Search

The current paper presents a geometrically inspired approach for the optimization and control of low pressure filtration units for (waste)water treatment. The optimization aims at minimizing the operational costs, encompassing both energy and chemical cleaning costs (and, hence, accounting for reversible as well as irreversible fouling), while ensuring a minimum required nett water flux. Advantage has been taken of the process’ cyclic nature. In each cycle the transmembrane pressure increases during the forward flow phase due to fouling and decreases again due to backflushing. Assuming linear increases/decreases allows the use of simple geometric approaches for computing analytical cost and constraint expressions. Relating both slopes to operational parameters as forward flux and filtration time, provides the handles for the actual control and optimization. However, due to the discrete nature of the number of cycles in one operation run, a relaxation strategy is employed for solving the optimization problem. Simulation results are presented for a preliminary filtration model inferred from a microfiltration plant for secondary effluent upgrading in the context of a research project with the Keppel Seghers company.

Given the vital role of nitrogen for plant growth on the one hand, versus the severe impact on the environment of extensive use of inorganic nitrogen fertilizers on the other hand, the need for alternative fertilization becomes apparent. In this respect, the nitrogen fixing bacteria of the genus Azospirillumoffer interesting perspectives since inoculation of the plant roots with these bacteria positively affects plant growth. Studies have shown that the growth promoting power of the bacteria can probably be attributed more to the production of the auxin indole-3-acetic acid (IAA) than to the nitrogen fixation qualities of the genus. The aim of this study is therefore to characterize and quantify the IAA production of Azospirillum brasilenseSp245 on a macroscopic level as to exploit the resulting model in optimal experiment design studies. The data obtained from these optimal experiments should be rich enough to aid in further unravelling the studied IAA production mechanism.

One-dimensional models that can adequately describe the flow and settling process in secondary settling tanks (SSTs) are an important tool with respect to control and optimisation of activated sludge wastewater treatment systems. In this study, the focus is on the one-dimensional convection-diffusion model proposed by Hamilton et al. (1992). Like most one-dimensional sedimentation models, it is based on a mass balance equation, which is discretized by dividing the settler into a number of horizontal layers with equal height. The advantage that convection-diffusion models have over most other one-dimensional models is that they generate mesh independent concentration profiles. Nevertheless, a minimal number of layers nmin is required to ensure convergence of the concentration profiles with respect to the number of layers. In this paper, the required number of layers is determined for a wide range of loading and operational conditions, and the sensitivity of nminwith respect to the model parameters is assessed. The required number of layers is shown to depend strongly on the operational status of the settling tank and on the value of the diffusion coefficient

In the search for a reliable but simple model for the biodegradation processes of an activated sludge wastewater treatment plant, this paper presents a linear multi-model which is valid for the global operating region of a standard carbon and nitrogen removing facility. The overall procedure, consisting of three steps (i.e., data generation, local model linearization and model interpolation) has been introduced in previous work (see, e.g.,(Smets et al., 2003b)) for the most basic configuration of one anoxic and one aerated tank followed by a point settler. Refinements to the methodology are however needed (and presented here) once the influent flow rate range is increased and the benchmark configuration, proposed by the COST 682 working group no. 2, is taken as the simulation protocol. The main advantage of the obtained linear model (structure) remains the alliance of high predictive power with low complexity, rendering the model fit for on-line optimization and control schemes.

In this paper a strategy is proposed to reduce the Activated Sludge Model no. 1 which describes the biotransformation processes in a common activated sludge process with N-removal. The procedure consists of three steps. In the first step representative input/output data sets are generated by simulating the full ASMl model. In the second step the ASMl model is rewritten in state space format with linear approximations of the nonlinear (kinetic) terms. In the third step the unknown parameters in the linear terms are identified based on the generated input/output data. To reduce the amount of parameter sets that have to be identified (to cover all the operation ranges of the plant), a model interpolation procedure is proposed. The main advantage of the obtained linear model (structure) is that it combines high predictive value with very low computation time

This paper deals with the design of a feedback controller for fed-batch microbial growth processes which forces the substrate concentration Csto a desired setpoint cs*, starting from an arbitrary (initial) substrate concentration and when non-monotonic growth kinetics apply. This problem is representative for a lot of industrial fermentation processes, with the baker’s yeast fermentation as a well known example. It is assumed that the specific growth rate μis function of the substrate concentration only. A first approach exploits the availability of on-line measurements of both the substrate and biomass concentrations. A second approach is based on on-line measurements of the biomass concentration only.