Gerrit van Straten

Kinetic models for detection of toxicity in a microbial fuel cell based biosensor

Currently available models describing microbial fuel cell (MFC) polarization curves, do not describe the effect of the presence of toxic components. A bioelectrochemical model combined with enzyme inhibition kinetics, that describes the polarization curve of an MFC-based biosensor, was modified to describe four types of toxicity. To get a stable and sensitive sensor, the overpotential has to be controlled. Simulations with the four modified models were performed to predict the overpotential that gives the most sensitive sensor. These simulations were based on data and parameter values from experimental results under non-toxic conditions. Given the parameter values from experimental results, controlling the overpotential at 250 mV leads to a sensor that is most sensitive to components that influence the whole bacterial metabolism or that influence the substrate affinity constant (Km). Controlling the overpotential at 105 mV is the most sensitive setting for components influencing the ratio of biochemical over electrochemical reaction rate constants (K1), while an overpotential of 76 mV gives the most sensitive setting for components that influence the ratio of the forward over backward biochemical rate constants (K2). The sensitivity of the biosensor was also analyzed for robustness against changes in the model parameters other than toxicity. As an example, the tradeoff between sensitivity and robustness for the model describing changes on K1 (IK1) is presented. The biosensor is sensitive for toxic components and robust for changes in model parameter K2 when overpotential is controlled between 118 and 140 mV under the simulated conditions.

A recursively identified model for short-term predictions of NH4/NO3 – concentrations in alternating activated sludge processes

Abstract One of the stumbling blocks in the operation of alternatingly aerated activated sludge processes (ASPs) for nitrogen removal is the limited knowledge of both the varying influent composition and the complex dynamics of the biological process. This paper presents a simple physical N-removal model for alternatingly aerated, continuously mixed ASPs. The simplicity is achieved by capturing the slower process dynamics in recursively estimated time-varying model parameters. Both seasonal and diurnal parameter variations are tracked. Also the influent ammonium concentration is treated as a recursively estimated model parameter. The method performs excellently on real data collected from an alternatingly aerated pilot scale ASP fed with municipal wastewater. Simulation of the resulting time-varying model yields accurate and computationally cheap predictions of ammonium and nitrate concentrations in the specific plant under operation over the next hours. Simulation for different control input scenarios can be used to optimize process performance, either manually by operators or automatically by model based optimizing controllers. Another possible application is optimization of the sludge (biomass) concentration, as the estimated parameters contain information regarding process load and concentrations and activities of the N-removing biomass. From this information it can be computed whether there is an excess/shortage of sludge in the reactor.

Anomalies in moisture transport during broccoli drying monitored by MRI

Magnetic resonance imaging (MRI) offers unique opportunities to monitor moisture transport during drying or heating of food, which can render unexpected insights. Here, we report about MRI observations made during the drying of broccoli stalks indicating anomalous drying behaviour. In fresh broccoli samples the moisture content in the core of the sample increases during drying, which conflicts with Fickian diffusion. We have put the hypothesis that this increase of moisture is due to the stress diffusion induced by the elastic impermeable skin. Pre-treatments that change skin and bulk elastic properties of broccoli show that our hypothesis of stress-diffusion is plausible.

Modeling and analysis of the dynamic behavior of the XlnR regulon in Aspergillus niger.

BackgroundIn this paper the dynamics of the transcription-translation system for XlnR regulon in Aspergillus niger is modeled. The model is based on Hill regulation functions and uses ordinary differential equations. The network response to a trigger of D-xylose is considered and stability analysis is performed. The activating, repressive feedback, and the combined effect of the two feedbacks on the network behavior are analyzed.ResultsSimulation and systems analysis showed significant influence of activating and repressing feedback on metabolite expression profiles. The dynamics of the D-xylose input function has an important effect on the profiles of the individual metabolite concentrations. Variation of the time delay in the feedback loop has no significant effect on the pattern of the response. The stability and existence of oscillatory behavior depends on which proteins are involved in the feedback loop.ConclusionsThe dynamics in the regulation properties of the network are dictated mainly by the transcription and translation degradation rate parameters, and by the D-xylose consumption profile. This holds true with and without feedback in the network. Feedback was found to significantly influence the expression dynamics of genes and proteins. Feedback increases the metabolite abundance, changes the steady state values, alters the time trajectories and affects the response oscillatory behavior and stability conditions. The modeling approach provides insight into network behavioral dynamics particularly for small-sized networks. The analysis of the network dynamics has provided useful information for experimental design for future in vitro experimental work.

