approach based on principal component analysis (PCA) and fuzzy clustering extracting the necessary monitoring information solely out of transmembrane pressure data was investigated for this purpose. Out of three tested PCA techniques the two functional methods proved useful to cope with noise and outliers as opposed to the common standard PCA, while all of them presented similar capabilities for revealing data trends and patterns.

A dynamical population modeling of invasive species with reference to the crayfish Procambarus clarkii.

In this paper we present a discrete dynamical population modeling of invasive species, with reference to the swamp crayfish Procambarus clarkii. Since this species can cause environmental damage of various kinds, it is necessary to evaluate its expected in not yet infested areas. A structured discrete model is built, taking into account all biological information we were able to find, including the environmental variability implemented by means of stochastic parameters (coefficients of fertility, death, etc.

Artificial intelligence control of a sequencing batch reactor for nitrogen removal via nitrite from landfill leachate.

Leachate generated in old landfills is a high-strength wastewater, which is particularly difficult to treat owing to its low biochemical oxygen demand/total Kjeldahl nitrogen ratio. This paper seeks to demonstrate that reliable leachate treatment by means of sequencing batch reactors (SBRs) is indeed possible by means of the application of a smart control system. This study assesses the results of a computer-controlled bench-scale SBR treating raw sanitary landfill leachate to achieve nitrogen removal through the nitrite shortcut.

Nitrogen removal via nitrite in a sequencing batch reactor treating sanitary landfill leachate.

The present paper reports the results of the application of a control system, based on artificial intelligence concepts, for the automation of a bench-scale SBR treating leachate generated in old landfills. Attention was given to the nitritation and denitritation processes in order to enhance the nitrogen removal efficiency. Nitrification and nitrogen removal were usually higher than 98% and 95%, respectively, whereas COD removal was approximately 20-30% due to the low biodegradability of organic matter in the leachate from old landfills; therefore, external COD was added to accomplish the denitrification process.

Reliability of parameter estimation in respirometric models.

When modelling a biochemical system, the fact that model parameters cannot be estimated exactly stimulates the definition of tests for checking unreliable estimates and design better experiments. The method applied in this paper is a further development from Marsili-Libelli et al. [2003.