Paolo Andreussi

River flood forecasting with a neural network model

A neural network model was developed to analyze and forecast the behavior of the river Tagliamento, in Italy, during heavy rain periods. The model makes use of distributed rainfall information coming from several rain gauges in the mountain district and predicts the water level of the river at the section closing the mountain district. The water level at the closing section in the hours preceding the event was used to characterize the behavior of the river system subject to the rainfall perturbation. Model predictions are very accurate (i.e., mean square error is less than 4%) when the model is used with a 1-hour time horizon. Increasing the time horizon, thus making the model suitable for flood forecasting, decreases the accuracy of the model. A limiting time horizon is found corresponding to the minimum time lag between the water level at the closing section and the rainfall, which is characteristic of each flooding event and depends on the rainfall and on the state of saturation of the basin. Performance of the model remains satisfactory up to 5 hours. A model of this type using just rainfall and water level information does not appear to be capable of predicting beyond this time limit.

Statistical characterization of slug flow in horizontal pipes

Abstract Air/water slug flow in 53 and 90 mm i.d. horizontal pipes has been investigated for a large range of gas and liquid velocities. For this purpose a special instrumentation with simultaneous data acquisition and analysis has been developed. This has been used to determine the mean slug characteristics (length, holdup and velocity) and their statistical distributions.

Artificial neural network approach to flood forecasting in the River Arno

Abstract The basin of the River Arno is a flood-prone area where flooding events have caused damage valued at more than 100 billion euro in the last 40 years. At present, the occurrence of an event similar to the 1966 flood of Firenze (Florence) would result in damage costing over 15.5 billion euro. Therefore, the use of flood forecasting and early warning systems is mandatory to reduce the economic losses and the risk for people. In this work, a flood forecasting model is presented that exploits the real-time information available for the basin (rainfall data, hydrometric data and information on dam operation) to predict the water-level evolution. The model is based on artificial neural networks, which were successfully used in previous works to predict floods in an unregulated basin and to predict water-level evolution in the Arno basin under low flow conditions. Accurate predictions are obtained using a two-year data set and a special treatment of input data; which allows a balance to be found between the spatial and temporal resolution of rainfall information and the model complexity. The prediction of water-level evolution remains accurate within a forecast time ahead of 6 h, which is the minimum time lag for the river to respond to dam releases under saturated conditions of the basin. The predicted flow rate percentage error ranges from 7 to 15% from the 1-h ahead to 6-h ahead predictions, and the accuracy of prediction increases for each time ahead of prediction, as the flow rate increases, suggesting that the model is particularly suited for flood forecasting purposes.

An impedance method for the measurement of liquid hold-up in two-phase flow

Abstract A liquid hold-up gauge based on the measurement of the electrical impedance has been developed for application in gas-liquid pipe flow. The gauge consists of two ring electrodes mounted flush to the pipe wall. The impedance (capacitance or conductance) seen by the electrodes depends on the distance between them and on the liquid hold-up. For distances above three tube diameters, the impedance is independent of the flow configuration for all separated flow patterns and, with good approximation, also for intermittent flows. Moreover, capacitance or conductance are linearly related to the liquid hold-up. The impedance under bubble flow conditions closely follows the theoretical predictions due to Maxwell. Also for the other flow configurations (annular, stratified, intermittent) the results of static and/or dynamic calibration agree closely with theoretical models.

The effect of water in the low-temperature catalytic oxidation of hydrogen sulfide to sulfur over activated carbon

Abstract This study investigates the use of low-temperature catalytic oxidation for the removal of H 2 S from tail gases originating from geothermal plants, with special focus on the effect of water on the overall performance of the activated carbon catalyst. It is shown that water strongly influences the reaction rate and the total amount of sulfur that can be adsorbed on the catalyst prior to regeneration. It is suggested that the reaction takes place in a thin water layer, inside the carbon pores, from the reaction of dissolved H 2 S with chemisorbed oxygen.

An investigation of void fraction in liquid slugs for horizontal and inclined gas-liquid pipe flow

Abstract The void fraction in liquid slugs has been determined for air—water fiow in horizontal and near-horizontal pipes by a newly-developed conductance probe technique. A semi-empirical correlation has been developed and compared with the present measurements and available data. This correlation predicts reasonably well the observed effects of diameter, inclination and physical properties.

A physically based correlation for drop size in annular flow

Abstract The derivation of a correlation for drop size in annular flow based on a mechanistic model is presented. Optimum values of four constants were obtained by a fit to measured data over a wide range of gas and liquid flow rates, physical properties and pipe diameter.

FORECASTING RIVER FLOW RATE DURING LOW-FLOW PERIODS USING NEURAL NETWORKS

The pollution in the river Arno downstream of the city of Florence is a severe environmental problem during low-flow periods when the river flow rate is insufficient to support the natural waste assimilation mechanisms which include degradation, transport, and mixing. Forecasting the river flow rate during these low-flow periods is crucial for water quality management. In this paper a neural network model is presented for forecasting river flow for up to 6 days. The model uses basin-averaged rainfall measurements, water level, and hydropower production data. It is necessary to use hydropower production data since during low-flow periods the water discharged into the river from reservoirs can be a major fraction of total flow rate. Model predictions were found to be accurate with root-mean-square error on the predicted river flow rate less then 8% over the entire time horizon of prediction. This model will be useful for managing the water quality in the river when employed with river quality models.

STRATIFIED GAS-LIQUID FLOW IN DOWNWARDLY INCLINED PIPES

Abstract Measurements of liquid hold-up and pressure drop are reported for stratified flow in a slightly inclined (0.65° and 2.1°), 5 cm pipeline. Velocity profiles in the gas phase have been determined for a limited number of flow conditions. Semi-empirical correlations are proposed for the transition to slug flow, the interfacial friction factor and the liquid hold-up.

VOID DISTRIBUTION IN SLUG FLOW

Abstract Air-water slug flow at atmospheric conditions in horizontal pipes of 31 and 53 mm i.d. has been characterized experimentally with local (optical) and cross-sectional (conductance) probes. The objective of the work is to improve the description of slug flow and the closure relations required in mean kinematic slug flow models. Local measurements include the radial void fraction distribution in the slugs, the size of the dispersed bubbles in the slug and the aeration of the liquid layer under the slug bubble. From cross-sectional holdup measurements, slug lengths and frequencies are determined, as well as the length of the highly aerated mixing zone in the front of the slugs. Measured values of film holdup at the tail of the slug bubbles and of slug frequency are compared with a physical model of slug flow derived from the work of Dukler & Hubbard.