Adaptive model based control for wastewater treatment plants

In biological wastewater treatment, nitrogen and phosphorous are removed by activated sludge. The process requires oxygen input via aeration of the activated sludge tank. Aeration is responsible for about 60% of the energy consumption of a treatment plant. Hence optimization of aeration can contribute considerably to the increase of energy-efficiency in wastewater treatment. To this end, we introduce an adaptive model based control strategy for aeration called adaptive WOMBAT. The strategy is an improvement of the original WOMBAT, which has been successfully implemented at wastewater treatment plant Westpoort in Amsterdam. In this paper we propose to improve the physics-based model by introducing automatic parameter adaptation. In an experimental model setup the adaptive model based control algorithm proves to result in better effluent quality with less energy consumption. Moreover, it is able to react to the varying circumstances of a real treatment plant and can, therefore, operate without human supervision

Two preconditioners for saddle point problems in fluid flows

In this paper two preconditioners for the saddle point problem are analysed: one based on the augmented Lagrangian approach and another involving artificial compressibility. Eigenvalue analysis shows that with these preconditioners small condition numbers can be achieved for the preconditioned saddle point matrix. The preconditioners are compared with commonly used preconditioners from literature for the Stokes and Oseen equation and an ocean flow problem. The numerical results confirm the analysis: the preconditioners are a good alternative to existing ones in fluid flow problems.

Solving large linear systems in an implicit thermohaline ocean model

The climate on earth is largely determined by the global ocean circulation. Hence it is important to predict how the flow will react to perturbation by for example melting icecaps. To answer questions about the stability of the global ocean flow, a computer model has been developed that is able to compute the flow. The ocean is influenced by the wind and by differences in salinity and temperature. Initially the use of the model was limited, because it required a large amount of computer time and of memory.This research focusses on speeding-up the ocean model. The solution of large systems of equations is by far the most time consuming part. In general there are two ways to solve such systems: via direct methods, that compute the solution in one step as accurate as possible, and via iterative methods, that makes a row of better and better approximations to the real solution and that use much less computer memory. We tried to combine both methods, but it appeared to be very hard to do that succesfully. It is better to use an iterative approach that respects and exploits the structure of the equations and the differences between variables. We designed a new algorithm for the ocean model that solves the systems based on this principle, which makes it possible to do faster and more accurate calculations

A tailored solver for bifurcation analysis of ocean-climate models

In this paper, we present a new linear system solver for use in a fully-implicit ocean model. The new solver allows to perform bifurcation analysis of relatively high-resolution primitive-equation ocean-climate models. It is based on a block-ILU approach and takes special advantage of the mathematical structure of the governing equations. In implicit models Jacobian matrices have to be constructed. Analytical construction is hard for complicated but more realistic representations of mixing. This is overcome by evaluating the Jacobian in part numerically. The performance of the new implicit ocean model is demonstrated using (i) a high-resolution model of the wind-forced double-gyre flow problem in a (relatively small) midlatitude spherical basin, and (ii) a medium-resolution model of thermohaline and wind-driven flows in an Atlantic size single-hemispheric basin.