Hybrid neural modelling of an anaerobic digester with respect to biological constraints.

A hybrid model for an anaerobic digestion process is proposed. The fermentation is assumed to be performed in two steps, acidogenesis and methanogenesis, by two bacterial populations. The model is based on mass balance equations, and the bacterial growth rates are represented by neural networks. In order to guarantee the biological meaning of the hybrid model (positivity of the concentrations, boundedness, saturation or inhibition of the growth rates) outside the training data set, a method that imposes constraints in the neural network is proposed. The method is applied to experimental data from a fixed bed reactor.

Hybrid modelling of anaerobic wastewater treatment processes.

This paper presents a hybrid approach for the modelling of an anaerobic digestion process. The hybrid model combines a feed-forward network, describing the bacterial kinetics, and the a priori knowledge based on the mass balances of the process components. We have considered an architecture which incorporates the neural network as a static model of unmeasured process parameters (kinetic growth rate) and an integrator for the dynamic representation of the process using a set of dynamic differential equations. The paper contains a description of the neural network component training procedure. The performance of this approach is illustrated with experimental data.

The effect of epilepsy on road traffic accidents and casualties.

A prospective study was carried out between October 1992 and June 1994 to investigate the effect of epilepsy as a risk factor in road traffic accidents (RTA) and casualties in the United Arab Emirates (UAE). The aim of this paper was to investigate the risk and effect of epilepsy on road traffic accidents and casualties and identify possible risk factors for traffic accidents and casualties and identify possible risk factors for traffic accidents and violations among these drivers. Most (70%) were young drivers and under age 40 years; 43% were UAE nationals; 41% had a primary school education; 48.8% had a full license to drive private or commercial taxis; 41% had less than two years driving experience and 17.1% used seat bolts regularly. Most (65.9%) admitted to crossing red traffic lights; 46.3% to parking in forbidden areas; 66% to speeding; 36% to smoking while driving; 34.1% to using the telephone whilst driving; 43.9% to putting their child in the front seat; 20.8% had been previously involved in an RTA; and 53.7% had sustained serious injury. A figure of 34.1% had at least one seizure per year and 26.8% had at least one seizure per month. The most common violations were careless driving (34.1%) and traffic regulation violations (24.4%). Significantly higher risk was observed for property damage (RR = 1.85; 95% CI = 0.64-5.14) and traffic violations (RR-1.91; 95% CI = 0.54-2.29). In the UAE there are no restrictions on the issue of driving licenses to people prone to epileptic seizures. In conclusion, it is emphasized that patients with conditions such as epilepsy should feel obliged to inform the traffic authorities or the health authorities about their condition. It seems likely that the problem could be greatly reduced if appropriate action was taken concerning epileptic drivers. It is hoped that the results and recommendations of this study will be useful to traffic and health authorities.