M. Klein Arfman

Bio: M. Klein Arfman is an academic researcher. The author has contributed to research in topics: Stochastic modelling & Water quality modelling. The author has an hindex of 1, co-authored 1 publications receiving 33 citations.

A bottom-up approach of stochastic demand allocation in water quality modelling

Abstract: An “all pipes” hydraulic model of a DMA-sized drinking water distribution system was onstructed with two types of demand allocations. One is constructed with the conventional op-down approach, i.e. a demand multiplier pattern from the booster station is llocated to all demand nodes with a correction factor to account for the average water emand on that node. The other is constructed with a bottom-up approach of demand allocation, i.e., each individual home is represented by one demand node with its own tochastic water demand pattern. The stochastic water demand patterns are constructed with an end-use model on per second basis and per individual home. The flow entering the test area was easured and a tracer test with sodium hloride was performed to measure travel imes. The two models were evaluated on the predicted sum of demands and travel times, compared with what was measured in the test area. The new bottom-up approach performs at least as well as the conventional top down approach with respect to total demand and travel times, without the need for any flow measurements or calibration measurements. The bottom-up approach leads to a stochastic method of hydraulic modelling and gives insight into the variability of travel times as an added feature beyond the conventional way of modelling.

Simulating Residential Water Demand with a Stochastic End-Use Model

Abstract: A water demand end-use model was developed to predict water demand patterns with a small time scale (1 s) and small spatial scale (residence level). The end-use model is based on statistical inform...

Smart meters for enhanced water supply network modelling and infrastructure planning

Abstract: To design water distribution network infrastructure, water utilities formulate daily demand profiles and peaking factors. However, traditional methods of developing such profiles and peaking factors, necessary to carry out water distribution network modelling, are often founded on a number of assumptions on how top-down bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters allows for a new novel methodology for using the continuous ‘big data’ generated by these meter fleets to create evidence-based water demand curves suitable for use in network models. To demonstrate the application of the developed method, high resolution water consumption data from households fitted with smart water meters were collected from the South East Queensland and Hervey Bay regions in Australia. Average day (AD), peak day (PD) and mean day maximum month (MDMM) demand curves, often used in water supply network modelling, were developed from the herein created methodology using both individual end-use level and hourly demand patterns from the smart meters. The resulting modelled water demand patterns for AD, PD and MDMM had morning and evening peaks occurring earlier and lower main peaks (AD: 12%; PD: 20%; MDMM: 33%) than the currently used demand profiles of the regions’ water utility. The paper concludes with a discussion on the implications of widespread smart water metering systems for enhanced water distribution infrastructure planning and management as well as the benefits to customers.

Stochastic water demand modelling for a better understanding of hydraulics in water distribution networks

Abstract: In the water distribution network water quality process take place influenced by de flow velocity and residence time of the water in the network. In order to understand how the water quality changes in the water distribution network, a good understanding of hydraulics is required. Specifically in the periphery of the network, where customers are connected, the hydraulics can change rapidly. During the night time the water is almost stagnant and the residence time increases. In the morning, when everybody gets up and flushes the toilet and takes a shower, high flow velocities can occur. During the remainder of the day flow velocities are low. The stochastic model SIMDEUM was developed to simulate water use in small time scales (1 s) and small spatial scales (per fixture). The model SIMDEUM and its applications in several hydraulic and water quality models in water distribution networks were tested against measurements within these networks. SIMDEUM enables a good model of flow velocities, residence times and the connected water quality processes in the water distribution network.