No abstract

The implementation of robust hydraulic control in water supply networks relies upon the utilisation of redundant flow estimation methods. In this paper, we propose a novel model-based flow estimation method for diaphragm-actuated globe valves based on three pressure signals, namely the valve inlet pressure, valve outlet pressure and control chamber pressure (the 3P flow estimation method). The proposed flow estimation method relies upon the accurate determination of a valve stem position based on a force-balance analysis for the diaphragm of a valve, the measured pressure differential across a valve and the flow coefficients of a valve (, ). A novel stem position estimation model for diaphragm-actuated globe valves has been formulated and experimentally validated. The non-linear parameterised valve stem position estimation model results in multiple solutions. We combine advances in signal processing with support vector machine classification to find a correct solution. We compare the proposed flow estimation method with a method that uses stem position sensor measurements of a valve and two pressure signals. A unique set of experimental data have been acquired for performance validation. We derive uncertainty bounds for the proposed flow estimation method and demonstrate its application for robust pressure control in water supply networks.

Frequent saltwater intrusions in the Chao Phraya River have had an impact on water supply to the residents of Bangkok and nearby areas. Although relocation of the raw water station is a long-term solution, it requires a large amount of time and investment. At present, knowing in advance when an intrusion occurs will support the waterworks authority in their operations. Here, we propose a method to forecast the salinity at the raw water pumping station from 24 h up to 120 h in advance. Each of the predictor variables has a physical impact on salinity. We explore a number of model candidates based on two common fitting methods: multiple linear regression and the artificial neural network. During model development, we found that the model behaved differently when the water level was high than when the water level was low (water level is measured at a point 164km upstream of the raw water pumping station); therefore, we propose a novel multilevel model approach that combines different sub-models, each of which is suitable for a particular water level. The models have been trained and selected through cross-validation, and tested on real data. According to the test results, the salinity can be forecasted with an RMSE of 0.054g/lat a forecast period of 24 h and up to 0.107g/lat a forecast period of 120 h.

The energy balance calculation for pressurized water networks is an important step in assessing the energy efficiency of water distribution systems. However, the calculation generally requires mathematical modelling of the water networks to estimate three important energy components: outgoing energy through water loss (), friction energy loss () and energy associated with water loss (). Based on a theoretical energy balance analysis of simplified pipe networks, a simple method is proposed to estimate , and with minimum data requirements: input energy, water loss (WL) and head loss between the source and the minimum energy point (ΔH). By inclusion of the head loss in water networks into the estimation, the percentages of and are lower and higher, respectively, than using only the percentage of WL. The percentage of can be a function of the percentage of ΔH. By demonstrating our analysis with the simulation results from the mathematical models of 20 real water networks, the proposed method can be used to effectively estimate , and as a top-down energy balance approach.

Phuket is a tropical island in Thailand that is famous for tourism. The COVID-19 pandemic resulted in the number of tourists reducing to almost zero. Since tourism contributes around one-half of the gross provincial product of Phuket, the impact was so severe that even the numbers of people employed and registered as locals decreased. Analysing the data from January 2015 to March 2021, we found that the total, residential and non-residential monthly consumptions dropped significantly after Thailand's State of Emergency was declared in March 2020. Unlike other studies that reported residential consumption increasing when people are required to stay home for a prolonged period, Phuket's residential consumption decreased by more than 10% from the pre-COVID-19 level, possibly due to the drop in peer-to-peer accommodation bookings. To study the impact on consumption in detail, we modelled using cascade regression analysis by dividing the predictors into three groups, namely socioeconomics, weather and calendar period. The results showed that the number of guest arrivals was the most statistically significant in all types of consumption and should be used as a predictor for water demand forecasting models in tourism areas.