Conscious worst case definition for risk assessment, part II A methodological case study for pesticide risk assessment
This paper illustrates, by a case study, how to apply the conceptual Worst-Case Definition (WCD) model, developed in the methodological paper in the current journal, by Sørensen et al. (2010-this issue). The case is about eco-toxicological risk assessment of pesticides under Danish conditions. Cumulative aspects are included on a conceptual basis as elements of the worst-case conditions. This defines factors that govern the risk assessment, including location in time and space of risk “hotspots”. Two pillars of concern drive the conceptual modelling: (1) What to protect (denoted Protected Units (PUs)) and (2) the reason for increased risk level (denoted Causes of Risks (CRs)). Both PUs and CRs are analysed using hierarchical procedures that facilitate a complete listing of concrete factors governing increased risk for adverse effect due to agricultural usage of pesticide. The factors governing pesticide risk are combined in a context that combines the protection of relevant groupings of organisms with the factors for increased risk level for each of these. Identification of the most important relations between defined types of PUs and CRs is illustrated using expert knowledge. Existing databases are used to form spatial distributed risk indicators as estimators for a selection of important relations between PUs and CRs. This paper illustrates how the WCD model can break down the complex issue of uncertainty into fractions that are more open for evaluations. Finally, it shows application of risk indicators in a multi-criterion analysis using respectively self organizing mapping and partial order technique in a comparative analysis that highlights critical aspects of uncertainty, due to the ambiguity between single risk indicator rankings.
Sorensen, P.B., Giralt F., Rallo R., Espinosa G., Munier B., Gyldenkaerne S., Thomsen M. (2010) Conscious worst case definition for risk assessment, part II A methodological case study for pesticide risk assessment. Science of the Total Environment,408:3860-3870
A neural network-based modeling approach with back-propagation and support vector regression algorithms was investigated as a mean of developing data-driven models for forecasting reverse osmosis (RO) plant performance and for potential use for operational diagnostics. The concept of plant “short-term memory” time-interval was introduced to capture the time-variability of plant performance since both a state of the plant model and standard time-series analyses for both flux decline and salt passage did not result in realistic predictive horizons for practical purposes. Past information of normalized permeate flux and salt passage were introduced as unique input variables along with process operating parameters to capture short-term plant performance variability. Sequential models, where the time-variation within each forecasting time-interval was also taken as input information, and marching forecasting models, where target values were predicted at fixed future times from past plant information, were developed. Models were trained, with normalized permeate flux and salt passage, for various model architectures, memory time-intervals and forecasting times using both back-propagation and support vector regression approaches. State of the plant models (without forecasting) were able to describe the relatively small permeate flux variations but were unable to capture salt passage trends (for any present time condition) since unsteady state phenomena could not be properly described without plant memory information. Forecasting of plant performance, with both sequential and marching models, yielded good predictive accuracy for short-term memory time-intervals in the range of 8-24 h for permeate flux and salt passage for forecasting times up to 24 h. Current work is ongoing to extend the approach for longer time scales and to incorporate data-driven forecasting models of RO plant into control strategies and process diagnostics.
Libotean D., Giralt J., Giralt, F. Rallo R., Wolfe, T., Cohen Y. (2008). Neural Network Approach for Modeling the Performance of Reverse Osmosis Membrane Desalting. Journal of Membrane Science, (326)2,408:419
An implementation of a Fuzzy Artmap neural network is used to detect and to identify (recognise) structures (patterns) embedded in the velocity field of a turbulent wake behind a circular cylinder. The net is trained to recognise both clockwise and anticlockwise eddies present in the u and v velocity fields at 420 diameters downstream of the cylinder that generates the wake, using a pre-processed part of the recorded velocity data. The phase relationship that exists between the angles of the velocity vectors of an eddy pattern is used to reduce the number of classes contained in the data, before the start of the training procedure. The net was made stricter by increasing the vigilance parameter within the interval [0.90, 0.95] and a set of net-weights were obtained for each value. Full data files were scanned with the net classifying patterns according to their phase characteristics. The net classifies about 27% of the recorded signals as eddy motions, with the strictest vigilance parameter and without the need to impose external initial templates. Spanwise distances (homogeneous direction of the flow) within the centres of the eddies identified suggest that they form pairs of counter-rotating vortices (double rollers). The number of patterns selected with Fuzzy Artmap is lower than that reported for template matching because the net classifies eddies according to the recirculating pattern present at the core or central region, while template matching extends the region over which correlation between data and template is performed. In both cases, the topology of educed patterns is in agreement.
Ferre-Giné J, Rallo R, Arenas A, Giralt F (1996) Identification of coherent structures in turbulent shear flows with a fuzzy ARTMAP neural network. International Journal of neural Systems, 7, 559-568.
- DOI: 10.1142/S0129065796000555