This paper proposes a systematic methodology for the analysis of the mass and charge balances in dynamic models expressed using the Petersen matrix notation. This methodology is based on the definition of the model components via elemental mass fractions and in the estimation of the COD as a function of the redox equations associated with these elements. This approach makes the automatic calculation of all the stoichiometric coefficients under different measuring units and the study of COD, charge or mass fluxes easier. As an example of its application this methodology was applied to the ADM1 in order to illustrate its usefulness for the analysis of organic matter characterisation, nitrogen release or biogas composition in anaerobic digestion. The application of the methodology for a rigorous integration of different IWA models is proposed for further study.

Although membrane bioreactors (MBRs) technology is still a growing sector, its progressive implementation all over the world, together with great technical achievements, has allowed it to reach a mature degree, just comparable to other more conventional wastewater treatment technologies. With current energy requirements around 0.6–1.1 kWh/m³ of treated wastewater and investment costs similar to conventional treatment plants, main market niche for MBRs can be areas with very high restrictive discharge limits, where treatment plants have to be compact or where water reuse is necessary. Operational costs are higher than for conventional treatments; consequently there is still a need and possibilities for energy saving and optimisation. This paper presents the development of a knowledge-based decision support system (DSS) for the integrated operation and remote control of the biological and physical (filtration and backwashing or relaxation) processes in MBRs. The core of the DSS is a knowledge-based control module for air-scour consumption automation and energy consumption minimisation.

MBR technology is currently challenging traditional wastewater treatment systems and is increasingly selected for WWTP upgrading. MBR systems typically are constructed on a smaller footprint, and provide superior treated water quality. However, the main drawback of MBR technology is that the permeability of membranes declines during filtration due to membrane fouling, which for a large part causes the high aeration requirements of an MBR to counteract this fouling phenomenon. Due to the complex and still unknown mechanisms of membrane fouling it is neither possible to describe clearly its development by means of a deterministic model, nor to control it with a purely mathematical law. Consequently the majority of MBR applications are controlled in an “open-loop” way i.e. with predefined and fixed air scour and filtration/relaxation or backwashing cycles, and scheduled inline or offline chemical cleaning as a preventive measure, without taking into account the real needs of membrane cleaning based on its filtration performance. However, existing theoretical and empirical knowledge about potential cause-effect relations between a number of factors (influent characteristics, biomass characteristics and operational conditions) and MBR operation can be used to build a knowledge-based decision support system (KB-DSS) for the automatic control of MBRs. This KB-DSS contains a knowledge-based control module, which, based on real time comparison of the current permeability trend with “reference trends”, aims at optimizing the operation and energy costs and decreasing fouling rates. In practice the automatic control system proposed regulates the set points of the key operational variables controlled in MBR systems (permeate flux, relaxation and backwash times, backwash flows and times, aeration flow rates, chemical cleaning frequency, waste sludge flow rate and recycle flow rates) and identifies its optimal value. This paper describes the concepts and the 3-level architecture of the knowledge-based DSS and details the knowledge-based control module. Preliminary results of the application of the control module to regulate the air flow rate of an MBR working with variable flux demonstrates the usefulness of this approach.