An autonomous weeding robot for organic farming

The objective of this research is the replacement of hand weeding in organic farming by a device working autonomously at field level. The autonomous weeding robot was designed using a structured design approach, giving a good overview of the total design. A vehicle was developed with a diesel engine, hydraulic transmission, four-wheel drive and four-wheel steering. The available power and the stability of the vehicle does not limit the freedom of research regarding solutions for intra-row weed detection and weeding actuators. To fulfill the function of navigation along the row a new machine vision algorithm was developed. A test in sugar beet in a greenhouse showed that the algorithm was able to find the crop row with an average error of less than 25 mm. The vehicle is a versatile design for an autonomous weeding robot in a research context. The result of the design has good potential for autonomous weeding in the near future.

Effect of toxic components on microbial fuel cell-polarization curves and estimation of the type of toxic inhibition.

Polarization curves are of paramount importance for the detection of toxic components in microbial fuel cell (MFC) based biosensors. In this study, polarization curves were made under non-toxic conditions and under toxic conditions after the addition of various concentrations of nickel, bentazon, sodiumdodecyl sulfate and potassium ferricyanide. The experimental polarization curves show that toxic components have an effect on the electrochemically active bacteria in the cell. (Extended) Butler Volmer Monod (BVM) models were used to describe the polarization curves of the MFC under nontoxic and toxic conditions. It was possible to properly fit the (extended) BVM models using linear regression techniques to the polarization curves and to distinguish between different types of kinetic inhibitions. For each of the toxic components, the value of the kinetic inhibition constant Ki was also estimated from the experimental data. The value of Ki indicates the sensitivity of the sensor for a specific component and thus can be used for the selection of the biosensor for a toxic component.

Online automatic tuning and control for fed-batch cultivation

Performance of controllers applied in biotechnological production is often below expectation. Online automatic tuning has the capability to improve control performance by adjusting control parameters. This work presents automatic tuning approaches for model reference specific growth rate control during fed-batch cultivation. The approaches are direct methods that use the error between observed specific growth rate and its set point; systematic perturbations of the cultivation are not necessary. Two automatic tuning methods proved to be efficient, in which the adaptation rate is based on a combination of the error, squared error and integral error. These methods are relatively simple and robust against disturbances, parameter uncertainties, and initialization errors. Application of the specific growth rate controller yields a stable system. The controller and automatic tuning methods are qualified by simulations and laboratory experiments with Bordetella pertussis.

Dealing with bio- and ecological complexity: Challenges and opportunities §

Abstract The complexities of the dynamic processes and their control associated with biological and ecological systems offer many challenges for the control engineer. Over the past decades the application of dynamic modelling and control has aided understanding of their complexities. At the same time using such complex systems as test-beds for new control methods has highlighted their limitations (e.g. in relation to system identification) and has thus acted as a catalyst for methodological advance. This paper continues the theme of exploring opportunities and achievements in applying modelling and control in the bio- and ecological domains.

Test of ACW-gradient optimisation algorithm in computation of an optimal control policy for achieving acceptable nitrate concentration of greenhouse lettuce

The adjustable control-variation weight (ACW)-gradient method proposed by Weinreb [Optimal Control with Multiple Bounded Inputs, Department of Electrical Engineering, Stanford University, Stanford, 1985, p. 148] is put to the test in finding optimal control laws for an optimisation problem with bounds on the inputs and terminal state constraints, presented by Ioslovich and Seginer [Acceptable nitrate concentraion of greenhouse lettuce: an optimal control policy for temperature, plant spacing and nitrate supply, in: Proceedings of the Agricontrol 2000, Wageningen, The Netherlands, IFAC, Wageningen University and Research Centre, Royal Dutch Institute of Engineers, 2000]. By making certain assumptions they derived properties of the solution in an analytic way. Here, it is shown that the numerical ACW-gradient algorithm is capable of finding solutions without making additional assumptions.

Field robot event, Wageningen, 5–6 June 2003

Abstract A brief report is given of the field robot contest held in Wageningen, 5–6 June 2003. The experience of the student competitors sheds light on a number of issues that need to be addressed before autonomous vehicles will become a reality.